An Impact of E-Commerce on Marketing and Operation in SME in Indonesia

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An Impact of E – Commerce on Marketing and Operation in SME in Indonesia

Abstract

This study is to distinguish the impacts and influence of E-commerce against Small Medium Enterprises (SMEs) in Indonesia. This research has given an overview of the progress in the adoption of E-commerce technology on operational and marketing level. It provides a substantial influence in improving the performance of SMEs, particularly the overall business performane in the Greater Jakarta Area. There are two important viewpoints that this study concentrates on, there are marketing and operations perspectives. It will investigate how these two aspects, both marketing and operations, have affected the performance of the SMEs including the moderating roles of the Firm Size and E-commerce experience. It will then be used to differentiate the impact of E-commerce on marketing and operating sectors and examine how their impact can affect the performance of SMEs in Indonesia. We will be using survey data from 120 correspondents of Indonesian SMEs that have adopted E-commerce technology. The methodology used to analyze the data is multiple linear regression analysis. We will also use moderated regression analysis to determine what the Firm Size and E-commerce experience roles are.

This research has provided an overview of the development in the use of E-commerce on operational level in which provides a significant influence in improving the performance of SMEs, particularly the performance of business in the Greater Jakarta. Whereby the result of hypothesis testing was derived by multiple linear regression method used. There is a positive impact by operational effect applicable to the SMEs from using e-commerce. One of the founding was that operations such as order processing, order fulfillment and supplier development, brought a positive impact on the use of E-commerce, improving the overall efficiency. The less developed impactful effect on the benefits can be gained from the use of SME E-commerce particularly in the short time ahead.

Based on the established hypothesis about marketing effect of E-commerce on SMEs. It is concluded that the usage of E-commerce has a positive marketing effect and significantly help increase the business performance of SMEs. Many companies prefer in increasing their investment marketing effort such as in online advertising, display products as well in brand recognition. Businesses sometimes must often double their marketing effort, as it is still not a common practice for Indonesian to shop online and particularly very difficult for SMEs due to limited resources available at their disposal. The people in Indonesia still have preferential over traditional transactions compared to transacting online.

Based on the results of this research it is proven that E-commerce brings positive advantages for SMEs, so it is advisable for SMEs that have no E-commerce presence to start implementing it. In addition, for SMEs that already have implemented E-commerce technology should be more invested in the operation and marketing aspects in order for the business performance to be optimal.

Keywords:

E-commerce, SME, Performance, Operations Effect, Marketing Effect, E-commerce Experience

Contents

I. INTRODUCTION

1.1 Background

1.2 Problem Formulation

1.3 Research Objectives

1.4 The Scope of Research

1.5 Structure of the Thesis

II. LITERATURE SURVEY

2.1 Structure of the Thesis

2.1.1 Definition of SMEs

2.1.2 Characteristics of SMEs

2.1.3 Development of SMEs in Indonesia

2.1.4 The success and failure of SMEs

2.2 E-Commerce

2.2.1 Definition of E-Commerce

2.2.1 Characteristics of E-commerce

2.2.2 Classification of E-commerce

2.2.3 E-commerce For SMEs

2.2.4 Drivers of Adoption of E-commerce in SMEs

2.2.5 Inhibiting Adoption of E-commerce

2.2.6 Benefits of E-commerce For SMEs

2.2.7 Disadvantages of Adoption of E-Commerce for SMEs

2.3 Impact Use of E-commerce on Aspects of Marketing and Operations

2.3.1 Impact of the Use of E-commerce Marketing Aspect

2.3.2 Advantages of E-commerce in Marketing Aspect

2.3.3 Impact of E-commerce on Operation Aspects

2.4 Business Performance

2.5 Firm Size

2.5.1 Definition of Firm Size Company Size

2.6 E-commerce Experience

2.7 Research Accomplished

III. Research Methodology

3.1 Research Design

3.2 Theoretical Framework

3.3 Scope of the Study

3.3.1 Analysis Unit

3.3.2 Research Object

3.3.3 Research Area

3.4 Research Hypothesis

3.4.1 Influence of operations and marketing aspects of the use of E-commerce and the performance of SMEs

3.4.2 Moderating effect on the impact of the use of E-commerce

3.5 Operationalization of Research Variables

3.5.1 Independent Variable (X)

3.5.3 Variable Moderator (Z)

3.5.4 Demographic Variables / Characteristics

3.6 Population and Sample

3.6.1 Population

3.6.2 Sample

3.6.3 Sample Processing Techniques

3.7 Data Collection Methods

3.7.1 Primary Data

3.7.2 Measurement of Variables

3.8 Questionnaire Design

3.8.1 Introduction

3.8.2 Screening Question

3.8.3 Business Profile

3.8.4 Operations Effect

3.8.5 Marketing Effect

3.8.6 Performance

3.8.7 Profile of Respondents

3.9 Analysis Technique and Hypotheses Testing

3.9.1 Preliminary Analysis

3.9.2 Frequency Distribution

3.9.3 Validity and Reliability Test

3.9.4 Classical Assumption Test

3.9.4.1 Multicollinearity Test (VIF)

3.9.5 Test Multiple Linear Regression

3.9.5.1 The coefficient of determination Test (R2)

3.9.5.2 Significant Simultaneous Test (F-Test)

3.9.5.3 Significant Partial Test (t-test)

3.9.6 Moderating Regression Analysis

3.9.6.1 Moderated Multiple Regression

IV. DISCUSSION AND ANALYSIS

4.1 Test Validity and Test the reliability Pre-test

4.1.1 Validity of Test Results Pretesting Questionnaire

4.2 Descriptive Analysis

4.2.3 Descriptive Analysis Performance Variables

4.3 Sample Profile

4.3.2 How long has the business been operated

4.3.3 How long has the business use of E-commerce

4.3.4 Turnover per year

4.3.5 Total Assets

4.3.6 Total number of Employee

4.3.7 Domicile Enterprises

4.3.8 Regional Marketing

4.4 Classical Assumption Test

4.4.1 Multicollinearity Test (VIF)

4.5 Hypothesis 1 and Hypothesis 2

4.5.1 Regression Analysis

4.5.1.1 Analysis of Marketing and Operations Effect of Against the Performance (Coefficient of Determination R2)

4.5.1.2 Simultaneous Analysis of Marketing and Operations Effect of Against the Performance (Variance Test Analysis)

4.5.1.3 Analysis of Marketing and Operations Effect of against the Performance partially (T-test)

4.6 Hypothesis Testing Hypothesis 3 & 4

4.6.1 Moderate Regression Analysis

4.6.1.1 The relationship of E-commerce Experience (Moderator) to the Operations and Marketing Effect against the Performance (hypothesis 3a and 3b Test)

4.6.1.2 Firm Size Moderator size in relation with Operations effect and Marketing Effect on Performance (Hypothesis 4a and 4b)

4.7 Discussion

V. CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusion

5.2 Suggestions

5.3 Limitations of Research

5.4 Further Research

Reference

Appendix

1.     Introduction

1.1             Background

Small Medium Enterprises (SME) in Indonesia has a crucial role in developing the national economy, especially on the economic growth and the reduction of unemployment rate. Furthermore, it helps the development of rural areas such as small provinces, villages as well as small islands. Based on world statistic, in order for developing economy to be a developed economy a minimum of 2% from the total population is needed. In 2015 the estimation of the total population in Indonesia are 250 million and the total SME are 57 million, therefore in 2015 Indonesia has 4.3% of SME. But from that 4.3% we can determine the successful rate are pretty small. In order for Indonesia economy to improve significantly, Indonesia has to focus on improving the SME success in the near future.

Nowadays businesses are not constrained in space and time, with the help of information technology, communication between seller and buyer has become efficient and practical. It also helps online transaction for buyer and seller. As we all know, internet development and usage are evolving rapidly in Indonesia. This phenomenon can be seen as the dramatic annual increment in internet usage as well as internet penetration. According to MarkPlus Insight as one of the largest research company in Southeast Asia, the total of Internet User in Indonesia in 2013 is 74.6 million. It shows an increase in 13 million since 2012 of 61.1 million users. The user who access more than three hours per day also shows a dramatic change from 24.2 million to 31.7 million. It shows positive relationship between internet user and netizen who access more than three hours per day.

E-commerce has a positive impact on various business components, several studies were looking at the role of E-commerce in the sub-functions of businesses, such as marketing and operation. Since E-commerce provide an innovative way to manage the available resources efficiently, it can also affect the various sub-functions of the business.

Based on this, the author was interested to analyze more about the effects after the use of E-commerce on the performance of SMEs in Indonesia. The purpose of this research is to distinguish the impact of E-commerce on marketing functions and operating functions and investigate how their impact can affect the performance of SMEs in Indonesia. This study will also be used to investigate the role of moderating variables such as E-commerce experience and SME’s firm size for determining the relationship-impact performance. Therefore, it would be interesting to do the research with the title: An Impact of E – Commerce on Marketing and Operation in SME in Indonesia”

1.2             Problem Formulation

The number of SMEs regardless of the type of industry or size, is still very limited know of any positive benefit on the use of E commerce. On the other hand, a lot more research to discuss the applications and factors in using e-commerce, but academic studies with a systematic explanation to provide information on positive impact on the use of technology E-commerce, especially in improving the aspect or function of a micro, small and medium enterprises (SMEs) on the performance is still very limited.

Based on the brief description above, the formulation of the problem in this research outlined in the following research questions:

  • Is there any effect of the use of E-commerce on operations and marketing aspects to the performance of SMEs in Indonesia?
  • How is the role of E-commerce experience and firm size in moderating the relationship between the use of E-commerce impact on operations and marketing aspects of the performance of SMEs in Indonesia?

1.3             Research Objectives

Objectives to be accomplished in this study are:

  1. To determine the use of E-commerce on marketing and operation aspects, how positive the impact is on the performance of SMEs in Indonesia.
  2. To determine the role of E-commerce experience and Firm size in moderating the relationship between the use of E-commerce in affecting on marketing and operation aspects of the performance of SMEs in Indonesia.

1.4             The Scope of Research

This study is focused on the impact of SMEs that use E-commerce in Indonesia, especially in the Greater Jakarta area (Jakarta – Bogor – Depok – Tangerang-Bekasi).

1.5             Structure of the Thesis

This study will be divided into five main chapters, namely:

Chapter 1: Introduction

Introductory chapter consists of background, the formulation of the research problem, research objectives, the benefits of research, the scope of research and systematic study.

Chapter 2: Basis Theory

In this chapter will discuss and elaborate on the results of the review of the research literature or secondary data from a series of theories that can be used to support this research.

Chapter 3: Research Methodology

This section provides a description of the methodology used in this study, which includes study design, definitions and operationalization of research variables, data collection methods, population and samples, sampling methods and data processing, research models, research hypothesis, data collection techniques, research instruments, and the timing of data collections.

Chapter 4: Data Processing

This chapter contains the processing and analysis of primary data that has been collected in order to obtain research results that will answer the research objectives.

Chapter 5: Conclusions and Recommendations

At the end, this chapter will contain the conclusions of the research, limitations of the study, suggestions and inputs related to this study to the relevant parties.

2. LITERATURE SURVEY

2.1 Structure of the Thesis

There is a definition that distinguishes between micro, small and medium enterprises (SMEs) to large enterprises. This definition is based on several things such as the amount of assets, sales turnover per year, and the number of workers from several sources. Beside that explanation of the characteristics that make SMEs differ from large enterprises, and matters that could affect the success or failure of SMEs.

2.1.1        Definition of SMEs

Small and medium enterprises (SMEs) by Carson (1995) generally characterized by the limited scale of their operations aspect. Ramanathan et al. (2012) revealed SMEs play an important role in the economic development in many countries around the world. The existence of SMEs in many countries are unique in the extend that each country has their own provision in defining SME. In general, the Organization for Economic Corporation and Development (OECD, 2005) explains that the SME is an enterprise with the number of employees less than 250 employees. Nevertheless, according to Pham et al (2011) SME located in Vietnam should have fewer than 300 employees, less than 500 employees in Germany and less than 100 employees to Belgium. Some definitions of SMEs granted by institutions and other countries are:

Table 1 Definition of SMEs from several organizations and countries

Country Definition of SME Size
World Bank Micro (Employees < 10 people, up to $100K Asset, Income up to $100K/year) Number of Employees, Assets and Income
Small (Employees < 30 people, up to $3 Million Asset, Income up to $3 Million/year)
Medium (Employees < 300 people, up to $15 M asset, Income up to $15 M/year)
Country Definition of SME Size
Singapore Businesses should be owned minimally of 30% local shareholders with fixed productive assets under SG $ 15 M Local share ownership and value of productive assets
Malaysia Small Industry (SI) Number of employees between 5-50, Total Share Capital $500 K Number of Employees and the Value of Assets
Medium Industry (MI) The number of employees between 50-75, Total Capital Stock $500 K – $2.5 M
Japan Mining and Manufacturing with less than 300 employees and a share capital of less than US$ 2.5 M Industry Type, Number of Employees and Capital
Wholesale with less than 100 employees and a share capital of less than US$ 840K
Retail with less than 54 employees and the share capital of less than US$ 820K
Service with less than 100 employees and a share capital of less than US $ 420K
South Korea Enterprises with the number of employees below 300 Number of Employees and Value Assets
Country Definition of SME Size
European Union Medium Sized Enterprise the number of employees less than 250 people, an income of less than $50 M and assets less than $ 50 M. The number of employees, value of assets and income
Small Sized Enterprise the number of employees less than 50 people, an income of less than $ 10 M.

and assets less than $ 13 M.

Micro Sized Enterprise the number of employees less than 10 people, an income of less than $ 2 M and assets of less than $ 2 M

In Indonesia SMEs are defined in the Law No. 2008 in the gallery of SMEs (2012) as:

  1. Micro

Micro enterprises are productive businesses owned by individuals and / or individual entities that meet the criteria of micro enterprises as stated in this law.

  1. Small Business

Small business group is an established economic enterprise, performed by an individual or business entity that is not a subsidiary or a branch of a company, owned, controlled, or be part either directly or indirectly from medium or large enterprises that meet the criteria of small businesses stated in the law.

  1. Medium Enterprises

Medium enterprises are stand-alone enterprise, which carried out by individuals or entities that are not subsidiaries or branches of company, owned, controlled or become part either directly or indirectly with a small business or large enterprise with total net assets or annual sales revenue as stated in the law.

Law No. 20 of 2008 also explains the characteristics of SMEs in terms of net worth (assets) and sales revenue (turnover).

Table 2 Criteria for SMEs under the Act. No. 20 of 2008

No Business Asset Turnover
1 Micro Max. 50 Million Max. 300 Million
2 Small > 50 Million – 500 Million > 300 Million-2.5 Billion
3 Medium > 500 Millions – 10 Billion > 2.5 Billion-50 Billion

The Central Bureau of Statistics classifying micro, small and medium enterprises based on the amount of the existing workforce.

Table 3 Criteria for SMEs by the Central Bureau of Statistics

No Description Criteria (Labor)
1 Micro 1 – 4 People
2 Small Businesses 5 – 19 People
3 Medium Businesses 20 – 99 People

2.1.2        Characteristics of SMEs

Research conducted by MacGregor (2006) describe the SMEs characteristics as follows:

  1. Managed by a small management team (Finance, Operations, Marketing, Human Resources)
  2. Owner has a strong influence
  3. Centralized power and control
  4. Lack of specialized staff
  5. Multifunctional management
  6. Lack of control over business environment
  7. Limited market share
  8. Low employee turnover
  9. Reluctant to take a risk

In addition to the above characteristics, MacGregor and Vrazalic (2004) also conducted a review and summary of some of the previous studies and have outlined some characteristic of SMEs. The summaries and reviews are:

  1. Management of SMEs are small and centralized with Short-term orientation
  2. Low management ability
  3. SMEs showed its independence and avoid speculation which would reduce its independence.
  4. The owners sometimes hide information from colleagues
  5. The process of decision-making based on intuition and rarely based of detail planning and in-depth study
  6. Business owners have great influence in decision making
  7. Disturbance of value and attention in the decision making process
  8. Planning and collaboration process is informal and inadequate
  9. The difficulty in obtaining capital and other resources due to limited resources available
  10. Lack investment in information and technology and limitations in using technology
  11. Have skills in technical and specialist employees which provide little training to IT Staff
  12. Limited variety of products and services that are owned
  13. The cramped market share sometimes rely on some consumers
  14. SMEs are more oriented to product while large businesses are more oriented to the consumer
  15. Less interested in having a larger market share
  16. Not being able to compete with larger competitors
  17. Lack of observing surrounding market condition, larger competitor, also the uncertainty
  18. SMEs face higher chance of failure compared to larger business
  19. Risk adverse

2.1.3        Development of SMEs in Indonesia

The number of SMEs in Indonesia based on data from the Ministry of the union of Small and Medium Enterprises (2011) suggests that the number of businesses in Indonesia is quite ample. With a total number of more than 49 million SMEs in 2006, or equivalent to 99.99% of the total businesses in Indonesia. While the number of large businesses in the same year approximately 5,000 business unit only or less than 1% of the total number of business units in Indonesia. Until 2010, the data shows that the growth of SMEs in Indonesia has increased by 9.8% from 2006, to 53.2 million units, large enterprises also increased but only by 5.69% from 2006 to 4,838 business units.

