Construction professionals will make decisions, big and small, and the quality of those decisions, based on multiple factors, will affect the successful implementation of the projects they govern. The aim of this study is to investigate the influencing factors in making decisions for construction professionals, and to propose a suitable model to help reduce these factors. To ascertain the influencing factors 12 construction professionals, participate in semi-structured interviews, consisting of 19 questions centred around the themes of validity of information, variety of resources, volume of resources, velocity of information and veracity of resources and information, the 5Vs. The results concluded that of the small sample captured, participants find that decision making is not a major issue in their respective fields. However, the findings indicate that only half of participants use regulatory body’s such as Health &Safety Executive as a source of information. Personal and team experience in making decisions are used in the majority of cases. Subsequently, strategic level participants focus on building relationships, tactical and operational level participants focus on solving a problem. The findings diverge away from the opinions of the lead researcher, but the participants interviewed welcomed the idea of the 5Vs model to help in future decisions
Keywords: construction industry, decision making, decision model, influencing factors.
People must make occupational decisions over their entire lifespan (Hartung, 2005 ) which are among the most powerful factors that influence people’s lives (Gail Hackett, 1995). The lead researcher observed the impact of decision making while working in the construction industry in London. This project therefore looks at decisions making in construction, its people and explores processes available to help mitigate this issue. Decision making was a problem on site as there were limited guidelines present to follow and limit impact on the project. This in turn would cause delays to schedule and lead to rework which can degrade the project performance (Palaneeswaran Ekambaram, 2014).
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Indecision is defined as a wavering between two or more possible courses of action : irresolution (Merriam-Webster, 2019) and has been seen as “an inability” to be “reduced” (Fre´de´ric Guay, 2003). All persons involved will face this inability if the guidelines for making decisions are not effective and accessible. For example, operatives have one port of call when faced with an issue i.e. their supervisor and if this supervisor cannot make a reasonable or immediate decision for the operative, they are left waiting on a decision from a higher authority. This can lead to frustration for the operatives as they are frequently faced with decisions and are disturbed from carrying out their tasks.
The researcher found there were processes when it came to decision making for personal on site, PROACT model and the 5 Whys technique. While some processes use different approaches, they take time and need careful consideration and deliberation of data. This is unsuitable for all personal on site. This project will look at factors that influence decision making and guidelines to follow to educate those involved in the industry to lessen their impact. The guidelines should consist of being able to eliminate the problem efficiently, effectively and build the correct environment for making the best decision at the time based on the available information.
There has been extensive research into decision-making processes, but little into why we don’t make decisions (Brooks, 2011). Much of the existing literature expresses the multiple factors that influence the decision-making process and including models to help deal with decision making (Ayeley. P.Tchangani, 2010). These factors are important to consider as they affect people’s decisions, and impact on their life. As the construction industry is a key factor in a countries economy (Craveiro, Duarte, Bartolo, & JorgeBartolo, 2019), it is important to find ways to help the sector overcome some of its shortcomings.
Much of the construction industry has best practice, when doing most tasks (Mishra, 2019). With making decisions it is no different, but can these best practices be improved by models used within other industries? The car industry for example, has a reputation of being widely production line and automation based. The components assembled in a relatively quick process, while the construction industry uses improvisation, emanating from unplanned, emergent situations, that later result in costly production and additional working hours. (Tetik, Peltokorpi, Seppänen, & Holmström, 2019) The car industry is on the forefront of innovation with new hybrid manufacturing platforms (Chen, Lau, & Tang, 2019) and the construction industry is characterised by high fragmentation with low productivity (Boton & Forgues, 2017), being low-tech and still relying on craft based methods (Craveiro, Duarte, Bartolo, & JorgeBartolo, 2019). The manufacturing industry uses additive manufacturing, add materials layer by layer to create parts (Yi, Gläßner, & C.Aurich, 2019) to help manufacturers gain more flexibility on both product and system level (Yi, Gläßner, & C.Aurich, 2019). So, can the use of this type of additive manufacturing be used in construction to help flexibility? In the paper “additive manufacturing as an enabling technology for digital construction: a perspective on construction 4.0”, (Craveiro, Duarte, Bartolo, & JorgeBartolo, 2019) they explore how additive manufacturing can be used as an enabling technology for construction. They use this model in the creation of 3D- printed structures and concluded that producing large non-supported structures is still a big challenge, but that additive manufacturing can positively impact the construction industry and empower construction productivity, quality, cost resource efficiency and promote collaboration (Craveiro, Duarte, Bartolo, & JorgeBartolo, 2019) which is one of the main success factors of a construction project (Boton & Forgues, 2017).
