Shareholder Satisfaction And Relationship With Stakeholders Commerce Essay

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Matzler et al (2005) studied the relationship between customer satisfaction and shareholder value. The study concluded that that there is a positive relationship between customer satisfaction and shareholder value; (2) that the strongest relationship between customer satisfaction and shareholder value is three quarters after the measurement of customer satisfaction; (3) that the strength of the relationship is not affected by turbulences on financial markets; and (4) that it is reasonable to assume that there exists an optimal level of customer satisfaction. As a result, customer satisfaction is a strong vehicle to increase shareholder value. However, from a managerial point of view, one important question is still not answered: how many resources should be devoted to increasing customer satisfaction? Although their analysis indicates that satisfaction ratings above a certain threshold destroy value as they require too many resources, the results have to be interpreted very cautiously. This question cannot be addressed on an aggregate or an industry level, but rather on firm level. To find a reliable answer to this question much research is clearly needed.

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On a related study, Hallowell (1996) relationships among customer satisfaction, customer loyalty, and profitability warrant further research. Researchers may benefit from avoiding three of the difficulties encountered in this paper. First, to the degree possible they should work with an organization to develop exemplary measurement systems before measuring satisfaction, loyalty, and profitability. This should benefit both the organization and the research. Second, they may want to focus on industries other than banking, both for the purpose of extending external validity and to examine whether variance explained will dramatically increase for data sets from industries in which profitability can be expected to be more immediately tied to customer satisfaction (unlike commercial banking, which, as noted, may be subject to variation in profitability due to non-customer satisfaction-related activities, such as treasury functions). Finally, researchers may want to focus on data collected in relatively stable industries over an extended period. The analysis of such data may enable conclusions to be drawn about both relationships among variables and causality.

An important caveat must be made regarding the findings of this study. A reader might infer from the conclusion that since customer satisfaction is related to profit, a bank should endeavour to satisfy every customer. This could be an error in interpretation. A bank's population of customers undoubtedly contains individuals who either cannot be satisfied, given the service levels and pricing the bank is capable of offering, or will never be profitable, given their banking activity (their use of resources relative to the revenue they supply). Any bank would be wise to target and serve only those customers whose needs it can meet better than its competitors in a profitable manner. These are the customers who are most likely to remain with that bank for long periods, which will purchase multiple products and services, who will recommend the bank to their friends and relations, and who may be the source of superior returns to the bank's shareholders.

Part II - Data Assessment

Hypothesis

The drawn hypothesis for the situation is that the firm's performance is affected by its supplier relationship through strategic purchasing.

Analysis and Findings

The collected data from 119 firms through survey questionnaire is entered in SPSS for analysis. It undergone Cronbach's Alpha Reliability, Factor Analysis and one way Analysis of Variance (ANOVA). The results obtained form the mentioned tests will be discussed in the following sections of the paper:

Cronbach's Alpha

Normally, the Cronbach's Alpha reliability coefficient ranges between 0-1. However, there is no actual limit for the coefficient. The closer Cronbach's alpha coefficient is to 1.0 the greater the internal consistency of the items in the scale. Based upon the formula _ = rk / [1 + (k -1)r] where k is the number of items considered and r is the mean of the inter-item correlations the size of alpha is determined by both the number of items in the scale and the mean inter-item correlations. George and Mallery (2003) provide the following rules of thumb: "_ > .9 - Excellent, _ > .8 - Good, _ > .7 - Acceptable, _ > .6 - Questionable, _ > .5 - Poor, and_ < .5 - Unacceptable".

In the case of the obtained data, the reliability coefficient is .887 which indicates good reliability coefficient. While increasing the value of alpha is partially dependent upon the number of items in the scale, it should be noted that this has diminishing returns. It should also be noted that an alpha of .8 is probably a reasonable goal. It should also be noted that while a high value for Cronbach's alpha indicates good internal consistency of the items in the scale, it does not mean that the scale is one-dimensional. The dimensionality of the scale can be computed using the factor analysis which will be discussed in the next section.

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Factor Analysis

The method followed here was to first examine the initial responses of the participants with a view to selecting a subset of characteristics that might influence further responses. Then, survey responses were analysed at the item level, using figures, tables, or text alone, to provide a first impression.

These item level responses were scrutinised for underlying patterns via factor analytic procedures (Note that all procedures reported here utilise SPSS). A prerequisite for including an item was that responses were not too badly skewed (i.e., 90% or more of responses clustered in single cell) and that more generally, the level of response to that item was not insufficient (<15-20%) to destabilise analysis. The factors identified in this fashion correspond to the primary topics or latent variables to which correspondents seem to be responding in terms of various related items.