Table 4 Number of SMEs and large enterprises 2006-2010

Business 2006 2007 2008 2009 2010
Micro 48,512,438 49,608,953 50,847,771 52,176,795 53,207,500
Small 472,602 498,565 522,124 546,675 573,601
Medium 36,763 38,282 39,717 41,133 42,631
Big 4,577 4,463 4,650 4,677 4,838

Reference: Ministry of the union of Small and Medium Enterprises

2.1.4        The success and failure of SMEs

Many things can affect the success and failure of an SME. Rambat (2007) explains that there are four factors that drive the failure of a small business, namely:

  1. Managers who are less able to manage small businesses.
  2. Lack of support from the management. Founder or business owners got a new offer for new business ventures so that attention is divided on the business.
  3. Weak control/supervision, with weak supervision it can lead to profit loss and inefficient use of resources
  4. Lack of capital to run the business.

In addition to factors that encourage the failure of small businesses Rambat (2007) said that there are two factors that made small businesses successful:

  1. Small business owners are typically resilient, perseverance, work hard and have high goals and dedication.
  2. External factors that increase the demand for goods and services.

2.2 E-Commerce

Electronic commerce or e-commerce is similar with traditional commerce, but it has certain advantages that can directly help increase revenue and profits of a business. The flexibility of electronic commerce can help business to trim down the marketing cost. The information that needed to be delivered to the consumer such as product prices, new products or services, etc can be done more effective and efficiently without being limited to space and time. Businesses that do business electronically can also cut the cost of operations because they do not need to display their products in the store with many store clerks being employed. According Sholekan (2009; 14) traditional commerce is basically the act of businesses selling goods and/or services to generate income, their net profit are basically generated from the difference between the market price plus the income minus operational expenses.

Table 5 Comparison of traditional trade media and E-commerce (Electronic Commerce)

Type of Activity Offline Media Online Media
Sales Cycle Traditional business E-commerce
Acquire product information Magazine, newspaper, flyers Website, Search Engine
Request item Printed forms, letters Online catalogue, E-mail
Check catalogs, prices Catalogs Online Catalogs
Check product availability and confirm price Phone, fax E-mail
Generate order Printed form E-mail, websites
Prioritize order Manually Online database
Inventory checking Phone, fax Online database, websites
Schedule delivery Printed form Email, Online database
Create invoices Printed form Online Database
Receive product Shipper Shipper (unless electronic)
Confirm receipt Printed form E-mail

Table 5 Comparison of traditional trade media and E-commerce (Electronic Commerce)

Type of Activity Offline Media Online Media  
Send/receive invoice Mail E-mail, EDI
Schedule payment Printed form EDI, Online database
Send/receive payment Mail EDI

2.2.1 Definition of E-Commerce

There is some argument about the definition of Electronic Commerce or E-commerce. The opinion was expressed by several experts and business owners themselves. E-commerce is the process of buying and selling of products or electronic data network services through the Internet and World Wide Web (Grandon and Pearson (2004). Whereas another opinion expressed by Turban (2004) in the book of “Electronic Commerce” defined that E-commerce is a process of buying and selling or exchange of goods, services and information using media in a computer network.

McKay and Marshall (2004) in Ramanathan et al. (2012) define that E-commerce is the use of computer networks (including the Internet) to conduct businesses such as buying, selling, exchanging products, services and information. Other ideas on E-commerce are product sales through the web and is also known as “e-business” e-tailing “and” I-commerce “(PC Magazine, 2012).

One study suggests the development of E-commerce for SMEs conducted by Chen and McQueen (2008) describe and unify multiple models into a single model of development of E-commerce. The new model developed has four stages in the development of E-commerce, namely:

Table 6 Model E-Commerce development according to Chen and McQueen (2008)

Stages E-commerce activity
Messaging Searching information, Using online services and Using email to communicate with customers and suppliers

 

Table 6 Model E-Commerce development according to Chen and McQueen (2008)

Stages E-commerce activity
Online marketing Having a static website and online catalogue

Email ordering

Online ordering Using website for two way information

Cart order system implementation

Convenience payment method: bank transfer

Online transaction Automatic order system with confirmation and electronic invoicing

Credit card and EDI welcome

Front and back end integration

Overseas market expansion

E-business Integrating internal processes using ICT

2.2.1        Characteristics of E-commerce

Broadly speaking, the E-commerce itself has characteristics that noted by Indrajit (2001) in the book of “Tips & Strategies business in cyberspace” examples

  • The two sides transaction
  • An exchange of goods, services or information
  • Internet is the main medium in that trade process

In accordance with the above explanation it can be seen that the development of technology have an influence in the process of E-commerce.

2.2.2        Classification of E-commerce

There are several models in the Classification of E-commerce as proposed by Kotler and Armstrong (2008):

  1. Business to Consumer (B2C), to sell goods and services online to the end consumer.
  2. Business-to-Business (B2B) using the web, Email, online product catalogue, online trading networks, and other online resources to reach new business customers, serving the current customers effectively and achieve efficient purchasing and a competitive price.
  3. Consumer to Consumer (C2C), the exchange of goods and information online between the end consumers. In some cases, the Internet provides an excellent tool where consumers can purchase or redeem goods or information directly with one another. Example: taobao.com, ebay.com.
  4. Consumer-to-Business (C2B), an online exchange where the consumer is looking for sellers, learn about their offerings, and started purchasing, sometimes moving terms of the transaction. With the internet consumers can encourage business transactions and not vice versa as this was the case at priceline.com or ctrip.com where the consumer is able to search for airline tickets, hotel rooms, cruise and vacation packages.

2.2.3        E-commerce For SMEs

Ramanathan et al. (2012) have found that the smaller the size of a company the easier it is to fix the marketing function by using E-commerce as a tool and it can also help improve overall business performance compare to big enterprises. There are some typical cases where marketing function can be improved by investing in electronic technology are in online advertising, customer knowledge and brand recognition.

Karagozoglu and Lindell (2004) have found that E-commerce have a positive impact on the development of customer base (marketing function), whereas no significant impact on the purchasing department. On the other hand, according to Love and Irani (2004) E-commerce helps SMEs to understand the benefits in the operations function, which helps to improve overall performance.

The views expressed by Johnston et al. (2007) in Ramanathan et al (2012) have found that E-commerce helps boost revenue growth and reduce costs. Laudon and Traver at the Ramanathan et al., 2012 stated that the information technology in the E-commerce provides a new way of marketing because the time and space can be eliminated and it can also help to reduce maintenance and development costs of the goods.

By applying E-Commerce in SME will certainly get the benefits. Some of the benefits described by Dominic et al (2010) are:

  1. Up front benefit. With no or little cost you can open an online store as long there is internet access.
  2. Low cost of rent. The cost of starting an online store is much lower when compared to the cost of opening a brick and mortar store.
  3. It does not require many employees. Being able to cut the number of employees because it is not necessary to have shop keepers.
  4. No inventory needed as they can just act as a middleman.
  5. Broader market reach, with the online store items or products it can be sold to a wider range of markets.
  6. Save the cost of promotion. Promotion online is cheaper and faster, there are some ways that E-commerce can use as a promotion such as search engine optimization / Search Engine Optimizer (SEO), website promotion, free advertising, website promotion, free bookmarks and social networking sites. Of course, the costs incurred for some would be cheaper than creating a brochure, banner, banners and more.
  7. No down time to wait for the customers to come to the store, because transactions can be done online, including email, wiring or automated system owned by the store.

2.2.4        Drivers of Adoption of E-commerce in SMEs

There are some factors that trigger SMEs to adopt E-commerce. According to Reimenschneider & Mykytyn (2000) and Power & Sohal (2002) in MacGregor &Vrazalic (2004), there are at least fourteen things that will trigger SMEs to adopt E-Commerce. They are:

  1. Demand/consumers consumption,
  2. Competition
  3. Support of the supplier
  4. Saving costs
  5. Improvement in quality of customer service
  6. Short waiting time
  7. Increase sales
  8. Internal efficiency optimization
  9. Strengthen relationships with partners
  10. Consumer spreads/new markets
  11. Improving competitiveness
  12. Supporting SMEs technician
  13. Improve marketing
  14. Improve the system of monitoring and implementation

2.2.5        Inhibiting Adoption of E-commerce

Several studies have been conducted to determine the factors inhibiting the use of E-commerce. One study conducted by Lawrence & Tar (2010) shows some of the things that become an obstacle of E-commerce in developing countries are:

  • Infrastructural Barrier
  • Socio-Culture Barrier
  • Socio-Economic Barrier
  • Lack of Government Policy &Support Barriers

In addition to several inhibitors which have been described by Lawrence & Tar (2010) over the previous few studies, have also explained some of the things that concluded as an obstacle for early stage of SMEs that has adopted E-commerce.  In the study conducted MacGregor and Vrazalic (2004) there are seven things which are revealed by previous studies as the obstacles of SMEs as follows:

  1. E-commerce is less suitable to the products/services
  2. E-commerce isnot fit to the SMEs business of conduct
  3. Lack of appropriateness of theE-commerce to the consumers
  4. The lack of technicians who understand to operate E-commerce
  5. The security risk
  6. High cost
  7. less understanding of how to choose the best software and hardware for the business

2.2.6        Benefits of E-commerce For SMEs

Of course, by adopting E-commerce in the business of SMEs expect to get various benefits. In some previous studies Quayle (2002) in MacGregor &Vrazalic (2004) mentions several advantages when SMEs adopt E-commerce. Some of the benefits received by SMEs are:

  1. Administrative costs are lower
  2. The cost of production is lower
  3. Reduction of wait time
  4. Reduce excess inventory
  5. Increase sales
  6. Improving internal efficiency
  7. Strengthen relationships with business contacts
  8. New customers and markets
  9. Improving competitiveness
  10. Improve marketing
  11. Improving the quality of information

2.2.7        Disadvantages of Adoption of E-Commerce for SMEs

In addition to the benefits that are gained, some studies also revealed that there aresome disadvantages or negative effects of the adoption of E-Commerce by SMEs. In his study MacGregor &Vrazalic (2004) mentions some of the losses that may arise for SMEs are:

  1. complex relationship with vendors
  2. Cost may become higher
  3. Computer Maintenance Costs
  4. Additional tasks
  5. Lower the flexibility of working
  6. Security Reasons
  7. The emergence of dependence with E-commerce

2.3 Impact Use of E-commerce on Aspects of Marketing and Operations

2.3.1        Impact of the Use of E-commerce Marketing Aspect

The emergence of E-commerce change how to market product. It changes the conservative method to electronic, for example business owner only need to check the orders from electronic interactive shopping systems. One study that suggests the impact of the use of E commerce in the aspect of marketing is done by Gunasekran et al., (2002) are:

  1. Product promotion

E-commerce has increased the sale of products and services, through directly contacting customers.

  1. New sales channels

E-commerce can create new distribution channels for existing products with the direct support of customer knowledge and communication flexibility.

  1. Direct savings

Cost of delivering information to customers is cheaper. Electronic delivery of digital products (such as music and software) compared to costly shipping costs.

  1. Reduced cycle time

The delivery time for digital products and services can be reduced to seconds. In addition, the administrative work associated with physical delivery, especially in international borders, can be significantly reduced, reducing the time cycle of more than 90 percent.

  1. Customer service

Customer service can be improved by enabling customers to find detailed information online. (For example, FedEx allows customers to track the status of their packages) Also, customer service can answer questions in seconds using e-mail.

2.3.2        Advantages of E-commerce in Marketing Aspect

According to research Gunasekaran et al., (2002) E-commerce has increase marketing ability, such as improve research in obtaining information to evaluate alternatives information and help decision making (choice is selected from the alternative) as follows:

  1. Search for Products

When using the Internet to search for products, people may use a hyperlink, 3D navigation, search engine or other navigation technology that assist them in searching for the required products. This search can be done by using category selection in the search engine.

  1. Management of search criteria

Management Criteria occurs when a person finds information that encourages a person to change the search criteria. Criteria can be more specific because of the information obtained. For example, when the user wants to purchase something and in the middle of that they see another products that they actually need. These activities may take place before the product search, such as when a site used to collect information to supplement one’s knowledge of the product to be purchased.

  1. Comparison of products

Someone needs to compare multiple products prior to purchase, for example, to look for the cheapest product, or perhaps the best one. Most of the time it will require simultaneous and relative products assessment of the criteria.

2.3.3        Impact of E-commerce on Operation Aspects

In the study Gunasekaran et al., (2002) E-commerce has an impact on the sub-functions of the aspects of the operation of a business that can be described below:

  1. Product Design

Internet with a web-based data collection could help in improving the design quality and increase competitiveness in the global market. Design and collaborators located in different parts of the world can exchange information by using the Internet and World Wide Web (www). This can reduce design time and improve the accuracy of information on product design, and at the same time help to design a product that can grasp the targeted market.

  1. Production

In order for the company to be more competitive, there are two things that are needed such as speed and accurate information system. Communication and data collection constraints can be reduced by Web-based technology in goods and services. Using data-based management, data warehouse and data mining technology, the web can facilitate customer-supplier interactions, data collection, and data analysis process.

  1. Distribution

Internet can actually be used to distribute a wide range of information products, products like software and music that can also be digitized. Internet distribution can help in a significant amount of savings, company which use traditional transportation may use Internet-based tools to improve customer service. This allows customers to track the delivery of their orders without having to contact the sender directly.

  1. Warehousing

E-commerce can minimize the cost generated by inventory, insurance, warehousing and security. It is because E-commerce businesses does not need a huge inventory and warehouse to store their products. The auditing process for better stock management and accountability becomes possible. Reduction of manual processes will reduce the need for manual labor. E-commerce also helps to improve cash flow – a great improvement in matching invoices and receipts with real time payment status.

  1. Supplier Development

Many organizations are capable of doing their business globally is because of the E-commerce adaptation. By using E-commerce, companies can reduce or even eliminate the constraints related to time and distance. Companies can help buyers to find the best price and solutions to what they are looking for. On the other side E-commerce also can help to build strong relationship with customers, suppliers and business partners and with Internet-based internal networks to facilitate collaboration among employees, dissemination of information, and reduce communication costs.

2.4 Business Performance

Performance is referring to the level of achievement of a company within a certain time period. Performance is an important factor in the development of a company. The company’s goal is comprised of:

  • Remain standing or survive, to make a profit and grow, these can be achieved if the company has a good performance.

The concept of performance is very difficult to explain in the field of research, especially in the business setting which includes, both entrepreneurship and SMEs. Based on this, some of the arguments often raised. For example, Venkatraman and Ramanujam (1986) noted that the performance is difficult to construct a holistic operational service refers to the different aspects of the effectiveness of the organization. They noted that the Murphy et al. (1996) in the literature review their entrepreneurial studies agree with this when they presented the results of studies using different measures of performance that ranges from efficiency, profits, market share and leverage.

Many concepts put forward several people in connection with the concept of performance. For example, Murphy et al. (1996) called the entrepreneurial performance. Alam (2009) in a study of SMEs refers to it as the company’s performance. Several other studies also referred to as the organization’s performance (Jermias and Setiawan, 2008; Katou, 2008; Pritchard, Holling, Lammers and Clark, 2002). In another aspect, Jasra, Khan, Hunjra, Rehman and Azam (2011) prefers to associate performance to business and call it a business success or performance. Despite the diversity of the explanation of performance, all falls into business category. Therefore, the business performance framework become one measurement because for each individuals, companies and organizations that form a business activities making the performance of the business become more common. However, the performance of the company/organization are used primarily to associate small companies and entrepreneurial organization are on to large corporations.

However, based on the advice from Tangen (2005) and for clearer understanding of the arguments above, the performance of the business are being classified into two large groups, corporate/organizational performance and the performance of the owner/entrepreneur (see figure 2.1 below). In business research, both groups are the most frequently mentioned. From Figure 2.1, it shows that it can be used for measuring the performance of the company/organization or performance of the owner/entrepreneur in any research that involves measurement of performance.

Business Performance

Owner/Entrepreneur Performance

Firm/Organizational Performance

Organizational Performance (Large/Corporate)

Firm Performance (Entrepreneurial/SME)

Figure 1 Classification of Business Performance

Generally, Performance is a measurement or indicator for the evaluation or assessment of individual, group of companies, and organizations. It tells the strengths and weaknesses of what you want to measure. In business, it helps to measure the current business situation. Murphy et al. (1996) in the performance of entrepreneurial studies show that the performance of a business could reveal the following: efficiency, growth, income, size, liquidity, success or failure, market share and leverage. Alarape (2007) noted that the performance revealed operational efficiency and business growth. In another dimension, Jermias et al. (2008) noted that the performance provides information on the following: planning, investigating, coordinating, evaluating, staffing, and negotiation in which represent an individual, company or organization’s overall performance. These indicators are also linked to the performance of both individuals and organizations. Chew and Sharma (2005) provides performance indicators such as efficiency, internal liquidity, and strategic human resource as the effectiveness, profitability and leverage. Therefore, Ruzzier, Hisrich and Antoncic (2006) in their study of the proposed internationalization of SMEs both sales growth and profitability as a performance indicator.

Performance measurement is a complex and is a great challenge for researchers (Beal, 2000) because performance is a multidimensional nature and therefore performance measurement with a single measurement dimension is not able to provide a comprehensive understanding (Bhargava et al, 1994). So, performance measurement should use or integrate multiple measurement (Bhargava et al, 1994; Venkatraman & Ramunajam, 1986). Beal (2000) argue that there is no consensus on the most appropriate measure of performance in a research and the objective that measures performance that had been used in many studies still have many shortcomings. For example the size of ROI (Return On Investment) has a weakness, because there are various methods of measuring the value of inventories and depreciation of the fixed cost (Wright et al, 1995).