Looking at the processes these industries use, can we incorporate these tools into aspects of the construction industry to help make decisions? Concrete is one of the most common construction materials used all over the world (K.Alqahtani & Zafar, Characterization of processed lightweight aggregate and its effect on physical properties of concrete, 2019) and is a staple material to the industry, similarly bread is a staple material to the baking industry. With that in mind looking into the cooking process for bread might help the construction process with concrete in making good decisions. Both have parameters that need to be in place to ensure expected outcomes, have a shelf life and both have control parameters to ensure successful implementation, the control parameters such as temperature and time during “baking” is an engineering problem that is critical to this implementation (Manhiça, Lucas, & Richards, 2012). In the baking industry there is a no-wait constraint due to the fermentation process in dough production (T.Hecker, B.Hussein, OlivierPaquet-Durand, A.Hussein, & Becker, 2013), even though baking includes many steps such as mixing, moulding, baking and uses standard equipment like mixers, dividers, moulders, cutters/knives dough conditioners and ovens (I.SKotsianis, VGiannou, & Tzia, 2002) the end product relies on all these steps being brought together with efficiency, planning and decisiveness .The production planning is almost based completely on practical experience instead of the usage of mathematical methods like scheduling theory. (T.Hecker, B.Hussein, OlivierPaquet-Durand, A.Hussein, & Becker, 2013). (T.Hecker, B.Hussein, OlivierPaquet-Durand, A.Hussein, & Becker, 2013) used algorithms to solve optimization problems in time spans of less than 15 minutes and yielded significant cost function benefits compared to initial product sequence. The use of such algorithms in construction is a possibility and the use of experience-based methods can also have the required effect of optimization in concrete production.
In understanding that practices can benefit the construction industry, looking into what influences in those industries may also be of benefit. To look at what influence’s decision making we must examine the culture around decision making. Culture is the underlying system of values particular to a group or society that shapes the development of personality traits (Mueller & Thomas, 2001). Studies show that the Americans tend to focus on the tiger( foreground item) and the Chinese and Japanese focus on the jungle ( general context) (G.Martinsons & M.Davison, 2006), with this the Japanese prefer thorough, slower decisions (analytical) whereas Americans prefer recognition- based decision making (Behavioural) (Yates & Oliveira, 2016) because it is a means of expressing individualism (Yates & Oliveira, 2016). With that in mind take the study of factors influencing big data decision-making quality, by Marijn Janssen, Haikovan der Voort and Agung Wahyudi, proposing to find the factors influencing BD( big data) decision-making and what the business implications are with the decision (Voort:AgungWahyudi, 2017). They outline the quality of data, the processing of the data, the transfer of data and how they all influence the quality of the decision-making process. They find that variety, velocity, veracity, validity and volume of data all need to be in place to deal with the characteristics of BD. In summarising their findings: the development of effective contractual and relational governance mechanisms (Voort:AgungWahyudi, 2017) for the management of the BD chain is critical. So, in finding the influencing factors in big data decisions the Japanese would promote extensive planning in governance mechanisms due to their comfort with cognitive complexity (G.Martinsons & M.Davison, 2006) in thorough examination, and alternatively the Americans would promote communication with their peers and subordinates to promote suggestions and compromises. (G.Martinsons & M.Davison, 2006). Both are not wrong but there are two different approaches to the same problem, meaning whichever part of world you are in there is a difference in the focus towards making decisions.
Best practices and processes in decision making in other industries can be analysed and adapted to the construction industry to identify areas of decision making or improve the process of making decisions. While construction projects are bespoke, with a range of variables and differences in expertise required, the true cost of decisions are as yet unmeasured, leading to the conclusion that the creation of a tool kit, which utilises competent models from different industries to help members make decisions in construction is required.