The protocol adopted here for factor analysis was to use default settings initially (Principal Axis Factor - PAF) and to rotate the matrix of loadings to obtain orthogonal (independent) factors (Varimax rotation). The prime goal of factor analysis is to identity simple (items loadings >0.30 on only one factor) that are interpretable, assuming that items are factorable (The Kaiser-Meyer-Olkin measure of sampling adequacy tests whether the partial correlations among variables are small. Bartlett's test of sphericity tests whether the correlation matrix is an identity matrix, indicating that the factor model is inappropriate).

Once clearly defined and interpretable factors had been identified (Factor loadings =>.10 were illustrated via an included table even though only item loadings >0.30 were considered relevant to factor loadings), and responses related to these factors were saved in the form of factor scores. These Bartlett factor scores are equivalent to sub-scale or scale scores with means of zero and standard deviations of one (z-scores), and with participants credited with separate scores in relation to each identified factor.

A Principal Axis Factor (PAF) with a Varimax (orthogonal) rotation of 22 of the 24 Likert scale questions from this survey questionnaire was conducted on data gathered from 119participants. An examination of the Kaiser-Meyer Olkin measure of sampling adequacy suggested that the sample was factorable (KMO=.698).

One-Way ANOVA

One - way Analysis of Variance (ANOVA) is done with the dependent variable which is the firm's performance and this is in terms of financial, market, customer, and technological, competitive and strategic terms as compared with the independent variable which is strategic purchasing. The results are discussed in the following:

Question 4.1 - The purchasing function in my firm has a formally written long-range plan (for example, 5-10 years)

Based on the performed ANOVA, firms that have formally written range plans in performing the purchasing function has .097 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .045, .539, .020,.069 and .001 significance respectively.

Question 4.2 - Purchasing's long-range plan is reviewed and adjusted to match changes in the company's strategic plans on a regular basis

Based on the performed ANOVA, firms that review and adjust their purchasing long range plan so that it can match their company's strategic plans has .235 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .015,.676, .158,.037 and .001 significance respectively.

Question 4.3 - Purchasing's long-range plan includes the various types of relationships to be established with key suppliers

Based on the performed ANOVA, firms that purchasing's long range plan include various types of relationships to be established with key suppliers has .235 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .015,.676, .158,.037 and .001 significance respectively.

Question 4.5 - "We work closely with managers from other functions and from high corporate levels to produce the long-range purchasing plan for our firm."

Based on the performed ANOVA, firms that work closely with other function's managers and from high corporate level in order to produce long range purchasing plan for their firm has .376 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .089, .405, .741, .177 and .039 significance respectively.

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Question 5.1 - "we believe that the relationships we have with our key suppliers is one of our most important assets".

Based on the performed ANOVA, firms that believe that their relationships with their key supplier is one of their most important assets has .127 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .097, .815, .274, .074 and .039 significance respectively.

Question 5.2 - "we believe that strong relationships with our key suppliers are critical to our long-term success."

Based on the performed ANOVA, firms that believe that their relationships with their key supplier is critical to their success has .709 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .060, .067, .015, .005 and .492 significance respectively.

Question 5.3 - "we view our key suppliers as important as our employees and customers."

Based on the performed ANOVA, firms that believe that key suppliers are as important as their employees and customers has .074 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .704, .320, .036, .069 and .085 significance respectively.

Question 5.4 - "we believe that the strategic goals of our firm and key suppliers are co-operatively related".

Based on the performed ANOVA, firms that believe that the strategic tools of their firms and their key suppliers are related has .433 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .025, .449, .046, .061 and .284 significance respectively.

Question 5.5 - "we care about our key suppliers"

Based on the performed ANOVA, firms that care about their supplier have .764 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .522, .151, .002, .002 and .001 significance respectively.

Question 5.6 - "we have frequent face-to-face planning communication with our key suppliers."

Based on the performed ANOVA, firms that have frequent face to face planning communication with their supplier have .289 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .800, .495, .021, .039 and .052 significance respectively.

Question 5.7 - "we follow through on promises we make to our key suppliers"

Based on the performed ANOVA, firms that follow through on the promises they make on their key suppliers have .111 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .017, .034, .298, .231 and .057 significance respectively.

Question 5.8- "we try to develop a good understanding of the key suppliers' business."

Based on the performed ANOVA, firms that are trying to develop a good understanding of the key suppliers' business have .365 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .075, .349, .390, .954 and .443 significance respectively.

Question 5.9- "we resolve problems with our key suppliers through dialogue rather than pressure".

Based on the performed ANOVA, firms that resolve their problems with their key suppliers by means of dialogue rather than pressure have .683 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .401, .329, .043, .006 and .026 significance respectively.