Furthermore Sapienza et al (1988) suggest that the size of the performance-based organizations have a shortage of accounting and finance that caused by variations in accounting method, also caused by the tendency of manipulation of figures from the management thus resulting in the invalid measurement. In anticipation of the unavailability of performance objective data in a study, it is possible to use a subjective measure, which is based on the perception of the manager (Beal, 2000). Zahra and Das (1993) proved that the subjective performance measures have high levels of reliability and validity. Besides, the research of Voss & Voss (2000) showed a correlation between subjective and objective performance measurements.

Based on the description above, the company’s performance is measured using a subjective measurement that is based on the perceptions of staff and managers of companies on the various dimensions of business performance measurement. Dimensional performance measurement commonly used in a variety of research is growth, profitability and efficiency (Murphy, et.al, 1996). Barkham, et.al (19 960 in Wicklund (1999) confirmed that sales growth is very good indicator of the performance and has become the best growth measurement of performance. Furthermore, Wicklund (1999) adds that growth, fuelled by rising demand of the products that offered by the company, which means higher sales growth indicates market share growth.

According to Bhargava, et.al (1994) assessing the company’s ability to achieve efficiencies of scale and market power is not the only measurement to measure the effectiveness of the market, growth in market share can also be used. Dimensions profitability intended to determine the company’s ability to generate earnings and to find out how far the company is managed effectively. Profitability indicators are used to adopt the study of Shrader, et.al (1989); Rue and Ibrahim (1998) the ROI (Return On Investment). ROI is calculated from the net profit after tax EAT (Earnings after Tax) divided by total assets.

2.5 Firm Size

  1.           

2.5.1        Definition of Firm Size Company Size

According to (Ferry and Jones, 1979 Panjaitan: 2004), the size of the company is a large scale where small companies can be classified according to a variety of ways, including: total assets, log size, the stock market value, the value of corporate assets, the value of turnover of the company, the large number of workers and others. Basically, according to EdySuwito and Arleen Herawaty (2005: 138) the size of the company is divided into three categories: “large company (large firm), medium (medium-size) and small companies (small firm). Determining the size of the company is based on the total assets of the company “. Meanwhile, according to the Central Bureau of Statistics, the size of a business that is based on the number of SMEs existing workforce, for micro businesses consist of 1 to 4 People, Small Business 5 up to 19 people and Medium Business 20 to 99 people.

According to the study Romero et al., 2010, large companies can take advantage economies of scale, but in case of diseconomies of scale can lead to efficiency when a company is too big. Based on research Love and Irani (2004), development of new skills of employees to information technology are indispensable in improving the competitiveness of companies. According to Wu et al., 2006, the size of a company can have a huge impact on company performance. Large company could obtain greater synergy effects of human and financial resources which leads to better performance. Therefore, the size of a business is measured by the number of employees. This allows us to identify the nature of the relationship between the ability of particular aspects of supply chain operations with the company’s performance more effectively. In other words, this study proves that the size of companies in relation to aspects of supply chain operations mainly very closely linked to the number of employees.

2.6 E-commerce Experience

Experience in the use of E-commerce is an important thing that can affect how companies take advantage of their ability to use E-commerce (Tornatsky and Fleischer, 1990, Wang and Ahmed, 2009; Wu et al., 2003). Research from Ramanathan et al., (2012) explains that in terms of E-commerce experience is not all organizations / businesses that use E-commerce technology learned from experience that the mere nature. That is, if an organization does not learn then some companies are still less sensitive in recognizing the duration of implementing E-commerce technology.

2.7 Research Accomplished

This study is a replication of the study R. Ramanathan et al. (2012) the impact of E-commerce on Taiwanese SMEs Marketing and operations effects. Where this study examined the relationship between the impact of the use of E-commerce on marketing and operation aspects of the performance of SMEs using data from a survey of 110 SMEs in Taiwan. Research using SPSS analysis to examine the direct and indirect effects of the impact of the use of E-commerce on the performance of SMEs. Firm Size and E-commerce experience is used as a moderator variable to moderate the relationship between the effects of marketing and operations with the performance of SMEs. Where the variables in the study is the effect of marketing variables, variable effect operations, variable firm size, variable E-commerce experience and the variable SME’s performance. Where the results of this study indicate that the effect of the use of E commerce in the marketing and operation aspects provide significant positive results on the performance of SMEs, firm size and E-commerce experience also plays a moderating role in this connection. From the findings regarding the effects of its moderation, a moderate effect first E-commerce experience (measured in terms of the number of years) in adopting e-commerce applications have not shown better performance as compared to a more low or variable moderate E-commerce experience did not yield significant results in effect, and marketing operations moderating effect on performance. Furthermore, the moderator variables Firm Size found that successfully moderate the operations effect on performance, otherwise Firm Size no significant effect in moderating the marketing effect on performance, meaning that a larger company (with a higher number of employees) can use the E-commerce better to improve aspects of their operations than smaller firms, while small firms achieve better marketing improvement with the use of E-commerce than large companies to help improve their overall performance. In this study, the authors tried to take some of the earlier study with discussion of the impact of the use of E-commerce in the Micro, Small and Medium Enterprises. It is intended to provide more information on the topics of research to be done.

Research Ramanathan et al. (2012) The impact of e-commerce on Taiwanese SMEs Marketing and operations also Inspired effects of a constructs impact of the use of Information Technology or E-commerce developed by Love and Irani (2004), An exploratory study of information technology evaluation and benefits management practices of SMEs in the construction industry. Where these studies have suggested that how e-commerce can help SMEs understand the benefits within the operations function, which helps to improve overall performance. This study examined the relationship between investment using information technology and the company’s performance in Australia. Where the moderate variable in these studies is firm size. The results of this study in which the model study Love and Irani indicated that the organization type is significantly different from the amount of turnover of investment in information technology and the level of investment in information technology (IT Investments) is not influenced by firm size moderate.

The second study entitled “Analysis of Effect of the benefits of Business E-commerce on the Performance Retail (case study of small and medium enterprises in the computer retail Jakarta) written by Samuel Utomo (2011) Extension Program students of the Faculty of Economics, University of Indonesia. This study was made in order to know the benefits of E-commerce at Retail on the performance of the technology and financial performance in Indonesia. In this study there were five dependent variables are the internal administrative efficiency, the expansion of the market, supply management, cost savings, and overall customer service in influencing the performance of the information technology and financial performance. The object of this research is a shop / retail computer in Jakarta. The data gathering technique in this study is in the form of quantitative research with the number of respondents as many as 70. The results of this study is the first variable internal administrative efficiency, market expansion, customer service has an influence on the performance of information technology, while variable inventory management and cost savings negatively affect the performance of information technology. The second discovery that the expansion of the positive influence of variables on the performance of financial markets, while the variables of internal administration, inventory management, cost savings, and customer service has a negative influence on financial performance.

Research Wu et al. (2006) tries to explain the level of E commerce impact on the performance of companies that may be affected by various uncertainties characteristic of companies such as the environment, employee training, and technological capabilities. Where the concept of this research is based on the views Resource Based View (RBV) that the purpose of this study is to explain the ability of Supply Chain technology that uses information on the company and what are the difficult things to apply technologically in the company. A company’s size and experience in using e-commerce are two important characteristics that can affect how companies take advantage of e-commerce. This study uses data from the middle to the top 264 companies in the United States, the study found that the role of the supply chain (supply chain capabilities) that as a mediator variable between investment in information technology with business performance. The results showed that the ability of the supply chain is able to change the related information technology resources to the highest value of the company. The mediating effect of supply chain capabilities of supply chain capabilities also explained that can help better the impact of the use of information technology on business performance. These results also provide some implications for managing the supply chain system. In particular, managers need to recognize the role of the supply chain’s ability to realize the value of information technology resources. Firm size as control variables in this study had no significant effect on the performance of the company.

The fourth study titled an exploratory comparison of electronic commerce adoption in large and small enterprises conducted by Grimshaw et al., (2002). This study compares the reasons why small companies and large companies have adopted electronic commerce (E-commerce) and compare the impact of the adoption of E commerce by both groups of companies. This study using questionnaire (quantitative) and was performed on small businesses and large enterprises that use e-commerce by in the UK. The author found that the use of E-commerce for dealing with competitors, improve customer service and improve relationships with suppliers, improving operational efficiency is to develop a larger business in adopting e-commerce. Therefore it would seem that small businesses did see e-commerce as an opportunity to improve performance. For large enterprises that E-commerce is seen as a more defensive approach, especially as an opportunity to simplify the complex internal in every process of cost reduction. The study also found that small businesses had achieved greater benefits in the use of E-commerce with large companies likely to adopt e-commerce to improve their internal operations.

Further research is still related to the impact on the use of E-commerce in businesses is also conducted by Barnes et al. (2004). In his research titled: Economy in the old economy- three case study example, has given a clear picture of where E-commerce can be applied differently in different management functions, such as in sales, logistics planning and timely delivery. This study investigates SMEs which adopt e-commerce technology as a practice around the UK in which generally have experienced the benefits in terms of growth in their sales. Using case studies obtained from three companies engaged in manufacturing, the study found an explanation on how some companies / producers are still in the era of the old economy able to use e-commerce to seek a competitive advantage in the new economy era. Throughout this research, the author found that e-commerce can help manufacturers in increasing cost-efficiency. He also found that the possibility of Internet-based technologies are widely available to all manufacturers and the benefits of cost-efficiency from E-commerce seems to be for the long run. Strategy based on reaching the perfection in quality of service can be an attractive alternative to many producers, instead of only focusing on price advantage.  Such a strategy may not be sustainable if the Internet-based information technology can be utilized to provide superior service. There is a possibility of increasing barriers to entry of potential competitors, and increase barriers to exit and transfer fees for customers. From here we can see that these studies have come to the conclusion the use of E-commerce can have a positive impact on performance.

Of the six (6) previous studies above, there are some similarities with research conducted by the author. The similarity in research topic of the impact or the benefits arising from the use of E-commerce in Small and Medium Enterprises. Another similarity is the use of a quantitative approach similar to the five (5) study by Ramanathan et al. (2012), Utomo (2011), Love and Irani (2004), Wu et al. (2006), and Grimshaw et al. (2002). However, there are five (5) differences with the previous studies one of them being as the research object. Also worth to mention that the previous studies were conducted in different countries with quantitative methods, to name the few were: the United Kingdom, Taiwan, Australia, and the United States.

III. Research Methodology

3.1 Research Design

The research design refers to the framework used in conducting the research, it contains series of necessary procedures to obtain the information needed to answer the research problems. According to Cooper & Schindler (2006) research design is the blueprint of data collection, data measurement, and data analysis that helps researchers to allocate the limited resources by placing important choices in the methodology. Plan is a scheme and comprehensive program from a research. Plan includes an outline of what will the investigator research on, start from writing several hypotheses, operational implications up to the final data analysis.

This research is a descriptive study with the purpose of obtaining an overview of the impact of the use of E-commerce of SMEs in Indonesia to the business performance: in the aspects of Marketing and Operations. The design of this study is confirmatory where the study was intended to evaluate and confirm the model that has been built in the previous research.

Through this study, researchers wanted to determine and analyze the impact of the use of E-commerce in the influence of marketing and operation aspects to the business performance. The method used in this research is a quantitative method. Quantitative methods used to examine the population or a particular sample, the sampling technique is generally done at random, data collection using research instruments, quantitative data analysis / statistics with the aim to test the hypothesis that has been set (Sugiyono, 2009, p13). Quantitative research will be carried out with single crosssection method (Malhotra, 2007).

Data will be collected through a questionnaire survey technique, the data will be processed with statistical methods using SPSS PASW Statistics 18 program in which the questions on the questionnaire for this study were drawn from the study. The collected data is then processed by using statistical methods Multiple Linear Regression and Multiple Regression Moderated.

Researchers conducted the pre-testing of the 30 respondents before the primary data collection. Pre-testing is a method to prevent and anticipate problems that might arise. It also aimed to see whether the respondents understand every question asked, and to minimalize the error in the question. (Malhotra, 2007).

Generally, this research starts through several stages: preliminary studies, problem identification, framework and research hypotheses, data collection and conclusion.

3.2 Theoretical Framework

Based on the literature review on the analysis of the impact of the use of E-commerce in the marketing and operation aspects to the performance of SMEs with firm size and E-commerce experience as a moderator variable. In the model or framework, it can be seen that there are some direct influence of the use of E-commerce that impacted on the business performance. Other than that, we can also see a moderate influence of firm size and E-commerce experience to the relationship. Furthermore, this research model or framework can be seen in figure 3.1 as follows:

E-commerce
Experience

H1

H3b

H3a

Operations

Performance

H4a

Marketing

 

H4b

H2

Firm Size

 

Figure 2 Impact Research Model Use of E-commerce the performance of SMEs

Source: Ramanathan et al., 2012

In the study Ramanathan et al., The use of E-commerce adopted by SMEs are influenced by marketing and operations aspects that were in the body of an enterprise and its relationship to performance.

3.3 Scope of the Study

3.3.1 Analysis Unit

The unit of analysis in this study are individuals in various types of SMEs industry such as leaders, owners, sales managers or business managers.

3.3.2 Research Object

The object of this research are various types of SMEs in Indonesia, especially in JABODETABEK (Jakarta, Bogor, Depok, Tangerang, Bekasi) area which ranging from agriculture, horticulture/Forestry/Fishing, Mining/Quarrying, Manufacturing/Garments/Creative Industries, Electricity/Gas/Water, Building/Construction /Infrastructure, Trade/Hotel/Leisure/Tourism/Restaurants, Transportation/Communications, Finance/Rental/Banking/Investment/Capital Markets and Other Services (Central Bureau statistics, 2004) which adopted the usage of E-commerce. The location of this research is done on Micro, Small and Medium Enterprises in the Greater Jakarta area (Jakarta-Bogor-Depok-Tangerang-Bekasi). The reasons for selecting these locations is because the area is experiencing significant growth in these various types of SMEs industry.

3.3.3 Research Area

The research was carried out in the territory of Indonesia and precisely in the Greater Jakarta area (Jakarta-Bogor-Depok-Tangerang-Bekasi).

3.4 Research Hypothesis

This study was conducted to re-prove whether the benefits of E-commerce on SMEs as a theory Ramanathan et al. (2012) could affect the performance of business in Indonesia. Based on the background of the problems and objectives of previous research, the research hypothesis is formulated as follows:

3.4.1 Influence of operations and marketing aspects of the use of E-commerce and the performance of SMEs

Many innovative ways used in implementing E-commerce Company, where innovative way are expected to improve overall business performance, this also applies to the operation and marketing in SMEs. E-commerce helps to improve the company’s operations management functions, usually combining both e-commerce (online channel) and bricks-and-mortar (traditional offline channels) to increase sales and profits (Schniederjans and Cao, 2002). Likewise, information technology in the E-commerce provides a new way of marketing because of time and distance can be eliminated. E-commerce in general can reduce the costs of development and maintenance of materials (Laudon and Traver, 2007). There are some advantageous of E-commerce in the operation aspect which is the application of information such as inventory tracking, internet secure ordering and tracking order (Heimand Field, 2007), while when viewed in terms of marketing it can reduce the costs of advertising and warehousing (Laudon and Traver, 2007; Kumar and Petersen, 2006; Santarelli and D’altri, 2003).

Karagozoglu and Lindell (2004) have found that E-commerce has a positive impact on the development of customer base (marketing function), whereas no significant impact on the purchasing management. Conversely, according to Love and Irani (2004) E-commerce helps SMEs understand the benefits of the operations function, it improve the overall performance. By using structural equation modeling, Hafeez e tal. (2006) have identified a positive relationship between supply chain strategy and business strategy, and this relationship varies between adopters and non-adopters of e-business in the UK. It should be noted again that since there are rapid development of technology, especially in e-commerce, it has resulted in continual evolution concept of supply and demand chain with a shift in power from supplier to customer.

  • Hypothesis 1: The use of E-commerce in the operation aspects has a positive influence towards the SMEs’ performance in Indonesia
  • Hypothesis 2: The use of E-commerce in the marketing aspects has a positive influence towards the SMEs’ performance in Indonesia

 

3.4.2 Moderating effect on the impact of the use of E-commerce

In several research studies said that the impact of the use of E-commerce in SMEs can vary depending on the characteristics of the company. For example, the impact of the E-commerce on business performance can be affected by various characteristics such as company environmental uncertainty, employee training, and technological capabilities (Wu et al., 2003). A firm size and E-commerce experience in using E- commerce are two important characteristics that can affect how companies take advantage of their ability to use E- commerce (Tornatsky and Fleischer, 1990, Wang and Ahmed, 2009; Wu et al., 2006). In general, as the company is more experienced with the E-commerce. The impact of the use of E-commerce on the performance of the company may differ.

Companies that have more experience with E-commerce may have the time and resources to specialize in one part of its function that is controlling the E-commerce technology and they may take the positive impact of using the E-commerce. The impact will affect the performance of SMEs and in this regard will be moderated by E-commerce experience.

  • Hypothesis 3a: The longer the experience of using E-commerce, the greater the positive impact provided by the operating aspects of the performance of SMEs in Indonesia
  • Hypothesis 3b: The longer the experience of using E-commerce, the greater the positive influence marketing aspects of the performance of SMEs in Indonesia

 

Fix cost can be significant when the company decided to adopt E-commerce. In this case it can be called sunk cost, which sunk costs may affect the company depending on its business scale. Larger businesses can enjoy the economies of scale towards better utilization of e-commerce to provide greater impact of E-commerce on the performance. According to Wang and Ahmed (2009), it has been identified that one of the factor in adopting E-commerce for SMEs in the UK is the Firm Size. According to the study, larger businesses benefit more when adopting the technology in compare to the small businesses. Wu etal., (2003) uses the firm size as a control variable for the use of E-commerce, because the larger organization (in terms of number of employees) might be able to utilize more accessible resources to improve business performance. Daniel and Grimshaw (2002) found that the larger the firm size can improve the efficiency of the operations based on the E-commerce more easily. According to the results of Wu etal., 2006, the size of the company can have a huge impact on the company performance. Therefore, the firm size is measured by the number of employees, this allows us to identify the relationship nature of the operation aspects especially the supply chain to the business performance is more effective. In other words, it has been proven that the Firm size in the relation of supply chain has a strong relationship with the number of employees.