Leading on from the research problem, which is the lack of research undertaken into the subject area of decisions for construction, the methods used to gather the information from subjects have to be specific because the area is subject to interpretation and biases. Due to this, a qualitive approach is adopted with the use of semi-structured interviews as well as a questionnaire survey. The use of semi-structured interviews, 20 minutes in duration, will encourage open conservation and discussion on relevant themes, using a questionnaire, at the time of the interview, to gather more information. The questionnaire will use predesigned questions to be of the same style to ensure that the results can be repeated and correlated against others. A scoring method with the use of Likert scales, option lists and rating lists will be used to gather the necessary data. The uses of such scoring methods will give rich information off the interviewee. The main themes evident in the questionnaire focus on Variety, Velocity, Veracity, Validity, Volume based on the literature of (Marijn, Haiko, & Agung, 2016) to determine what factors influence subjects when dealing with decision making. Interviews were conducted, with face to face meetings along with telephone calls. The interviewees were selected on their experience in the construction industry, both in Ireland and the UK, and their position in their respective companies. To this end the interviewees range from late twenties to late fifties, with occupations ranging from Senior site manager, regional manager, Architect, Project manager to Carpenter Foreman. All interviewees were given anonymity and ensured that all their data is confidential. Administrating the questions, from the questionnaire will be done by the lead researcher and due to the free flow of the interview any relevant information was transcribed. Given that the nature of the interviews is similar, the interviewees were given the same instruction in the same order to ensure data was comparable. The data was interpreted, by carefully studying the interviews and extracting the responses, based on the themes above.
Results and Analysis
The results were gathered from 12 participants with a range of managerial responsibilities, via 2 telephone calls and 10 face to face interviews. They include, Site Manager, Accountant Client Manager, Architect, Architectural Technologist, Building Survey, Architectural Technologist, Construction Director, Software Development Manager, Building Engineer, Architect. All candidates were asked to give their opinion around the topic of decision making. From these interviews a qualitative analysis of the data was undertaken. A total of 19 questions were asked using tick boxes, Likert scales and yes/ no questions. With that some respondents give several ticks for the same questions, where they deemed it necessary. There are reoccurring trends and themes throughout the data. Upon completion of the interview process the questions were listed with the responses for each participant tabulated.
Questions centred around validity focused on the participants view on relevance and accuracy of information of data for decision making. Collective responses to the question “do you tend to check the validity of the information you use”?(Question 7) Showed 50% of participants always check the information and stated that information on a piece of paper isn’t a valid source of information, it must be in formal writing or in an email and 41% stating they often check for legal reasons and only one participant stating they occasionally check the information they use. Analysis of (Question 6), “what the main sources of information are when making your decision”? all participants stated that they use their own experience and 91% said they use team experience as a source. Surprisingly only 50% of the candidates use a regulatory authority as a source of information.
Question on Variety asked the participants about the handling of decisions from different origins. In response to (Question 1), “how often do you make minor decisions/operational issues and major decisions/ critical issues”? 75% of participants said they make minor decisions often, with only 58% of participants agreeing the same response for major issues. Analysis of the question around the effectiveness of decision making in their area, "how would you rate the effectiveness of decision making in your area" (Question 5) showed that 58% find the level of focus is good in their area. This can be linked to (Question 4),"what is authority level bound by in your opinion" where 66% of participants said authority level is bounded by the classification of change. The data suggests decisions both minor and major can be effectively handled so long as the classification of change does not exceed their own authority level.
The volume question highlights the impact decisions have on the metal capacity participants. In (Question 17), “how often is a reversal of a decision made”? 41% said that decisions are rarely reversed, however when asked how this reversal impacts you? (Question 18), 33% of participants responded with a negative impact on themselves. This linked with (Question 19), “how does this reversal impact the project” 66% of participants showed it has a negative impact on the project. The data suggests that if decisions are reversed the project will be more negatively affected than the people in the project.
Velocity questions focus on the speed of decision making. When asked the question (Question 8), “how do you view the speed of decision making”? 58% agreed that both minor and major decision are made at about the right speed, as to ensure the decisions made keep people safe, do consider and review all the alternatives possible to achieve the best outcome. This with the fact that 58%, (Question 14)," do you have a toolkit/ guideline to follow for making decisions" do not have a model to follow for making decision, presents the opportunity of a model, like the 5Vs, to decrease the % of participants who feel decisions are made too slow or too fast.