Question 5.10 - "we listen carefully to the suggestions made by our key suppliers."

Based on the performed ANOVA, firms that listen carefully to the suggestions made by their key suppliers have .312 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .108, .790, .470, .087 and .215 significance respectively.

Question 5.11 - "We spend considerable time, effort and money to develop strong relationships with our key suppliers."

Based on the performed ANOVA, firms that spend considerable time, effort and money in developing strong relationships with their key supplier have .019 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .024, .185, .064, .039 and .022 significance respectively.

Question 5.12 - "when a key supplier gives us sensitive information we keep it confidential."

Based on the performed ANOVA, firms that keep sensitive information confidential when given by the supplier have .071 significance in terms of financial terms. On the other hand, in terms of market terms, customer terms, technological terms, competitive terms, and strategic terms, the SPSS results showed .070, .903, .056, .083 and .004 significance respectively.

Conclusion

From the interim results it is possible to conclude that purchasing practices individually and collectively influence a broad set of performance measures. It seems that companies have to go through a holistic evolution of PSM capabilities rather than implementing singular best practices. Due to the low number of only 119 data sets considered in this evaluation, it has not been possible to show causal effects or quantify the impact of individual purchasing practice dimensions on performance at the current stage. Nevertheless, the findings already have important implications especially for further research but also for practice.

Higher average purchasing practice scores significantly correlate with performance across industries along several performances measurement concepts. 29 out of the 43 individual purchasing practice dimensions correlate significantly with the meta performance measure "average" for only 119 data sets already. This supports the assumption that at least most of the purchasing practice dimensions measure management practices positively influencing PSM and company performance. This finding proves a direct relationship between good purchasing practices and excellent company performance. It is in line with many of the other empirical studies in this area (to name a few: Tan et al. (1998); Goh et al. (1999); D'Avanzo et al. (2003).

Nevertheless, to prove all of the purchasing practice items, more data points have to be collected going forward. The correlations between average purchasing practice scores and both objective (average annual reduction of COGS, annual PSM cost reductions) and subjective performance measures (self-assessment of the CPO, assessment by industry peers) show that PSM practices correlate with a broad set of outcome measures. However, when correlating individual dimensions against the different performance measures, two things become obvious: First, from a research perspective, the results of an empirical study on the impact of purchasing practices on performance heavily depend on the choice of the performance measure(s). That is why results from studies focusing on only one performance measure could lead to wrong or at least incomplete conclusions. Second, from a practical perspective, a focus on one PSM performance measure only would inevitably limit the scope of PSM transformation efforts. This is in line with the findings of Carter et al. (2005) who also state the necessity of a comprehensive performance measurement system.

The significantly lower variance of PSM practice scores within companies than between companies indicates that PSM organizations have to go through a holistic and consistent evolution to step up from a pure support functions to strategic supply management (Kraljic, 1983; Jahns, 2005). This is in line with the findings from Das and Narasimhan (2000) who prove the existence of the latent variable purchasing competence. It seems that implementation of best practices in certain areas cannot result in maximum impact unless the practices are in line with overall organizational capabilities. To ultimately prove this hypothesis and quantify the relations between the hypothesized latent variables "PSM evolution level" and "PSM performance", structural equation modeling will have to be used when evaluating the final data set. Yet the findings presented here can serve as a first indication.

Base on the study, the influence of four purchasing strategies (Effective Negotiation, Collaborative relationship and interaction, Effective Cost Management and Supply Base Management) are importance in creating positive impact on manufacturing performance competitive priorities consists of cost reduction, quality improvement, cycle time reduction, new product introduction timeline, delivery speed and dependability plus customization responsiveness.. In an increasing competitive business environment, purchasing strategic integration role as moderator is crucial in creating alignment between purchasing strategies and manufacturing performance in order to ensure purchasing function stayed in tag with strategic planning process and objective.

The knowledge of the purchasing competence dimensions could help managers in two ways: (1) they can use it as a diagnosis tool of their strategic purchasing level (strategic or not), and (2) they can use the underlying variables as key factors to improve their purchasing alignment with business goals and plans. There are clear benefits associated with elevating the purchasing function to a strategic function, for example at the level of new product and service development, cost reduction, and key suppliers strategic alliances.

As future work, it is intended to implement a cross-industry survey to validate our dimensions of purchasing competence. It is also intended to study the impact of purchasing competence on organization success, integrating it in a more comprehensive model that acknowledges a network environment - business partners. At a first stage, our research will comprehend a definition of a structural equation model concerning the following set of three hypotheses: (1) increased purchasing competence has a positive effect on firm's performance, (2) increased purchasing competence has a positive effect on firm's innovativeness and (3) increased market orientation has a positive effect on firm's purchasing competence.