  • Hypothesis 4a: The larger the size of a business based on the number of Employees, the greater the positive impact provided by the operating aspects of the performance of SMEs

 

  • Hypothesis 4b: The larger the size of a business based on the number of Employees, the greater the positive impact provided by the marketing aspects of the performance of SMEs

3.5 Operationalization of Research Variables

The research variables are determined by the theoretical foundation and confirmed by the research hypothesis. Variable research is an attribute of a group of people or objects that have variations between one and another in the group. The operational definition describes the particular way that can be used by researchers to operationalize the construct, thus allowing other researchers to replicate the measurement in the same way, or to try to develop ways to construct a better measurement.

In this study there are three main variables, namely the independent variable (predictor), the dependent variable (criterium) and moderator variables (moderating). The independent variables and the dependent variable was measured using a Likert scale using six scales answers to assess how far the perceived level of agreement with each statement from the respondents. As for the moderator variables it was measured using an ordinal scale. In addition, demographic variables are added in this study to measure the characteristics of respondents who entered into the sample. The description of operational variables can be illustrated as follows:

3.5.1 Independent Variable (X)

The independent variable is an independent variable or variables is not influenced by other variables. According Sugiyono (2009; 61) “independent variable is a variable that was the cause of the onset or changes in the dependent variable. In this study, the independent variable (X) are the influence of marketing and operation.

Free variable in this study are the variables that the cause of the onset of change in the dependent variable, that is the performance of SMEs. These variables influence the use of E-commerce in SMEs in Indonesia.

3.5.3 Variable Moderator (Z)

Moderator variables are variables that affect (strengthen or weaken) the relationship between independent variables and the dependent. This variable is also known as the second independent variable (Sugiyono, 2009). In this research there are two variables moderator such as E-commerce experience and firm size.

3.5.4 Demographic Variables / Characteristics

Demographic variables in this study were made to know the respondents more with obtaining more data such as, SMEs type, how old is your business, how long does it adopt E-commerce, business turnover, total assets, number of employees, marketing region.

For the early stages of the preparation of the questionnaire, the authors first draw up the operationalization of variables to get the questions right and could reflect the variables in the study and resolve the problem of this research. The structural questions that will be used will be easily understood by the respondents. The questionnaire will also have instructions and clear information to minimize errors when filling out the questionnaire.

The variables used in this questionnaire drawn from previous studies conducted Ramanathan et al (2012) and operationalized using a Likert scale, and can be seen in the table below:

Table 7Variable Operationalization

Variable Indicator Size Source
 

 

 

Performance

  1. Sales growth
  2. Customer base
  3. Customer satisfaction
  4. Process enhancement
  5. Competitive Advantage
 

 

 

Interval

 

 

 

 

Turley, 2001;

George, 2001

Table 7 Operationalization Variable (Continued)

Variable Indicator Size Source
 

 

 

Marketing effect

 

  1. Online advertising
  2. Customer awareness
  3. Brand recognition
  4. Exposure to product/ service
 

 

 

 

Interval

 

 

 

Clayton dan Waldron, 2003; Kumar dan Petersen, 2006

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Operations effect

  1. Value Improvement of service/product.
  2. Quality improvement of service/product.
  3. Internal communication.
  4. Change in customer ordering.
  5. 24/7 availability of placing orders.
  6. Simple web design.
  7. Comprehensiveness of the information.
  8. Security of online payment.
  9. Well-protected customers’ information.
  10. Reliable order confirmation.
  11. Delivery reliability.
  12. Order accuracy.
  13. After sales service or contact.
  14. Identify new suppliers.
  15. Online ordering and purchasing.
  16. Communication with suppliers.
 

 

 

 

 

 

 

 

 

 

 

 

Interval

 

 

 

 

 

 

 

 

 

 

Soliman dan Youssef, 2003; Cagliano et al, 2003;Clayton dan Waldron, 2003;

Kumar dan Petersen, 2006

Table 7 Operationalization Variable (Continued)

Variable Indicator Size Source
 

 

 

Characteristics Variable

  1. Type of SME
  2. Time of Using of E- commerce
  3. Business Turnover
  4. Total Asset
  5. Total Workforce
  6. Marketing Region
 

 

 

 

Ordinal

 

 

 

Ramanathan et al., 2012

E-commerce experience Time of using E-commerce  

Interval

Ramanathan et al.,   2012;  Wu

et al., 2006

 

Firm size

Firm size based on total employee  

Interval

Romero et al., 2010; Wu et

al., 2006

Source: Ramanathan et al. (2012).

3.6 Population and Sample

3.6.1 Population

Population is a collection of individuals or objects of research that has quality and characteristics or specific traits that set by the researcher to learn and then drawn the conclusions (Sugiyono, 2009: 72). Based on the quantity and characteristics of the population it can be understood as a group of individuals or objects of observation that has at least one common characteristic (Cooper and Emory, 1995). The population in this study are various types of SMEs that are situated in Indonesia, particularly in the Greater Jakarta area (Jakarta-Bogor-Depok-Tangerang-Bekasi). The unit of analysis in this study are individuals from management, owners, sales managers, or managers of SMEs. It can be said that these people will be the most aware of the businesses. There are nine industry types SMEs that will be examined in this research paper, namely agriculture, horticulture / Forestry / Fishing, Mining / Quarrying, Manufacturing / Garment / Creative Industries, Electricity / Gas / Water, Building / Construction / Infrastructure, Trade / Hotel / Leisure / Tourism / Restaurants, Transportation / Communications, Finance / rental / Banking / Investment / Capital Markets and services (Central Bureau of Statistics, 2004)

3.6.2 Sample

The sample is part of the number and characteristics possessed by the population (Sugiyono, 2009, p73). The sample in this study is SMEs that use E-commerce or business that intent to have E-commerce based websites or conducting online transactions on a website where transactions occur online. The sampling method used was non-probability sampling, a core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection (i.e., probabilistic methods), which is the cornerstone of probability sampling techniques.  In this research we are using the convenience sample, it is made up of people who are easy to reach. The researchers selected a sample of members of the population because it is in the right place and at the right time (Malhotra, 2010). In the case of sampling there are some things that must be considered one of them small and medium enterprises restrictions are as follows:

Limitation of micro industry, according to Law – Law No. 20 in 2008:

  1. The net worth of less than Rp50.000.000,00 (fifty million rupiahs), excluding land and buildings
  2. Have an annual sales turnover of less than Rp300.000.000,00 (three hundred million rupiah)
  3. The number of workers is less than 5 people

Limitation of small industry, according to Law – Law No. 20 in 2008:

  1. Having a net worth of more than Rp50.000.000,00 (fifty million rupiahs) up to at most 500.000.000,00 (five hundred million rupiahs) not including land and buildings; or
  2. Have an annual sales turnover of more than Rp300.000.000,00 (three hundred million rupiahs) up to at most Rp2.500.000.000,00 (two billion five hundred million rupiahs)
  3. The number of employed 5 up to 19 people
  4. Net worth includes land and buildings less than or equal to 200 million (≤ 200 million)

Limitation medium industry, according to Law – Law No. 20 in 2008:

  1. Having a net worth of more than Rp500.000.000,00 (five hundred million rupiahs) up to at most 10.000.000.000,00 (ten billion rupiahs), excluding land and buildings; or
  2. Have an annual sales turnover of more than Rp2.500.000.000,00 (two billion five hundred million rupiahs) up to at most Rp50.000.000.000,00 (fifty billion rupiahs).
  3. The number of workers 20 to 99 people.
  4. Net worth includes land and buildings more than 200 million but not more than or equal to 10 billion (200 million <wealth ≤ 10 billion)

Thus, the criteria of samples in this study are as follows:

  1. In these type of industries (agriculture, horticulture / Animal Husbandry / Forestry / Fishing, Mining / Quarrying, Manufacturing / Garment / Creative Industries, Electricity / Gas / Water, Building / Construction / Infrastructure, Trade / Hotel / Leisure / Tourism / restaurants, Freight /Communication, Finance / Rental / Banking / Investment / Capital Markets and Services)
  2. Using e-commerce system
  3. Based in the Greater Jakarta area
  4. The annual turnover <50 Billion
  5. Total number of employee are up to 100 people
  6. Engaged in the business from 0 year to over 5 years
  7. Adopt E-commerce from 0 years to over 5 years

3.6.3 Sample Processing Techniques

In this research, in order to determine the sample number, the researcher will used the Slovin formula as follows:

n =     N       ………………………………… (3.1)

1 + N.e2

Description:

n = sample size

N = population size, according to data from the Ministry of Cooperatives and SMEs recorded 56.534.592 the number of SMEs in Indonesia in 2012.

e = per cent leeway inaccuracy due to sampling error that can be tolerated is usually 1 to 10%. In this study determined at 90% confidence level with an error tolerance of 10%.

So,

n =        56.534.592

      1+ 56.534.592 x 0.12

n = 99.99 (rounded to 100)

The above formula used has the assumption of normally distributed population. The minimum size acceptable error based on the design used in this study was 10%. So if Solvin formula used the samples taken is n> 100 respondents, so for the sample used at least 100 SMEs. In this study, researchers took a total sample of 120 questionnaires to reduce the amount of error.

3.7 Data Collection Methods

The method of data collection used to distinguish between primary data and secondary data are explained as follows:

3.7.1 Primary Data

Primary data collected by the researchers is for the specific purposes in response to research problem (Malhotara, 2010). The primary data is obtained directly from the questionnaires / surveys that has been given to respondents. The goal is to determine the impact of the use of E-commerce on the performance of SMEs. The method is used as the primary data research is to distribute the questionnaire by means of self-administered structure that is used to collect data from respondents.

 3.7.2 Measurement of Variables

The form of questions used in the questionnaire is Structured Non disguise which is form by a combination of multiple choice questions that are based on a Likert scale used to measure attitudes, opinions, and perceptions of a respondent. Likert scale allows respondents to express the level of agreement or disagreement related to a particular object. The advantage of Likert scale are it is easily created, shared and understood. Disadvantages of a Likert scale itself is time consuming (Malhotra, 2007). Forms of assessment of the questionnaire answer using the 5 weighting scale is as follows:

Table 8 Weights and Measurement Category Data

Category Value
Fully Disagree / Fully Unsatisfied / Greatly Reduced 1
Disagree / Unsatisfied / Reduced 2
Normal / The Same 3
Agree / Satisfied / Improved (Increased) 4
Fully Agree / Fully Satisfied / Greatly Increased 5

3.8 Questionnaire Design

The questionnaire is a general instrument which is flexible in the primary data collection techniques by way of asking questions to the respondents. The question will be designed in accordance with the input obtained from a secondary data research. In this study the authors will be used a Structural Question. The questionnaire study was made to gather all of the necessary information, which is information relating to the respondents as business owners / workers / managers of SMEs who are using the e-commerce. Researchers will meet with the respondent and asking the respondents to fill out a questionnaire distributed at the venue and wait for the respondents to complete the questionnaires. The author are using self-administered questionnaire in data collection. The types of questions in the questionnaire is a close-ended question, where the respondents choose the answer from the options available. This will facilitate the research in terms of data processing. In addition, for the distribution of the questionnaires, online questionnaires has been made so that it can be shared through electronic media such as social media, internet forums and also personal e-mail. The systematics process in the questionnaire used in this study are:

Figure 3 Questionnaires Design

Introduction

Screening Questions

Business Profile

Part 1: Operation Effect

Part 2: Marketing Effect

Part 3: Performance

Respondent Profile

Source: Processed by Writer

3.8.1 Introduction

This initial section will contain an introduction, the author will tell the name, department and researcher’s university. The author will also tell the purpose of this study and the theme of this research. In this section the author will ask the willingness and the cooperation of the respondents to fill out the questionnaire.

3.8.2 Screening Question

This section is intended to determine whether the respondents who filled out a questionnaire is part of the population. Example of the questions are “What is your position in the company?” and “Is your business are using e-commerce?” Screening Question is used as a screening tool to determine whether the respondents are fit for the criteria of the needed sample.

3.8.3 Business Profile

In this section respondents were asked to fill out the questionnaire about the profile of SMEs owned/managed, in this section there is also a moderator variables such as firm size and E-commerce experience.

3.8.4 Operations Effect

This section aims to determine the impact of the use of E-commerce seen in the influence from the operation aspects to the SMEs performance from respondents.

3.8.5 Marketing Effect

This section aims to determine the impact of the use of E-commerce seen in the influence from the marketing aspects to the SMEs performance from respondents

3.8.6 Performance

This section aims to determine how the performance of SMEs over the use of E-Commerce.

 3.8.7 Profile of Respondents

The questions asked in this section is to know the profile of the respondents who filled out the questionnaire such as age, sex and education background.

3.9 Analysis Technique and Hypotheses Testing

3.9.1 Preliminary Analysis

Preliminary examination conducted by the researchers is to determine the quality of the questionnaire that will be used. The examination carried out on all aspects of the questionnaire with the reference of Malhotra (2007), the questionnaire cannot be proceeded if:

  1. Respondent is not an owner or manager of Micro, Small and Medium Enterprises (SME) in the Greater Jakarta area.
  2. The pattern of responses that the respondents gave shows that the respondents did not fully understand the questions instructed in the questionnaire.
  3. Respondents’ answer are not sufficiently varied or showed any central tendency, for example, respondents simply choose number 4 in all series of 5 scale questions.
  4. The respondents did not fill all the questions in the questionnaire.
  5. The questionnaire received after the collection dateline.

3.9.2 Frequency Distribution

The use of the frequency distribution analysis to see the characteristics of the respondent in a character study. This analysis is done to see the profile of researcher’s respondents, such as: the type of respondent SMEs owned, how long the business has operated, how long has it adopt E-commerce, turnover, total of business assets, total number of employee, business domicile, and marketing region.

3.9.3 Validity and Reliability Test

The research will continue by testing the validity and reliability of the constructed questions that formed the main variables. In this study the validity test was based on face validity, it is the evaluation of the instruction and the words used or language in the questionnaire through pre-testing (Malhotra, 2007). It also based on the factor loading which aims to determine how accurate is the measurement indicators that represent the overall characteristics that has been measured. The higher the KMO value (above 0.5) means that the correlation between the variables’ pairs can be explained by other variables. Therefore the factor analysis can be used (Sharma, 1996). The support of PASW SPSS 18.0 software will be used to calculate of the loading factor.

Reliability testing is an approach to measure the reliability where respondents included in the scope of the same scale at two different times with the conditions of being equal (Wijaya, 2009). Researchers conducted a reliability test to measure the consistency and the reliability of the questions in the questionnaire in compare to the variables. Reliability testing is done by using the method of Cronbach’s Alpha analysis. Alpha coefficient varies from 0 to 1. According to Malhotra (2007), with limits of Cronbach’s Alpha value of 0.6, the questions in the questionnaire is considered reliable, consistent and relevant to the variables or factors in the study. If the scale of the questionnaire is proven reliable, then it will increase confidence in the reliability of the results of this study (Hair et al., 2006).

3.9.4 Classical Assumption Test

The classic assumption test is to determine whether the model obtained has met the standard requirements. It will be performed by using multicollinearity test.

3.9.4.1 Multicollinearity Test (VIF)

Multicollinearity test aims to test the regression model that can be found in a correlation between the independent variables (Ghozali, 2001). A good regression model should not has any correlation between independent variables. If the independent variables are correlated, then the variable is not orthogonal. Orthogonal variable is the independent variable which has a correlation values ​​between independent variables is equal to zero.

In this research the technique used to detect the presence of multicollinearity in the regression model is by observing the Variance Inflation Factor (VIF) value and tolerance value. If the tolerance value is close to 1, and the VIF is around 1 to 10, therefore it can be concluded that there is no multicollinearity between the independent variables in the regression model (Santoso, 2000).

The hypothesis are:

H0: No Multicollinearity

H1: Multicollinearity is present

The VIF value that is less than 10 indicates that the correlation between the independent variables can still be tolerated (Gujarati, 1995) which can be written as follows:

If VIF> 10 then H1 is accepted, there is multicollinearity.

If VIF <10 then H0 is accepted, there is no multicollinearity

 3.9.5 Test Multiple Linear Regression

Multiple Regression Analysis is a statistical technique that simultaneously builds a mathematical relationship between two or more independent variables (Malhotra, 2007). In order to determine the relationship between the free and dependent variables,  multiple linear regression approach will be used with the help of  a program called PASW Statistical tools Product and Service Solution for Windows version 18.0 (SPSS version 18).

The multiple linear regression methods is used to test the hypothesis of this research and it also meant to measure the impact of the Operations and Marketing aspect on Performance in SMEs.

In multiple regression analysis, there are several analyzes performed, there are:

3.9.5.1 The coefficient of determination Test (R2)

The coefficient of determination (R2) essentially measures how far the ability of the model to explain variations in the dependent variable. The coefficient of determination is between zero and one. If the R2 value is small it means that the ability of the independent variables in explaining the dependent variables is very limited. If the value is close to one (1) it means that the independent variables will provide almost all of the information needed to predict the variation of the dependent variables. (Kuncoro, 2003: 220). According to Lind (Suharyadi, 2004: 515), independent variables can explain the dependent variables.