The veracity questions were centred around conformity to fact of information and resources for decision making. The participants were asked the question, “what are the barriers/ drivers to group decision making”? (Question 11), 91% stating that different priorities are barriers to group decisions and 83% stated that good relationships drive group decisions. This linked with (Question 4), “what are the most important factors in making your decision”? Showing that solving a problem was the most important factor with 58% of participants scoring it 1, being the most important and 6 the least important. With further analysis of this question breaking the participants occupations into strategic, tactical operational levels, the results change. The data suggests that strategic level members (participants #B, #C, #D, #F, #H, #K) have an average mean score of 2.5 for solving a problem, when tactical and operational level participates(#A, #E, #G, #I, #J, #L) score it 1.8. Tactical and operational level participants have more focus on solving a problem than the strategic levels. The opposite is true for building relationships, strategic level participants score it 2.8 while tactical and operational level participant score it 4.3. Strategic members have more focus on building relationships compared to tactical and operational level participants.
Upon completion of the data gathering process and analysis, the issue of why making decisions is difficult, is not a major issue with regard to the candidates interviewed. This is contrary to the views of the researcher and diverges away from the findings of other papers. While there is variability in terms of the factors influencing the decision-making process when highlighting the respondent’s levels in their organisations, strategic, tactical and operational members respectively, the general consensus is that decisions are handled fairly well and both minor and major decisions are made at the right speed for the project. This could be linked to the fact that all candidates were from Irish and English backgrounds and the decision-making culture is similar in both. While the majority of respondents indicated there was no formal methodology or toolkit for the decision-making process on site, the presence of one would be useful for future projects. The questions asked were centred around the 5 V’s, validity, variety, volume, velocity, veracity and the objective was to gain insight into thinking of the interviewee. Key elements can be identified from the interview process these are, decision-making is a skill to possess, bringing experience and knowledge into the decision-making processes is vital, the job progresses at the rate it needs to, the review of information is as important as the decision and simplified presentation of information can improve the % of successful decisions. The data collected shows there are issues around accountability, responsibility, trust and resistance, in particular regard to using new methods for decision making. The proposed model hopes to resolve these issues.
The model breaks down the decision into four steps. Step one, acquisition of information, Validity and variety, are your sources for the decision factually sound and do they make sense, is the decision similar to previous decisions. Step 2, resource evaluation, Volume and velocity, do you have the required resources to complete the task, will it disrupt the project. Step 3 outcome conformity, veracity, are the previous steps correct and accurate and will they give a fair result and Step 4 making your decision. The proposed model will benefit the user in making their decisions in these areas and will lessen the impact of the influencing factors that affect them. The model needs to be inclusive, so that all members of the organisation will participate in the steps. Being Consistent will ensure that the same procedures are followed throughout the process making it easier to follow and train all members in the organisation and allow for accurate review of model performance. Due to the step process of the model there is an inherent assistant supported process, linked with traceability and justification for the decision made. This has great benefit for an audit trail and knowledge management process in review of the organisational performance. As per the findings of the report the model has the protentional of being a valuable toolkit for the decision making in the construction industry.
In conclusion the data did not show evidence of major concerns with regard to decision making within the sample of construction workers interviewed. The methods used to gather the information were specific to reduce wrong interpretation and biases. A qualitive analysis of the data was used. A total of 19 questions were asked to participants about decision making, based around the concept of the 5V model.
As part of the research other industries were examined with regard to the factors influencing decision making. Elements from the additive manufacturing industry, personal experience and the behavioural and analytical cultures of different countries, led to the proposal of a potential model. This model centres around validity of information and its relevance, variety of resources from different origins, volume of resources and its impact, velocity of information and veracity of resources and information and their conformity to fact, the 5Vs.
The results showed that decision making is handled to a satisfactory standard. Participants use personal and team experience to navigate decision making. Strategic level participants interviewed focused on building relationships while operational and tactical participants focused on solving a problem. Interestingly the main sources of information are not from the regulatory authorities such as the Health &Safety Executive, but from personal and team experience in making decisions. The Data showed that there is no major issue with decision making within their various fields. However, 83% of participants would opt to avail of a specific toolkit such as the 5V model to aid with both major and minor decisions.
On reflection the use of more Likert scales would generate a better picture of the candidate’s preferences and would allow for faster analysis of the data. This pilot study was completed on a small sample size of 12 people, further work built on the results from this study with a larger sample size would give a better insight.
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