  1. When R2> 0.5 is said to be good or accurate
  2. When R2 = 0.5 is said to be moderate
  3. When R2 <0.5 is said to be lacking

 

3.9.5.2 Significant Simultaneous Test (F-Test)

F test is used to determine the level of significance of the influence of the independent variables together with dependent variables. Testing criteria of the F-test is when the significant value of F is lower than the alpha (5%), it can be said that the variation of independent variables can explain the dependent variables, and if F value is greater than alpha used (5%) it is the vice versa.

  1. F-value <F table with Sig ≥ 0.05 then Ho is Accepted
  2. F-value> F table with Sig ≤ 0.05 then Ho is rejected

 

3.9.5.3 Significant Partial Test (t-test)

T-tests were used to test for significant partial influence of independent variables on the dependent variable in the regression models. This study used a significant level of 5% (a = 0.05).

Testing criteria of T-test are as follows:

  1. t <t table with Sig ≥ 0.05 then Ho is Accepted
  2. t count> t table with Sig ≤ 0.05 then Ho is Rejected

Multiple linear regression equation using the four dependent variables can be expressed in the equation:

Y = b0 + b1x1 + b2x2 + e ……………………………………….. (3.2)

Description:

Y = Performance

a = Constant

b = regression coefficient which shows the number increase or decrease in the dependent variable based on independent variables

x1 = Operations Effect

x2 = Marketing Effect

e = error

3.9.6 Moderating Regression Analysis

MRA (Moderating Regression Analysis) is necessary because there are moderator variables included in this study. Moderator variables are independent variables that will affect (strengthen or weaken) the relationship between the other independent variables to the dependent variable (Ghozali, 2011). Shelma et al. (In Ghozali, 2011) classifies moderator variables into three groups:

Table 9 Types of Variable Moderator

In relation with the Criterion or Predictor Not in relation with the criterion or predictor
No interaction with the predictor 1

Intervening, exogenous, antecedent, predictor

2

 

Moderator (Homologizer))

Interact with the predictor 3

 

Moderator

   (Quasi Moderator)

4

 

Moderator (Pure Moderator)

Source: Ghozali, Imam. 2011. Aplication Multivariate Analysis with IBM SPSS 19 program 5th edition. Semarang: Book Publisher at Diponegoro University.

The moderator variable grouping is based on its relationship with the criterion variable (dependent) and its interaction with the predictor variables (independent). If the moderator variables associated with the criterion or predictor, but not interacting with the predictor, then the variable is not a moderator but an intervening variable, exogenous, or predictor antesenden (quadrant 1). Type of moderator variables in quadrant 2 affects the strength of the relationship but did not interact with the predictor and did not correlate significantly with the criterion and predictor. These variables are included in the type of moderator homologizer variables (Ghozali, 2011).

In the third quadrant moderator variables are associated with the criterion variables or predictor variables. It also interacted with the predictor variables, these variables are included into the quasi moderator type. Quasi moderator is moderator variables that would modify the relationship between the predictor and the criterion variables (Ghozali, 2011). Whereas variable in quadrant 4 is a pure moderator. According to (Ghozali (2011) pure moderator variable does not operate as a predictor variable (independent) but directly interact with the other predictor variables. This variable is not related to the criterion and predictor variables, but it interacts with the predictor variables.

According to (Ghozali 2011), if a variable is a moderating variable, therefore the coefficients should be significantly at 0.05 or 0.10. A variable can be regarded as a moderator variable if it has significance under 0.05 or 0.10, depending on the level of trust that is used.

In this study, the moderator variables are the E-commerce experience and the Firm Size. E-commerce experience and Firm Size is used to moderate the relationship between the operations and marketing effect on the performance. For more details, the effect of moderating variables can be seen in the figure below:

Figure 4 The relationship between the Operations effect, marketing effect with Performance moderated by E-commerce experience.

E-commerce Experience

Performance

– Operation Effect
– Marketing Effect

Performance

– Operation Effect
– Marketing Effect

Figure 3.3 The relationship between the Operations effect, marketing effect with Performance moderated by the Firm Size.

Firm Size

 

3.9.6.1 Moderated Multiple Regression

Moderated multiple regression or abbreviated as MMR, according to Venkatraman (1989), it can be used to test the concept of fit as moderation, which is: “the fit or interaction between the independent variables by moderator variables is a determinant to the dependent variable (criterion)” Aguinis (1995) add that the relationship between the independent variable (denoted by X) with the dependent variable (denoted by Y) is a function of a third variable (denoted by Z), known as the moderator. Multiple Linear Regression procedures is as follows (Aguinis, 1995):

  1.                The first step is to detect the effect (main effect)

The independent variable and the moderator variable partially on the dependent variable, with the following equation:

Y = a + B1X + b2Z + e …………………………….. (3.3)

Where:

a = constant (intercept)

b1 = regression coefficient for the variable X (independent)

b2 = regression coefficient for the variable Z (moderator)

e = error

  1.                The second step is to insert a third variable

This step is a multiplication of the independent variable with moderator variable (X * Z), therefore:

Y = a + B1X + b2Z + b3X * Z + e …………………………….. (3.4)

Where b3, is the regression coefficient for the interaction between the variable X with Z, it is also called interaction term. Equation (2) above is called multiplicative models because of the interaction between the independent variable and the moderator.

Referring to the above procedure, the research proposed two models of regression (MLR) with the details below:

 

 

Table 10Data Analysis with Multiple Linear Regression (MLR)

Model MMR Regression Equation Description
Model 1 Y = a+b1X1 + b2X2 + b3 Z + e Y = Performance

 

X1 = Operations effect

 

X2 = Marketing effect

 

Z = Variable moderator (E- commerce experience/Firm Size)

b1,b2,b3,b4= coefficient regression

a = constant

e = error

Model 2 Y = a+b1X1 +  b2X2 +  b3Z   +

b4 X1* X2* Z + e

Y = Performance

 

X1 = Operations effect

 

X2 = Marketing effect

 

Z = Variable moderator (E- commerce experience/ Firm Size)

b1, b2, b3,= coefficient regression

a = constant

e = error

IV. DISCUSSION AND ANALYSIS

4.1 Test Validity and Test the reliability Pre-test

Pre-test is a step conducted by researchers to test the validity and reliability of the instruments to be used in the data collection tool. This is done in advance to ensure that all research instruments can be used and scientifically accountable before returning the questionnaire distributed to respondents with an amount that has been determined, it was 120 respondents. Ideally, the number of respondents needed to test the validity and reliability of the numbering 15 to 30 people (Malhotra, 2007).

Researchers have been collecting the data for the purposes of distributing questionnaires to pretest the offline and online. For offline pretest, researchers distributed the questionnaires directly to the respondents, while online means that researchers distributed the questionnaires with the help of Google docs to the respondents. Questionnaires were distributed to 30 respondents who qualify to become the respondents of this study, that is SMEs who uses e-commerce and domicile in Greater Jakarta area. Pretest is also used to test whether the construct of questions and other important parts of the questionnaire can be understood by respondents and it has been accurately enough to represent each tested variable. Data were collected through a pretest and then processed through PASW 18 software.

As a result from the literature survey there are 3 variables that encompassed the indicators of each construct operations effect, marketing effect, and a performance which is then used as a reference for this research questionnaire. These are some of the details: 16 indicators variables to construct operations effect, 4 indicator variables for marketing effect, 5 indicator variables for the performance. The following is the result of validity testing via factor loading and reliability using the SPSS PASW 18 software in comparison to the 3 variables presented in
Table 4.1:
 

 

 

 

 

 

Table 11Validity and Reliability Test Results Pretesting Questionnaire

Construct Dimen-sion KMO Status MSA Status Factor Loading Status Cronbach’s

α

Status
Operation Effect OE01-OE20 0.783 Valid >0.5 Valid >0.5 Valid 0.948 Reliable
Marketing Effect ME01-ME08 0.855 Valid >0.5 Valid >0.5 Valid 0.923 Reliable
Performance PR01-PR11 0.819 Valid >0.5 Valid >0.5 Valid 0.941 Reliable

SPSS results reprocessed by Writer

4.1.1 Validity of Test Results Pretesting Questionnaire

Based on existing theory, if the value of the loading factor is greater than 0.50, then the variable as well as the sample as a whole can be further analyzed. The sample is considered qualified for further research (Malhotra, 2007 and Santoso, 2006).

Referring to the validity of the test results, subsequent to the elimination of some indicator variables that are not valid, the rest of the indicator variable on operations aspect are constructs (OE01, OE02, OE03, OE04, OE05, OE06, OE07, OE08, OE09, OE10, OE11, OE12, OE13, OE14, OE15, OE16, OE 17, OE18, OE19, OE20) is valid, it is because of the value of KMO, MSA, and the loading factor is greater than 0.5. KMO value to construct the operation effect is amounted to 0.783, it means that the correlation between indicators variables is at 0.783. It shows that the construct can be used as a variable research model. MSA value is the value of the degree of correlation between variables in order to support the construct validity. MSA value on each indicators variable that compose the construct of operations effect that is OE01 to OE20 MSA has a value of more than 0.5. That means any indicator variable has been declared valid and can be implemented to explain the operations effect. Factor loading value indicates how strong factors that has indicator variable to describe a construct. Factor loading that exist in the indicator variables OE01 to OE20 have value ​​of greater than 0.5, which means the value of factor loading as valid or reliable.

Referring to the test results, it is known that an indicator variable on marketing effect constructs (ME01, ME02, ME03, ME04, ME05, ME06, ME07, and ME08) declared valid. This is because the value of MSA, KMO and loading factor are greater than 0.5. KMO value of construct marketing effect is 0.855, it means that the correlation between indicator variables that support the construct is 0.855, and therefore the construct can be used as a variable research model. MSA value is the value of the degree of correlation between variables in order to support the construct validity indicator, the value of each variable MSA on the indicators that compose the construct marketing effect is ME01 to ME08 has a value of more than 0.5 MSA. That means any indicator variable has been declared valid and can be implemented to explain the marketing effect. Factor loading value indicates how strong factors that has indicator variable to describe a construct. Factor loading that exist in the indicator variables ME01 to ME08 have value ​​of greater than 0.5, which means the value of factor loading as valid or reliable.

Referring to the test results, it is known that an indicator variable on marketing effect constructs (PR01, PR02, PR03, PR04, PR05, PR06, PR07, PR08, PR09, PR10, PR11) declared valid. This is because the value of MSA, KMO and loading factor are greater than 0.5. KMO value of construct marketing effect is 0.819, it means that the correlation between indicator variables that support the construct is 0.819, and therefore the construct can be used as a variable research model. MSA value is the value of the degree of correlation between variables in order to support the construct validity indicator, the value of each variable MSA on the indicators that compose the construct marketing effect is PR01 to PR11 has a value of more than 0.5 MSA. That means any indicator variable has been declared valid and can be implemented to explain the marketing effect. Factor loading value indicates how strong factors that has indicator variable to describe a construct. Factor loading that exist in the indicator variables PR01 to PR11 have value ​​of greater than 0.5, which means the value of factor loading as valid or reliable.

Thus the researchers feel no need to do re specification for validity value for each variable indicator and the constructs worth significant enough.

4.1.2 Pretesting Reliability of the Questionnaire Results

The technique used to test the reliability is Cronbach’s Alpha value that indicates the average correlation value between items that measure in the same construct. Alpha values ​​ranged from 0-1 (the bigger the value the more reliable it is), but Nunnaly (1978) on Pallant (2005) recommend a minimum alpha value of 0.7.

Based on the reliability test results listed in Table 4.1 can be seen that each construct can be said to be reliable, it is because Cronbach’s Alpha of each construct more than 0.7. Each value of Cronbach’s Alpha for each construct is 0.948 to construct operations effect, 0.923 to construct marketing effect, and 0.941 to construct performance.

Just like the validity test, the researchers feel no need to do re specification for reliability value for each variable indicator and the constructs worth significant enough.

4.2 Descriptive Analysis

In this section the researcher will describe the data based on the dimensions in the frequency table. It aims to facilitate in reading the results of this research. The data for the distribution table is derived from the results of the questionnaire data and processed by using SPSS PASW 18.0 software. It will be analyzed by looking at the frequency of the respondents that chose the provided answers. Discussions are also been made based on the profile of the respondents such as type of SMEs, How long has the business been operated, Experience, turnover, Asset, Size, domicile, and marketing region. The matrix of independent variables and the dependent variable will also be discussed in this study. The matrix of independent variables and dependent variable are operation effect, marketing effect and performance.

Table 12Variable Descriptive Analysis of Operations effect, marketing effect, and performance

 

Variables

Sub-Variables Operations effect Marketing effect  

Performance

Mean Mean Mean
Industry Type/ SME sector Agriculture, Plantations, Livestock, Forestry and Fisheries  

3.66

 

4.19

 

4.02

Mining and excavation
Manufacturing, Garment and Creative Industries 3.85 4.25 4.14
Electricity, Gas and Water
Building, Construction and Infrastructure 3.20 4.63 4.36
Trade, Hotels, Entertainment, Tourism and Restaurants 3.84 4.07 4.09
Transport and Communications 3.84 4.13 3.89
Finance, Real Estate, Banking, Investment and Capital Markets  

 

 

Services 3.86 4.21 4.15
Business Time < 1 Year 3.80 4.15 4.07
1-3 Years 3.87 4.18 4.13
3-5 Years 3.98 4.07 4.32
> 5 Years 3.60 4.27 3.99
Experience < 1 Year 3.79 4.13 4.06
1-3 Years 3.89 4.19 4.15
3-5 Years 3.92 4.35 4.33
> 5 Years 3.62 4.25 3.94
Earnings < Rp 50 Million 3.84 4.15 4.12
Rp 50 – Rp 300 Million 3.83 4.15 4.12
Rp 300 Million – Rp 2.5 Billion 3.87 4.34 4.04
Rp 2.5- Rp 50 Billion 3.20 4.63 4.36
Asset < Rp 50 Million 3.85 4.17 4.13
Rp 50 – Rp 300 Million 3.82 4.09 4.01
Rp300 Million – Rp2.5 Billion 3.73 4.43 4.29
Rp 2.5- Rp 50 Billion 4.05 4.13 4.09
Firm Size 1 – 4 people 3.85 4.14 4.08
5 – 19 people 3.80 4.28 4.24
20 – 99 people 3.55 4.38 4.41
> 100 people

Table 12 Variable Descriptive Analysis of Operations effect, marketing effect, and performance

 

Variables

 

Sub-Variables

Operations effect Marketing effect  

Performance

Mean Mean Mean
Domicile Jakarta 3.76 4.06 4.04
Bogor 3.98 4.22 4.25
Depok 3.74 4.24 4.03
Tangerang 3.86 4.13 4.19
Bekasi 4.18 4.44 4.38
Marketing Region Jakarta 3.86 3.97 4.10
JABODETABEK 3.74 4.16 4.11
Domestic 3.88 4.24 4.14
Domestic and International 3.89 4.02 4.01

Sources: processed by SPSS

4.2.1 Descriptive Analysis Operations Variable Effect

In this study, Operations Effect variables measured in 8 dimensions there are Type of SMEs, how long has the business been operated, Experience, turnover, Asset, Size, domicile, and marketing region. Assessment of respondents to the operations effect variable with those eight dimensions are each illustrated by the average value of the variable. From the above table it can be seen that most of the Mean value from the respondents in Operation Effect variable is above 3 or normal or ordinary.

In the characteristic dimension of type of SMEs all of them have mean values of ​​above 3. At the dimensional characteristics of type of SMEs in the Manufacturing, Garment and Creative Industries category, we can see that the highest mean value in the operation effect variable is 3.85. It is also shows that respondents in these categories may have been adopted E- commerce system in enhancing each of their operations aspects. For the category of Mining and Excavation, Electricity, Gas and Water, as well as the categories of Finance, Real Estate, Banking, Investment and Capital Market none of the respondents chose those categories, therefore we cannot determine the results from those categories.

In characteristic dimension of how long has the business been operated, it has a mean value of above 3. In the category of 3-5 years, which has the highest mean value that is 3.98, it indicates that respondents in the 3-5 year category may have been adopting E -commerce system to improve their operational aspect.

In characteristic dimension of Business Experience, it also has a mean value of above 3. In the category of 3-5 years, which has the highest mean value that is 3.92, it indicates that respondents in the 3-5 year category may have been adopting E -commerce system to improve every aspect in the operational.

In characteristic dimension of Business Earnings and Business Asset, both also have a mean value of above 3. In the category of Business Earnings of Rp300 million – Rp2.5 billion and Business Asset of Rp2.5 – Rp50 billion, which has the highest mean value that is 3.87 and 4.05 respectively. This results shows that respondents in that category may have adopted E -commerce system to improve their operational aspect.

For the characteristic dimension of 1-4 people in the Firm Size by total number of employees we can see that the mean value is 3.85 and as for the domicile characteristic dimension in the Bogor category, the mean value is 3.98. Both shows that respondents in the category of Bogor may have also used e-commerce to improve every activity of their operation. In the marketing region sector, we can see that the mean value of the Domestic and International is the highest among the other with 3.89. This also shows that the respondents in this category may already use e-commerce as a tool to improve their business operation.

4.2.2 Descriptive Analysis Variable Marketing Effect

In this study, Marketing Effect variables measured in 8 dimensions there are Type of SMEs, How long has the business been operated, Experience, turnover, Asset, Size, domicile, and marketing region. Assessment of respondents to the operations effect variable with those eight dimensions are each illustrated by the average value of the variable. From the above table it can be seen that most of the Mean value from the respondents in Marketing Effect variable is above 4 or agree.

In the characteristic dimension of type of SMEs all of them have mean values of ​​above 4. At the dimensional characteristics of type of SMEs in the Construction, Building and Infrastructure category, we can see that the highest mean value in the marketing effect variable is 4.63. It is also shows that respondents in these categories may have been adopted E- commerce system in enhancing each of their operations aspects. As for the category of Mining and Excavation, Electricity, Gas and Water, as well as the categories of Finance, Real Estate, Banking, Investment and Capital Market none of the respondents chose those categories, therefore we cannot determine the results from those categories.

In characteristic dimension of how long has the business been operated, it has a mean value of above 3. In the category of >5 years, which has the highest mean value that is 4.27, it indicates that respondents in the >5 year category may have been adopting E -commerce system to improve their marketing aspect.

In characteristic dimension of Business Experience, it also has a mean value of above 4. In the category of 3-5 years, which has the highest mean value that is 4.35, it indicates that respondents in the 3-5 year category may have been adopting E -commerce system to improve their marketing.

In characteristic dimension of Business Earnings and Business Asset, both also have a mean value of above 4. In the category of Business Earnings of Rp2.5 – Rp50 billion and Business Asset of Rp300 million – Rp2.5 billion, which has the highest mean value that is 4.63 and 4.43 respectively. This results shows that respondents in that category may have adopted E -commerce system to improve their marketing aspect.

For the characteristic dimension of 20 – 99 people in the Firm Size by total number of employees we can see that the mean value is 4.38 and as for the domicile characteristic dimension in the Bekasi category has the highest mean value of 4.44. In the marketing region sector, we can see that the mean value of the Domestic and International is the highest among the other with 4.24. It shows that respondents in these category who has the highest mean value may have also used e-commerce to improve every activity of their marketing.

4.2.3 Descriptive Analysis Performance Variables

Performance variables measured in 8 dimensions there are Type of SMEs, How long has the business been operated, Experience, turnover, Asset, Size, domicile, and marketing region. Assessment of respondents to the operations effect variable with those eight dimensions are each illustrated by the average value of the variable. From the above table it can be seen that most of the Mean value from the respondents in Marketing Effect variable is above 4 or agree.

In the characteristic dimension of type of SMEs all of them have mean values of ​​above 4. At the dimensional characteristics of type of SMEs in the Construction Building and Infrastructure in the Marketing Effect sector, we can see that they have highest mean value of 4.63. It is also shows that respondents in these categories agree on using E- commerce system in order to improve their overall performance as a SME. As for the category of Mining and Excavation, Electricity, Gas and Water, as well as the categories of Finance, Real Estate, Banking, Investment and Capital Market none of the respondents chose those categories, therefore we cannot determine the results from those categories.

In characteristic dimension of how long has the business been operated, it has a mean value of above 4. In the category of 3-5 years, which has the highest mean value that is 4.32, it indicates that respondents in the 3-5 year category may have been adopting E -commerce system to improve their overall performance.

In characteristic dimension of Business Experience, it also has a mean value of above 4. In the category of 3-5 years, which has the highest mean value that is 4.33, it indicates that respondents in the 3-5 year category may have been adopting E -commerce system to improve their overall performance as SME.

In characteristic dimension of Business Earnings and Business Asset, both also have a mean value of above 3. In the category of Business Earnings of Rp2.5 – Rp50 billion and Business Asset of Rp300 million – Rp2.5 billion, which has the highest mean value that is 4.36 and 4.29 respectively. This results shows that respondents in that category may have adopted E -commerce system to improve their overall performance.

For the characteristic dimension of 20 – 99 people in the Firm Size by total number of employees we can see that the mean value is 4.41 and as for the domicile characteristic dimension in the Bekasi category has the highest mean value of 4.38. In the marketing region sector, we can see that the mean value of the Domestic is the highest among the other with 4.14. It shows that respondents in these category who has the highest mean value may have also used e-commerce to improve their overall performance as SME.

4.3 Sample Profile

The profile of the respondents will be elaborated start from the type / category of SMEs, how long has the business been operated, how long has it used E- commerce, business turnover, Business Assets, number of employees, domicile and marketing region. This will be used as a respondent profile because it is relevant to this research. There are 120 SMEs who has been using E-commerce technology. All of the questionnaire has been answered well therefore it can be processed to get the overall description of the respondents.  Below here is the result of the business profile of the respondents:

Table 13Sample Profile

Respondent Profile Total Percentage(%)
Type of SME
Agriculture, Plantations, Livestock, Forestry and Fisheries 4 3.3
Mining and excavation 0 0
Manufacturing, Garment and Creative Industries 41 34.2
Electricity, Gas and Water 0 0
Building, Construction and Infrastructure 1 .8
Trade, Hotels, Entertainment, Tourism and Restaurants 47 39.2
Transport and Communications 4 3.3
Finance, Real Estate, Banking, Investment and Capital Markets 0 0
Services 23 19.2
Total 120 100
How long has the business been operated
< 1 year 46 38.3
1-3 Years 57 47.5
3-5 Years 9 7.5
> 5 Years 8 6.7
Total 120 100
How long has the business adopted E-commerce
< 1 year 59 49.2
1-3 Years 52 43.3
3-5 Years 6 5.0
> 5 Years 3 2.5
Total 120 100
 

Earnings per annum

< Rp 50 Million 82 68.3
Rp 50 – Rp 300 Million 27 22.5
Rp 300 Million – Rp 2.5 Billion 10 8.3
Rp 2.5- Rp 50 Billion 1 .8
Total 120 100
Total Assets
< Rp 50 Million 87 72.5
Rp 50 – Rp 300 Million 25 20.8
Rp 300 Million – Rp 2.5 Billion 7 5.8
Rp 2.5- Rp 50 Billion 1 .8
Total 120 100

Table 13 Sample Profile (Continued)

Respondents Profile Total Percentage (%)
Number of Employees
1 – 4 people 99 82.5
5 – 19 people 19 15.8
20 – 99 people 2 1.7
> 100 people 0 .0
Total 120 100
Domicile
Jakarta 58 48.3
Bogor 8 6.7
Depok 26 21.7
Tangerang 12 10.0
Bekasi 16 13.3
Total 120 100
Marketing Region
Jakarta 9 7.5
JABODETABEK 39 32.5
Domestic 59 49.2
Domestic and International 13 10.8
Total 120 100

Source: Field data are processed by the researcher.

4.3.1 Type / category of SMEs Respondents

Based on the results of the questionnaire regarding the type / category of SMEs, it can be seen all SMEs has used e-commerce which means that all aspects of the respondents can represent the variables based on each type of SME. Most of the respondents are Trade, Hotel, Leisure, Tourism and Restaurants (39.2%) it represents a population where the trade sector, Hotel, Leisure, Tourism and Restaurant is the greatest. While other categories of agriculture, horticulture, Livestock, Forestry and Fisheries has 3.3%, and 34.2% came from the category of Manufacturing, Garment and Creative Industries. As for the category of Building, Construction and Infrastructure it has only 0.8%, Transportation and Communications has 3.3%, and 19.2% came from the category of services. The category of Mining and Quarrying and Electricity, Gas and Water as well as the category from Finance, Real Estate, Banking, Investment and Capital Markets cannot be determined as there are no respondents come from these categories.

4.3.2 How long has the business been operated

Based on the results from the questionnaire, the highest results is from 1-3 year(s) that is 47.5%. So there are 47.5% out of 120 respondents that has 1-3 years long that their business has been operated. Whereas here is the other results:  <1 year was (38.3%), 3-5 years is (7.5%) and over 5 years was (6.7%).

4.3.3 How long has the business use of E-commerce

Based on the results of questionnaires on how long has the business been adopting / using E-commerce, there are <1 year was (49.2%), 1-3 years (43.3%), 3 – 5 years is (5%) and more than 5 years was (2.5%).

4.3.4 Turnover per year

It shows that the number of respondents who have a business turnover of less than 50 million is 68.3%, 50-300 million is 22.5%, 300 million – 2, 5 billion is 8.3% and has a turnover of 2.5 billion – 50 billion is 0.8%. It shows the average SMEs turnover per annum that use E-commerce is less than 50 million.

4.3.5 Total Assets

The number of respondents who have assets worth less than 50 million is 72.5%, Respondents who have assets worth 50-300 million is 20.8%, respondents who have assets worth 300 million – 2.5 billion is 5.8% and respondents who have assets worth 2.5 billion is 0.8%.

4.3.6 Total number of Employee

The results shows from the questionnaire are respondents who have a number of workers of 1-4 people is 82.5%, as for 5-19 people is 15.8%, and 20-99 people is only 1.7%.

4.3.7 Domicile Enterprises

Based on the results of the questionnaire regarding the domicile of SMEs that are domicile in Jakarta area amounted to 48.3%, for SMEs located in Bogor is 6.7%, 21.7% from Depok, 10% from Tangerang and Bekasi is 13.3%.

4.3.8 Regional Marketing

Based on the results of the questionnaire regarding the marketing region in Jakarta alone is 7.5%, 32.5% in JABODETABEK, the whole Indonesia is 49.2% and domestic and international marketing is 10.8%.

4.4 Classical Assumption Test

Researcher’s classic assumption test done to see if the model obtained meets the requirements of the test. Only multicolinearity Test will be performed in this test. Below here is the results of the classical assumption test:

 4.4.1 Multicollinearity Test (VIF)

Multicollinierity test is used to indicate the presence or absence of a direct relationship (correlation) between independent variables. Multicollinearity occurs if the value of Variance Inflation Factor (VIF) greater than 10 or smaller than the tolerance value of 0.10. Multicollinearity formulation of hypotheses are:

  1. Ho: No Multicollinearity
  2. H1: Multicollinearity present

Basis for a decision to test Multicollinearity

  1. If VIF> 10 (Ho is rejected: Multicollinearity present
  2. If the VIF <10 (Ho accepted: no Multicollinearity

Table 14Test Multicollinearity

Collinierity Statistic Test ( VIF ) Results
Operations effect 1.092
Marketing effect 1.096
E-commerce Experience 1.074
Firm Size 1.090

Sources: SPSS

Based on the analysis, there is no independent variables who had the variance inflation factor (VIF) of over ten. Thus, the results of the analysis showed no Multicollinearity problems. Operations Effect Variable has a VIF value of 1.092. Marketing Effect Variable has a VIF value of 1.596. E-commerce experience Variable has VIF value of 1.074 and Firm size Variable has a value of 1.090 VIF. It can be concluded that there is no direct relationship between the independent variables.

4.5 Hypothesis 1 and Hypothesis 2

  • Hypothesis 1: The use of E-commerce on the aspect of operation has a positive influence on the performance of SMEs in Indonesia
  • Hypothesis 2: Use of E-commerce on the aspect of marketing has a positive influence on the performance of SMEs in Indonesia

Both hypotheses, H1 and H2, are tested using multiple regression analysis, it is to determine the relationship between the independent variables on the dependent variable.

4.5.1 Regression Analysis

Multiple regression analysis is used to analyse the relationship between two or more independent variables and dependent variable. Multiple regression was also used with the aim of predicting degree of the influence between the independent variables and dependent variable.

4.5.1.1 Analysis of Marketing and Operations Effect of Against the Performance (Coefficient of Determination R2)

The first factor to be seen in this hypothesis is the value of R Square or commonly called the coefficient of determination (R2). This coefficient shows how much the independent variables can explain the variation of the dependent variable.

Table 15Test R and R Square

Model Summary

Model  

 

R

 

 

R Square

Adjusted R Square Std. Error of the Estimate
1 .802a .642 .636 .60301463

a. Predictors: (Constant), REGR Marketing effect, IndustryDummy, Operations REGR effect

Source: Processed Data SPSS

From the results above, it shows that the value of R obtained from regression test amounted to 0.802 or equal to 80.2%. Based on the assessment criteria correlation expressed by Sugiyono (2007) these values are in the strong category. Meanwhile the calculation of the value of R-squared obtained is 0.642 or equal to 64.2%.

Thus Operations and Marketing Effect has 64.2% influence against the performance of SMEs in the Greater Jakarta area.

4.5.1.2 Simultaneous Analysis of Marketing and Operations Effect of Against the Performance (Variance Test Analysis)

Analysis of Variance (ANOVA) or also known as the F-test. It is a test to look for a significance level of simultaneous effect of independent variables on the dependent variable.

Table 16F Test -Anova

ANOVAb

Model Sum of Squares Df Mean Square F Sig.
1 Regression 76.456 2 38.228 105.129 .000a
Residual 42.544 117 .364
Total 119.000 119

a. Predictors: (Constant), Marketing REGR effect, IndustryDummy, Operations REGR effect

b. Dependent Variable: REGR Performance

Regression analysis method (F-test) used in the test showed a significant result. These significant results showed the influence on both of the operations and marketing effect on the performance.

These test results give a big F value which is 105.129 that is larger than F table of 2.503 and 0.000 significance value. The significant values ​​is below 0.05 therefore the regression model can be used to predict the Performance. It can also be concluded that both Operations and Marketing Effect is influencing the Performance in SMEs in the Greater Jakarta area.

4.5.1.3 Analysis of Marketing and Operations Effect of against the Performance partially (T-test)

T analytical testing conducted to determine whether each independent variable that are Operation effect and Marketing effect has any influence with the dependent variable which is the Performance.

Table 17 Test t

Coefficientsα

Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
  (Constant) .000 .055 .000 1.000
REGR

Operations Effect

.258 .057 .258 4.505 .000
REGR

Marketing Effect

.694 .057 .694 12.110 .000
  1. Dependent Variable: REGR Performance

The test results of the individual or partial regression model shows significant results on the two variables, that are Operations effect with the t values of 4.505 and significant value of 0.000 and the Marketing effect with the t value of 12.110 and significant value of 0.000. Both of these variables has a value of t is greater than t table 1.667 and a significant value of less than 0.05, so it can be declared to have significant influence against the dependent variable, performance.

Table 18Test results for H1 & H2

 

Hypothesis

 

Description

 Beta Significant Value Results
 

 

   H1

 

Operations effect has a significant influence against the SMEs’ performance

 

 

0.258

 

 

0.000

 

H1

Accepted

   H2 Marketing effect has a significant influence against the SMEs’ performance  

 

0.694

 

 

0.000

H2

Accepted

From the above hypothesis test results, it shows that both of the hypothesis H1 and H2 are accepted. The research proved that using E-commerce for SMEs has a positive influence for the performance in the operation and marketing aspect.

4.6 Hypothesis Testing Hypothesis 3 & 4

4.6.1 Moderate Regression Analysis

This analysis is used to determine whether the e-commerce experience and firm size is the moderator variable effect on the relationship between operations and marketing effect with the performance. Testing the hypothesis with moderator variables can be determined by using two regression models (Sanchez and McKinley, 1998 and Bedeian and Mossholder, 1994). In the first model, regression test held between the independent variables, moderator variables, dummy variables / controls on the dependent variable. Then in the second model, the interaction test is done, which is the independent variables multiplied by a moderator variable (the independent variable x variable moderator). In this moderate regression there will be some regression testing that are R Square, F test and T test.

 4.6.1.1 The relationship of E-commerce Experience (Moderator) to the Operations and Marketing Effect against the Performance (hypothesis 3a and 3b Test)

  • Hypothesis 3a: The longer the experience of using E-commerce, the greater the positive impact of operating aspects to the performance of SMEs in Indonesia
  • Hypothesis 3b: The longer the experience of using E-commerce, the greater the positive impact of marketing aspects of the performance of SMEs in Indonesia

Regression moderation is conducted to determine whether the e-commerce experience can Moderate the relationship between operations and marketing effect with Performance. Here are the results of regression analysis using SPSS PASW 18 which have been summarized into a single table:

Table 19 Moderation Regression Test Results – E-commerce Experience

Variabel Model 1 Model 2
Beta t sig Beta t sig
Operations Effect 0.258 4.490 0.000 0.467 3.080 0.003
Marketing Effect 0.695 12.034 0.000 0.782 5.238 0.000
E-commerce Experience -0.015 -0.195 0.846 -0.028 -0.349 0.728
Operations Effect x E- commerce Experience -0.130 -1.485 0.140
Marketing Effect x E- commerce Experience -0.060 -0.645 0.520
R square 0.643 0.651
d R square 0.800
F 69.523 42.463

Source: SPSS Data that has been reprocessed by writer

In Table 4.9, model 1 shows the main effects of the operations effect, marketing effect and moderator variables (E-commerce experience) to the overall performance of the company. Model 2, adds two interaction terms of which are operations multiplied by the effect of E-commerce experience and marketing effect multiplied by E-commerce experience.

From the above results it can be seen that in the first model the value of R-square obtained is 0.643 or equal to 64.3%, while the second model R Square is 0.651 or 65.10%. Based on the assessment criteria of correlation written by Sugiyono (2007), both values ​​are in the strong category. However, the difference in the increase of R Square from model 1 to model 2 is only 0.008, it means it is not statistically significant because the value of increment is too small. According to Hair et al., 2006 the bigger the change in R square of the Model 1 and 2, therefore statistically the influence of moderator variables is expected to be more significant.

Regression analysis F test that were used show significant results. These significant results showed a significant influence of the E-commerce experience moderating effect and both marketing operations effect together on performance. These test results provide high F statistical value which is 69.523 that is larger than the f table value of 2.503 in model 1, while for model 2 it has a value of 42.463. It is shows that the regression model is significant to predict the result of the Performance.

At the T-test and significance test, model 1 obtained significant results on two independent variables that area Operations effect with t values ​​of 4.490 and 0.000 significant value which is smaller than 0.05 and marketing effect with t values ​​of 12.034 and significant value of 0.000 which is also smaller than 0.05. Both of these variables has a value of t is greater than t table 1.667, so it can be declared to have significant influence towards the dependent variable performance. However, the moderator variable E-commerce experience has a t value of -0.195 which is smaller than t table 1.667 and high significant value 0.846 which is bigger than p table that is 0.05 (p> 0.05), so the moderator variable E-commerce experience has no significant effect on the performance variables. In model 2, it shows that two independent variables still has a significant results that are t value for the operations effect ​​is 3.080 and the significance value is decrease to 0.003. The t values for marketing effect ​​is 5.328 and the significance value is unchanged which is 0.000. As for the moderator variable E-commerce Experience, it is not significant, it has a t value of -0.349 and significance value increased by 0.728, so for the moderating variable E-commerce experience, the researcher are prefer to choose  model 2. Interactions between independent variables multiplied by a variable moderator (operations effect × E-commerce experience and marketing effect × E-commerce experience) is also not significant. For the operation effect × E- commerce experience t value obtained is -1.485 and significance value is 0.140, whereas the marketing effect × E-commerce experience the t value obtained is -0.645 and the significance value of 0.520.

Thus, from the moderate regression test results it can been seen that the relationship between operations effect and marketing effect on the performance of SMEs in Indonesia are not affected by the E-commerce experience. These findings do not support both hypothesis, hypothesis 3a and hypothesis 3b.

Table 20Test Results of  H3a & H3b

 

Hypothesis

 

Variable

B value (Beta) Significant value Conclusion
 

 

 

H3a

E-commerce experience (operations effect x E- commerce experience)  

 

 

-0.130

 

 

 

0.140

 

H3a reject

 

 

 

H3b

E-commerce experience (marketing effect x E- commerce experience)  

 

 

-0.060

 

 

 

0.520

 

H3b reject

Source: Processed researchers

Based on the above hypothesis test results showed that the two hypotheses are rejected, namely H3a and H3b. From some of the above data processing results can be seen that the significance of the interaction operations effect x E-commerce experience is 0.140 and it is far greater than 0.05, so the variable is not significant. The effect of marketing variables x E-commerce experience has similar result, it has a significance value of 0.520, and therefore it is also not significant. It can be concluded that the variable E-commerce experience is not a moderator variable. So the effect relationship between operations and marketing effect will not weaken the performance even though E-commerce experience increases, and vice versa.

4.6.1.2 Firm Size Moderator size in relation with Operations effect and Marketing Effect on Performance (Hypothesis 4a and 4b)

  • Hypothesis 4a: The larger the size of a business based on the number of Employees, the greater the positive impact provided by the operating aspects of the performance of SMEs in Indonesia
  • Hypothesis 4b: The larger the size of a business based on the number of Employees, the greater the positive impact provided by the marketing aspects of the performance of SMEs in Indonesia

Regression moderation is conducted to determine whether the Firm Size can Moderate relationship between operations effect and marketing effect to Performance. Here are the results of regression analysis using SPSS PASW 18 which have been summarized into a single table:

Table 21 Moderation Regression Test Results – Firm Size

Variable Model 1 Model 2
Beta t sig Beta t sig
Operations Effect 0.267 4.655 0.000 0.457 2.961 0.004
Marketing Effect 0.683 11.868 0.000 0.435 2.341 0.021
E-commerce Experience 0.180 1.407 0.162 0.095 0.707 0.481
Operations Effect x Firm Size -0.153 -1.288 0.200
Marketing Effect x Firm Size 0.213 1.375 0.172
R square 0.648 0.659
d R square 0.110
F 71.334 44.145

Source: SPSS data has been reprocessed

In Table 4.11, model 1 shows the main effects of the operations effect, marketing effect and moderator variables (Firm Size) to the overall performance of the company. Model 2 adds two interaction terms, they are operations effect multiplied by Firm Size and marketing effect multiplied by Firm Size.

From the above results it can be seen that in the first model the value of R-square obtained is 0.648 or equal to 64.80%, while the second model is 0.659 or 65.90%, based on the assessment criteria expressed by the correlation Sugiyono (2007) the value of R Square is located in the strong category. Furthermore, there is an increase in R Square of models 1 to model 2, the increase is only 0.011, it means that it is statistically significant because the increase is quite large. According to Hair et al., 2006 the bigger the change in R square of the Model 1 and 2, therefore statistically the influence of moderator variables is expected to be more significant.

Regression analysis (F test) were used show significant results. These results showed a significant influence of the Firm size moderating effect and operation effect and marketing effect together on performance. These test results shows big F statistical value which is 71.334 that is larger than f table of 2.503 in model 1, while for model 2 it has a value of 44.145, it indicates regression model is significant to predict the Performance. Additionally these results show that the moderator Firm Size variable has a strong influence on the E-commerce impact on the performance of SMEs in Indonesia.

Table 4.11 gives the results of the regression analysis moderated by Firm Size variable as a moderator variable for hypothesis 4a and 4b. At t test results and significance, the model 1 obtained significant results on two independent variables they are Operations effect with t value of 4.655 and a significance value of 0.000, which is less than 0.05, and marketing effect with t values ​​of 11.868 and significance of 0.000 which is also less than 0.05. Both of these variables has a value of t is greater than t table 1.667, so it can be said that it has a significance influence towards the dependent variable performance. However, the moderator variables Firm size has a t value of 1.407 which is smaller than t table that is 1.667 and has 0.162 significance value which is greater than p table 0.05 (p> 0.05), so the moderator Firm Size variable is not significant to the performance variable. In model 2, it shows that two independent variables still has a significant results that are t value for the operations effect ​​is 2.961 and the significance value is fell by 0.004.  The marketing effect is also decreased, the level of significance is increased by 0.021, while for t value is 2.341. Therefore in this independent variable the researchers prefer the first model over the second model because the value in model 2 has decreased progressively. Firm Size for moderator variables on the second model is also not significant, the t value is 0.707 and the significance value decreased by 0.481. When the value is decreasing it shows that it is not significant, so that researchers prefer the first model (Model 1). Interactions between independent variables multiplied by a variable moderator (operations effect × E-commerce experience and marketing effect × E-commerce experience) is not significant. For the operation effect × E- commerce experience t value obtained is -1.288 and significance value is 0.200, whereas the marketing effect × E-commerce experience the t value obtained is 1.375 and the significance value of 0.172.

From the results of moderate regression test shows that the relationship between operation effect and marketing effect on the performance of SMEs in Indonesia are not affected by Firm size. These findings do not support the hypothesis Hypothesis 4a and 4b.

Table 22 Results of testing H4a & H4b

 

Hypothesis

Variable & Interaction B Value (Beta)  Significant value Conclusion
 

 

H4a

Firm Size (operations effect x Firm Size)  

-0.153

 

0.200

H4a rejected
 

 

H4b

Firm Size (marketing effect x Firm Size)  

0.213

 

0.172

 

H4b rejected

Source: Processed by Researchers

From the above hypothesis test results showed that the two hypotheses are rejected, namely H4a and H4b. From some of the above data processing results can be seen that the significance of the interaction operations effect x Firm Size is 0.200 which is much greater than 0.05, so it is not significant. The effect of marketing variables x Firm Size also shows similar results, It has significance value of 0.172 which is also greater than 0.05. It can be concluded that the Firm Size variable is not a moderator variables, thus the relationship between operations effect and marketing effect will not weaken the performance even though the Firm Size increases, and vice versa.

4.7 Discussion

Based on the analysis in previous sections, it can be seen that the effect of marketing operations and the effect of each significant influence on performance. However, Firm Size and E-commerce experience does not have a significant influence in moderating (amplify or attenuate) the relationship between operations effect and marketing effect on performance.

From the Hypothesis testing of E-commerce experience it shows that it has no significant effect of giving a moderate effect on operations effect and marketing effect on the performance of SMEs in Indonesia. This matter prove that the benefits of E-commerce in the E-commerce experience is not felt on the technology of E-commerce used by the company. When viewed from the perspective of organizational behavior (Senge, 1990), not all organizations / businesses that use E-commerce technology has learned from their experiences. It means that if an organization does not learn (and there are many companies, especially in the SME level), then some companies particularly in the SMEs are still less sensitive in recognizing the duration of implementing E-commerce technology.

Based on the hypothesis testing of Firm size, it can be found that there is no significant effect of giving a moderate effect on operations and marketing effect on the performance of SMEs in Indonesia. This proves that the benefits of E-commerce in the Firm size has not impacted enough on the technology of E-commerce used by the company. These results are consistent with the results of research conducted by Love and Irani (2004) who found that the level of investment in information technology is not affected by the size of the organization, as well as the findings of Wu et al., 2006 that the Firm Size has no significant effect to the company performance. This means the Firm Size that based on the number of employees cannot achieve the synergies with their investments.

Based on the hypothesis it can be proved that the operation effect have a significant impact on the performance, this is in accordance with the initial hypothesis taken in the journal of Ramanathan et al., (2012). This proves that the benefits of E-commerce in Firm Size has been felt in E-commerce technology used by the company. In general, it is possible to increase the investment in the aspect of the operation for enterprises or SMEs. These results are similar to several studies that has been conducted such as Wang and Ahmed (2009) and Wu et al. (2003). Likewise with the findings of Daniel and Grimshaw (2002), who found that large businesses showed great interest in adopting e-commerce to increase the efficiency of its operation aspect. If SMEs invest more in their operations aspects, they can increase their profits greatly. For example, the ordering process can be handled more efficiently when using E-commerce technology. Likewise, high levels of investment for the use of E-commerce can help in improving the security of transactions. Similarly, it is also to improve the accuracy and reliability of business in shipping goods to customers.

Based on the hypothesis it can be proved that the marketing effect has a significant impact on the performance, this is in accordance with the initial hypothesis in the journal of Ramanathan et al., (2012). This proves that the benefits of the use of E-commerce has been felt in improving the performance of the company. This means that they invest more in the technology used by the companies or SMEs in the aspect of marketing. It shows that indicator variables such as brand recognition, product display and online advertising are all related to the business performance.

In a previous study by Ramanathan et al., (2012) the author found that the impact of the use of E-commerce operation effect and marketing effect is a significant predictor of the performance. These results indicate that e-commerce really improve operational efficiency in companies or SMEs in Taiwan, where all the operations and marketing is done online, it has an immediate effect on cost reduction and performance improvement efforts such as increasing competitive advantage, finding new customers, and increase overall profitability. However, the E-commerce experience cannot provide a moderating influence that is bracing or even weaken the relationship between the impact of the use of E-commerce performance. Other findings where only big corporations or Larger SMEs who dare to invest more in the aspect of the operation can feel a significant influence on the use of E-commerce on performance. On the other hand, the size of the company or SMEs does not affect the impact of the use of E-commerce, especially the marketing aspect on the performance. Smaller company or SMEs are considered to make improvements in marketing when compared with bigger companies or SMEs. It is almost the same as the studies conducted in Indonesia, but the difference is on the Firm Size, in the research in Indonesia it shows that there is no significant relationship in the moderation between the operation effect with the performance. Another difference is that people in Taiwan are more courageous in investing in a wide range of their business activities compared to people in Indonesia, therefore the companies that are more dare to make a huge investment in business activities such as the operation and marketing will improve their business performance more. For that aspect, E-commerce in Taiwan get more impact or benefit compared to Indonesia. One interesting thing of this research study is about the Creative Industries. It has been test with some of the variables that exist in this research and the result is it has no effect, it means that when creative industry is compared to the type / category of other SMEs there is no difference. Although the creative industry is growing rapidly and contribute a large portion to GDP in Indonesia. Thus, all types / categories of SMEs are suitable to adopt E-commerce, especially in terms of operation and marketing to improve the business performance of SMEs in Indonesia.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

V. CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusion

Based on the results of the research and testing conducted basing on the hypotheses that have been formulated in advance to help answer this research purposes, the results of this study can be summarized as follows:

  1. This research has provided an overview of the development in the use of E-commerce on operational level in which provides a significant influence in improving the performance of SMEs, particularly the performance of business in the Greater Jakarta. Whereby the result of hypothesis testing was derived by multiple linear regression method used. There is a positive impact by operational effect applicable to the SMEs from using e-commerce. One of the founding was that operations such as order processing, order fulfillment and supplier development, brought a positive impact on the use of E-commerce, improving the overall efficiency. The less developed impactful effect on the benefits can be gained from the use of SME E-commerce particularly in the short time ahead.
  1. Based on the established hypothesis about marketing effect of E-commerce on SMEs. It is concluded that the usage of E-commerce has a positive marketing effect and significantly help increase the business performance of SMEs. Many companies prefer in increasing their investment marketing effort such as in online advertising, display products as well in brand recognition. Businesses sometimes must often double their marketing effort, as it is still not a common practice for Indonesian to shop online and particularly very difficult for SMEs due to limited resources available at their disposal. The people in Indonesia still have preferential over traditional transactions compared to transacting online.
  1. From the regression model used in this research to test our hypothesis about E-commerce usage experience, it has been determined E-commerce experience on SMEs do not affect the impact of the use of E-commerce (variable operations and marketing effect effect) on company business performance. Whereas the results of hypothesis testing known moderate multiple linear regression method has no significant effect in moderating (strengthen or weaken) operations and marketing effect on the performance of SMEs. It can be concluded that SMEs with a moderating variable E-commerce experience (measured in number of years) in adopting e-commerce applications have not shown better performance than the experiences lower.
  1. This research has provided an overview of how the variables work in moderating Firm Size (strengthen or weaken) the relationship between operations and marketing effect on the business performance of SMEs. Based on the results of hypothesis testing, firm Size in the setting of SMEs does not affect the impact of the use of E-commerce (variable operations and marketing effect effect) on company performance. Where based on the results of multiple linear regression test it had no significant effect in moderating (strengthen or weaken) operations and marketing effect on the business performance of SMEs. This study still has not been able to prove that a larger company (with a higher number of employees) can use the E-commerce to better improve operations and marketing aspects than small sized firms.

5.2 Suggestions

To further enrich the findings in this study, Advice that may be useful for further research in the field of study which investigated as follows:

  1. Based on the results of this research it is proven that E-commerce brings positive advantages for SMEs, so it is advisable for SMEs that have no E-commerce presence to start implementing it. In addition, for SMEs already have one should be more invested in operations and marketing aspects in order to improve business performance to be optimal.
  2. To analyze more about the correlation of role from moderation running an E-commerce business experience with Firm Size in increasing the influence of Operations and Marketing effect on the business performance of SMEs, provide any additional variables either as independent variables or mediating variables by adding how the management aspects of an organization or add the power that are considered to have a significant effect in improving the performance of SMEs. Besides this research can be further enhanced by adding indicators to measure the impact of marketing aspects related to the use of E-commerce. Moreover, the interaction between operations and marketing and its impact on performance can be explored using the Structured Equation Modeling (SEM). Because the technology E-commerce is an area of ​​highly dynamic and evolving, a similar survey can be conducted over various periods of time and trends over time.

5.3 Limitations of Research

Research conducted has several limitations as follows:

  1. The case study research, namely SMEs using e-commerce or of buying and selling online at various websites either individual or public website is much less understood by the respondent. This is possible because generally the form of E-commerce itself is focused on only one seller to many buyers. So it is a bit difficult for research in finding respondents.
  2. This study was conducted only on SMEs in general, which is where the study of each category of SMEs to the research model is not focused in this research so that research results generally applied in SMEs is to overlook special characteristics of each sector of industry or SMEs.

5.4 Further Research

Subsequent studies by several things, among others:

  1. Better use of respondents focus on SMEs that already has their own E-commerce sites are single (one seller to many buyers).
  2. Future research is expected to focus on one type or specific SME sector.

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Appendix

QUESTIONNAIRE RESEARCH

Dear Mr. / Mrs. / Mr. / Respondents with regards,

I fully understand that your time is limited and precious. Nevertheless, we hope that your willingness to take the time to fill out this questionnaire.

The questionnaire is organized in the framework of the preparation of final project (thesis) which is a graduation requirement for Master degree Economic and Management faculty, Beihang University. The questionnaire is intended to determine how Marketing and Operation impact on E- commerce in SME in Indonesia especially in the greater Jakarta Area (Jakarta-Bogor-Depok-Tangerang-Bekasi).

This questionnaire is used for scientific purposes, therefore all of your answers will be kept confidential. I really appreciate on the willingness and cooperation. Thank you

Sincerely,
Eunice Tobias

[email protected]

Beihang Univesity – Master Students at Management and Economic Faculty

Company Name: ……………………………………………………………

E-mail: ……………………………………………………

I. Screening

Instructions:

– Read each question carefully

– Provide a cross (X) in the column that has been provided

Does your business have at least been using the Internet for these choices?

* 1. Finding Information Online Booking 2. In your business 3. Communication via E-Mail with customers, suppliers or business partners.

1. Yes (Continue)

2. (If it does not, you can stop here, thank you for your time)

Does your business is located in the Greater Jakarta area? *

1. Yes, specify _ (Continue)

2. (If it does not stop here, thank you for your time)

Is your business sales turnover of less than Rp 50 billion / year? *

1. Yes (Continue)

2. (If it does not stop here, thank you for your time)

II. Business profile

Charging instructions :

– Read each question carefully

– Choose one answer to the question below

– Provide a cross (X) in the column that has been provided

We guarantee the anonymity along with the answers you provide.

No Questions Answers
1. What type of SME is your business?
  1. Agriculture, Plantations, Livestock, Forestry and Fisheries
  2. Mining and excavation
  3. Manufacturing, Garment and Creative Industries
  4. Electricity, Gas and Water
  5. Building, Construction and Infrastructure
  6. Trade, Hotels, Entertainment,
  7. Tourism and Restaurants
  8. Transportation and Communications
  9. Finance, Real Estate, Banking, Investment and Capital Markets
  10. Services
2. How many years has you established your company?
  1. < 1 year
  2. 1-3.4 Years
  3. 3.5 – 5 Years
  4. > 5 Years
3. How long have you adopted E-commerce for your business?
  1. < 1 Year
  2. 1-3.4 Years
  3. 3.5-5 Years
  4. > 5 Years
4. Business earning per annum?
  1. < Rp 50 Million
  2. Rp 50 – Rp 300 Million
  3. Rp 300 Million – Rp 2.5 Billion
  4. Rp 2.5- Rp 50 Billion
5. Total business asset/business value?

 

  1. < Rp 50 Million
  2. Rp 50 – Rp 300 Million
  3. Rp 300 Million – Rp 2.5 Billion
  4. Rp 2.5- Rp 50 Billion
6. Total work force/employee?
  1. 1 – 4 people
  1. 5 – 19 people
  1. 20 – 99 people
  1. > 100 people
7. Marketing region?
  1. Jakarta
  2. JABODETABEK
  3. Domestic
  4. Domestic and International

III. Effect Operations, Marketing Effect & Performance

Instructions:

– Read each question carefully

– Select one of the answers by providing a cross (X) as follows: 1 = Strongly Disagree 2 = Disagree 3 = Neutral 4 = Agree 5 = Strongly Agree

– Answer all questions that there was no omission

– We guarantee the anonymity along with the answers you give

– The definition of what is meant by the E-Commerce is the process of information exchange, buying and selling over the internet, transactions through other online media and communication through the medium of e-mail, Instant messaging or chat.

A. Operations Effect

 

 

No

 

Questions

 

 

Very disagree

 

 

Disagree

 

 

Neutral

 

 

Agree

 

 

Very agree

1 E-commerce has been adding value to the products / services that we offer to customers
2 E-commerce has improved the quality of our products / services
3 E-commerce makes it easier for us in the decision-making
 

 

No

 

Questions

Very disagree Disagree Neutral Agree Very agree
 

4

 

E-commerce assist customers in determining the order

 

5

 

Customers can specify the order at any time (24 hours)

 

6

 

The website that we use is very simple in its regulation

 

7

 

We provide all the necessary information the customer in the transaction (the way, the terms and conditions of purchase)

 

8

 

By using E- commerce, our efforts can provide goods / services to customers in full.

 

No

 

Questions

 

Very disagree

 

Disagree

 

Neutral

 

Agree

 

Very Agree

9 By using E-commerce, customer is confident that all of their data are confidentiality guaranteed
10 The website has a feature that we use e-mail confirmation after customers make a booking / transaction
11 Confirmation given can be trusted by customers
12 We use the E- Commerce businesses for distribution activities * (check the delivery status of goods and services to consumers, distributors)
13 We can improve the reliability in the delivery of products / services to customers
 

 

No

 

Questions

Very disagree Disagree Neutral Agree Very Agree
14 By using E- commerce we can respond to and serve customers when booking quickly
15 We can offer a more convenient after-sales service to customers
16 Our efforts to use the Internet to get information supplier supplies
17 E-commerce helps us to find a new supplier
18 By using E- commerce, customers can make payment transactions with ease
19 With E-commerce, we can communicate more effectively Supplier
20 Our efforts have been able to strengthen good business relationship with Supplier

B. Marketing Effect

 

 

No

 

Questions

 

 

Very Disagree

 

 

Disagree

 

 

Neutral

 

 

Agree

 

 

Very Agree

1 Enterprises We use the E- Commerce in marketing activities. (Advertising and promotion)
2 Our online advertising has helped in increasing the overall sales
3 Online advertising can help us in finding new customers with good
4 E-commerce has helped us in building brand product / service
 

 

No

 

 

Questions

 

 

Very Disagree

 

 

Disagree

 

 

Neutral

 

 

Agree

 

 

Very Agree

5 Provision of information products / services are complete can provide added value for customers
6 E-commerce helps us in presenting products / services in a variety of attractive display
7 Customers feel more comfortable in seeking information on products / services on our website
8 With E-commerce will be relatively easier for customers to find our products / services

C. Performance

 

 

No

 

Questions

 

 

Very Disagree

 

 

Disagree

 

 

Neutral

 

 

Agree

 

 

Very Agree

1 E-commerce has helped our efforts in:

Improve sales

overall performance

2 Increase market share
3 Find and increase new customers
4 Increase the number of database customers
5 Enhance good relations with Customers
6 Improve customer satisfaction
7 Expand the number of services to customers
8 Reduce the cost of business
9 Preparation for entering the global market
 

No

 

Questions

 

Very Disagree

 

Disagree

 

Neutral

 

Agree

 

Very Agree

10 Improving the competitiveness of businesses
11 The main determinant of tactics to be able to compete with other competitors

Respondents Profile:

– What is your position in the company?

a. Sole proprietor        c. Manager

b. Owners Together        d. The sales manager

e. Etc

– How old are you?

  1. < 20 Years old
  2. 21 – 30 Years old
  3. 30 – 40 Years old
  4. > 40 Years old

– What is your gender?

a. Male   b. Female

– What is your highest education?

  1. Secondary School/High School  c. Master / Doctoral
  2. Diploma/Bachelor Degree d. Graduated from Foreign University

– The business you already have a website, blog, or social media accounts alone? *

Yes

photo

If no, please state:

Thank you for your participation

A. Validity Test Results

ANNEX 2: RESULT IF SPSS 18.0

A.1 Test Validity Operations Effect

  1. KMO
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .783
Bartlett’s Test of Sphericity Approx. Chi- Square 404.392
df 190
Sig. .000
  1. MSA
Anti-image Correlation Operations Effect VI .758
Operations Effect QI .722
Operations Effect IC .807
Operations Effect CI .754
Operations Effect 24 .745
Operations Effect SW .830
Operations Effect Comp .756
Operations Effect Comp .822
Operations Effect WPC .693
Operations Effect roc .771
Operations Effect roc .789
Operations Effect dr .808
Operations Effect dr .819
Operations Effect oa .872
Operations Effect ass .795
Operations Effect ins .671
Operations Effect ins .765
Operations Effect oop .730
Operations Effect ows .829
Operations Effect ows .897
  1. Factor Analysis
Component
1
Operations Effect VI .740
Operations Effect QI .803
Operations Effect IC .708
Operations Effect CI .549
Operations Effect 24 .536
Operations Effect SW .668
Operations Effect Comp .609
Operations Effect Comp .770
Operations Effect WPC .617
Operations Effect roc .721
Operations Effect roc .694
Operations Effect dr .825
Operations Effect dr .831
Operations Effect oa .767
Operations Effect ass .773
Operations Effect ins .703
Operations Effect ins .715
Operations Effect oop .723
Operations Effect ows .676
Operations Effect ows .824

 

 

 

 

 

 

A.2 Test Validity Marketing Effect

 

1. KMO

 

KMO and Bartlett’s Test

 

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .855
Bartlett’s Test of Sphericity Approx. Chi- Square 161.768
 

Df

 

28

Sig. .000

2. MSA

Anti-image Correlation Marketing Effect ONLA .823
Marketing Effect ONLA .784
Marketing Effect ONLA .839
Marketing Effect BR .845
Marketing Effect BR .867
Marketing Effect EPS .888
Marketing Effect EPS .889
Marketing Effect EPS .890

3. Factor Analysis

Component Matrixa

 

Component

1
Marketing Effect ONLA .715
Marketing Effect ONLA .754
Marketing Effect ONLA .833
Marketing Effect BR .846
Marketing Effect BR .734
Marketing Effect EPS .853
Marketing Effect EPS .850
Marketing Effect EPS .875

A.3 Performance Test Validity

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .819
Bartlett’s Test of Sphericity Approx. Chi- Square 304.968
Df 55
Sig. .000

2. MSA

Anti-image Correlation Performance SG .732
Performance SG .798
Performance CB .812
Performance CB .782
Performance Cs .896
Performance Cs .782
Performance Cs .766
Performance PE .899
Performance PE .851
Performance C Adv .881
Performance C Adv .820

 

 

  1. Factor Analysis
Component
1
Performance SG .745
Performance SG .807
Performance CB .801
Performance CB .750
Performance Cs .806
Performance Cs .812
Performance Cs .805
Performance PE .783
Performance PE .847
Performance C Adv .880
Performance C Adv .832

 

 

B. Test Reliability

B.1 Test Reliability Operations Effect

Reliability Statistics

 

 

 

 

Cronbach’s Alpha

 

Cronbach’s Alpha Based on     Standardized Items

 

 

 

 

N of Items

.948 .951 27

B.2 MarketingEffect Reliability test

Reliability Statistics

 

 

 

 

Cronbach’s Alpha

 

Cronbach’s Alpha Based on     Standardized Items

 

 

 

 

N of Items

.873 .885 10

 

B.3 Performance

Reliability Statistics

 

 

 

 

Cronbach’s Alpha

 

Cronbach’s Alpha Based on     Standardized Items

 

 

 

 

N of Items

.941 .946 11

 

 

 

 

 

 

 

C. The reliability test (after pretest)

C.1  The reliability test Operations Effect (after pretest)

Reliability Statistics

 

 

 

Cronbach’s Alpha

Cronbach’s Alpha Based on     Standardized Items  

 

 

N of Items

.948 .949 20

 

 

C.2 MarketingEffect Reliability test

Reliability Statistics

 

 

 

Cronbach’s Alpha

Cronbach’s Alpha Based on     Standardized Items  

 

 

N of Items

.923 .924 8

 

C.3 Performance Reliability test

Reliability Statistics

 

 

 

Cronbach’s Alpha

Cronbach’s Alpha Based on     Standardized Items  

 

 

N of Items

.941 .946 11

 

 

 

 

 

 

 

 

D. Classical Assumption Test

 

D.1 Test Multicollinearity (VIF)

 

Coefficientsa

Model Collinearity Statistics
Tolerance VIF
1 REGR

Operations Effect

.915 1.092
REGR

Marketing Effect

.912 1.096
Experience .931 1.074
Size .917 1.090

E. Test Linear Regression (Operations & Marketing Effect Effect)

Model Summary

Model  

 

R

 

R

Square

 

Adjusted R Square

Std. Error of the Estimate
1 .802 .642 .636 .60301464

 

ANOVAb

Model Sum of Squares  

df

Mean Square  

F

 

Sig.

1 Regression 76.456 2 38.228 105.129 .000
Residual 42.544 117 .364
Total 119.000 119

 

 

 

Coefficientsa

Model  

 

Unstandardized Coefficients

 

 

Standardized Coefficients

 

 

 

 

 

 

t

 

 

 

 

 

 

Sig.

 

B

Std. Error  

Beta

1 (Constant) .000 .055 .000 1.000
REGR

Operations Effect

.258 .057 .258 4.505 .000
REGR

Marketing Effect

.694 .057 .694 12.110 .000

 

  1. Moderate regression test (E-commerce experience)

 

Model Summary

Model  

R

 

R

Square

 

Adjusted R Square

Std. Error of the Estimate
1 .802 .643 .633 .60550937

 

Anovab

Model Sum of Squares  

df

Mean Square  

F

 

Sig.

1 Regression 76.470 3 25.490 69.523 .000
Residual 42.530 116 .367
Total 119.000 119

 

 

 

 

 

 

Coefficientsa

Model  

 

Unstandardized Coefficients

 

 

Standardized Coefficients

 

 

 

 

 

 

t

 

 

 

 

 

 

Sig.

 

B

Std. Error  

Beta

1 (Constant) .025 .139 .179 .859
REGR

Operations Effect

.258 .058 .258 4.490 .000
REGR

Marketing Effect

.695 .058 .695 12.034 .000
Experience -.015 .079 -.011 -.195 .846

 

2.   Model2

Model Summary

Model  

R

 

R

Square

Adjusted R

Square

Std. Error of the Estimate
1 .807 .651 .635 .60388557

 

ANOVAb

Model Sum of Squares  

df

Mean Square  

F

 

Sig.

1 Regression 77.427 5 15.485 42.463 .000
Residual 41.573 114 .365
Total 119.000 119

 

 

 

 

 

 

Coefficientsa

Model  

 

Unstandardized Coefficients

 

 

Standardized Coefficients

 

 

 

 

 

 

t

 

 

 

 

 

 

Sig.

 

B

Std. Error  

Beta

1 (Constant) .054 .141 .381 .704
REGR

Operations Effect

.467 .152 .467 3.080 .003
REGR

Marketing Effect

.782 .149 .782 5.238 .000
Experience -.028 .081 -.020 -.349 .728
Operating x Experience -.130 .088 -.224 -1.485 .140
 

Marketing x Experience

 

-.060

 

.092

 

-.096

 

-.645

 

.520

 

  1. Moderate Regression Test (Firm size)

 

  1. Model 1

 

Model Summary

Model  

 

R

 

R

Square

 

Adjusted R Square

Std. Error of the Estimate
1 .805 .648 .639 .60050241

 

 

 

 

 

ANOVAb

Model Sum of Squares  

df

Mean Square  

F

 

Sig.

1 Regression 77.170 3 25.723 71.334 .000
Residual 41.830 116 .361
Total 119.000 119

 

Coefficientsa

Model  

 

Unstandardized Coefficients

 

 

Standardized Coefficients

 

 

 

 

 

 

t

 

 

 

 

 

 

Sig.

 

B

Std. Error  

Beta

1 (Constant) -.215 .162 -1.324 .188
REGR

Operations Effect

.267 .057 .267 4.655 .000
REGR

Marketing Effect

.683 .058 .683 11.868 .000
Total Employees .180 .128 .078 1.407 .162

 

2.   Model2

Model Summary

Model  

 

R

 

R

Square

Adjusted R

Square

Std. Error of the Estimate
1 .812 .659 .644 .59624923

 

 

 

ANOVAb

Model Sum of Squares  

df

Mean Square  

F

 

Sig.

1 Regression 78.472 5 15.694 44.145 .000
Residual 40.528 114 .356
Total 119.000 119

 

 

 

Coefficientsa

Model  

 

Unstandardized Coefficients

 

 

Standardized Coefficients

 

 

 

 

 

 

t

 

 

 

 

 

 

Sig.

 

B

Std. Error  

Beta

1 (Constant) -.129 .167 -.770 .443
REGR

Operations Effect

.457 .154 .457 2.961 .004
REGR

Marketing Effect

.435 .186 .435 2.341 .021
Total Employees .095 .135 .041 .707 .481
Operating x Size -.153 .119 -.198 -1.288 .200
Marketing x Size .213 .155 .256 1.375 .172

 

Table 11 Validity and Reliability Test Results Pretesting Questionnaire (Continued)

Source: Processed by SPSS

 

 

 

 

Construct

 

 

Dimension

 

 

KMO

 

 

Conclusion

 

 

MSA

 

 

Conclusion

 

Factor Loading

 

 

Conclusion

 

Cronb- ach’s alpha

 

Conclusion

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Operations effect

OE 01  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.783

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Valid

0.758  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Valid

0.740  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Valid

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.948

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Reliable

OE 02 0.722 0.803
OE 03 0.807 0.708
OE 04 0.754 0.549
OE 05 0.745 0.536
OE 06 0.830 0.668
OE 07 0.756 0.609
OE 08 0.822 0.770
OE 09 0.693 0.617
OE 10 0.771 0.721
OE 11 0.789 0.694
OE 12 0.808 0.825
OE 13 0.819 0.831
OE 14 0.872 0.767
OE 15 0.795 0.773
OE 16 0.671 0.703
OE 17 0.765 0.715
OE 18 0.730 0.723
OE 19 0.829 0.676
OE 20 0.897 0.824

 

 

 

 

Construct

 

 

Dimension

 

 

KMO

 

Conclusion

 

 

MSA

 

Conclusion

 

Factor Loading

 

Conclusion

 

Cronb- ach’s alpha

 

Conclusion

 

 

 

 

 

 

 

Marketing effect

ME 01  

 

 

 

 

 

 

0.855

 

 

 

 

 

 

 

Valid

0.823  

 

 

 

 

 

 

Valid

0.715  

 

 

 

 

 

 

Valid

 

 

 

 

 

 

 

0.923

 

 

 

 

 

 

 

 

Reliable

ME 02 0.784 0.754
ME 03 0.839 0.833
ME 04 0.845 0.846
ME 05 0.867 0.734
ME 06 0.888 0.853
ME 07 0.889 0.850
ME 08 0.890 0.875
 

 

 

 

 

 

 

 

 

 

Performance

PR 01  

 

 

 

 

 

 

 

 

 

0.819

 

 

 

 

 

 

 

 

 

 

Valid

0.732  

 

 

 

 

 

 

 

 

 

Valid

0.745  

 

 

 

 

 

 

 

 

 

Valid

 

 

 

 

 

 

 

 

 

 

0.941

 

 

 

 

 

 

 

 

 

 

 

 

Reliable

PR 02 0.798 0.807
PR 03 0.812 0.801
PR 04 0.782 0.750
PR 05 0.896 0.806
PR 06 0.782 0.812
PR 07 0.766 0.805
PR 08 0.899 0.783
PR 09 0.851 0.847
PR 10 0.881 0.880
PR 11 0.82 0.832

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