Faculty of Economics and business
MSc Thesis Supply Chain Management
Thesis
The unknown influencing the decisions of managers
during a disruption, a closer look at the effects of
COVID-19
25-01-2021
ELENA BAKKER
Student number: 2905167
Email:
e.bakker.15@student.rug.nl
ABSTRACT
The supply chain is often influenced by minor disruptions. Currently, the SC managers have to deal with a major disruption (e.g. COVID-19) influencing the supply chain on a global scale. Previous literature focuses mainly on the organizational response of the company on a supply chain disruption. This paper focuses on the individuals influence on the decision-making process. Using the Behavioral Theory of the Firm the influence of bounded rationality and aspiration levels are researched. Furthermore, this study uses a vignette in order to examine the choices the participants make in different Supply Chain Disruptions. We find that although aspiration discrepancy and risk-taking propensity do affect risk behavior, this is not necessarily influenced by the severity of Supply Chain Disruptions.
List of Abbreviations:
SC: supply chain
TABLE OF CONTEXT
ABSTRACT ... 2
INTRODUCTION ... 4
LITERATURE BACKGROUND ... 6
2.1 Disruptions ... 6
2.2 The behavioral theory of the firm (BTOF) ... 7
2.3 Risks ... 9
HYPOTHESES DEVELOPMENT ... 10
3.1 Aspiration level ... 10
3.2 Disruptions and risk behavior ... 11
3.3 Risk-taking propensity ... 11
METHODOLOGY ... 13
4.1 Experimental design ... 13
4.2 Sample ... 14
4.3 Treatment checks and Measurements ... 15
4.3.1 Dependent variable ... 15
4.3.2 Independent variables and moderator ... 16
4.3.3 Control variables ... 17
4.3.4 Manipulation and unidimensionality check ... 18
4.3.5 Multicollinearity check ... 19
4.3.5 Reliability of the study ... 20
FINDINGS ... 20
5.1 Aspiration discrepancy ... 20
5.2 Disruptions and risk-taking propensity ... 22
DISCUSSION AND CONCLUSION ... 24
6.1 Theoretical implications ... 24
6.2 Managerial implications ... 26
6.3 Future research and limitations ... 26
6.4 Conclusion ... 28
REFERENCES ... 29
Papers ... 29
Websites ... 32
APPENDIX ... 34
Appendix A: Company introduction ... 34
Appendix B: Minor disruption ... 35
Appendix C: Major disruption ... 35
Appendix D: No disruption ... 36
INTRODUCTION
In December 2019, the first news reports on COVID-19 were published (WHO, 2020). Currently still on going, COVID-19 proved to be one of the biggest current disruptions affecting companies on a global scale. During COVID-19, a number of companies experienced a greater demand of products, while the supply of materials has depleted making it difficult to meet the changed demand of products (Paul & Chowdhury, 2020). At the same time some companies experienced a weakened demand (Zanni, 2020). On top of that, the ability to ship and receive products on time was negatively affected due to shortages and bottlenecks in the logistic process (Zanni, 2020).
An example of a negative consequence of COVID-19 are the export bans which were put into place mainly for medical and protective equipment. Multiple countries (such as China) banned the export of masks, test kits and other medical supplies to other countries (Macmap, 2019). Additionally, people were forced to work from home due to government restrictions, resulting in departments and production sites having to scale- or close down (McKinsey, 2019). Around 94% of Fortune 1000 companies experienced supply chain disruptions due to COVID-19 (Sherman, 2020). Moreover, 75% of companies stated they are seeing negative or strongly negative impacts on their business’ performance during the pandemic (Accenture, 2020).
The supply chain managers are responsible for the supply chain decisions during a disruption in order to mitigate the impact (Ambulkar et al., 2016). The individual (e.g. manager) is an important factor when discussing disruptions and supply chain risks, since they have a frontline position when dealing with a disruption (DuHadway et al., 2018). Though most research regarding the decision-making process and risk management during a disruption is focused on the organizational level of decision making and not on the individual level of decision making (DuHadway et al., 2018). It is thus important to understand the behavior and preferences which influence the decision of an individual during the decision-making process. This paper will look at the influence of the individual on the decision-making process during a disruption through the lens of the behavioral theory of the firm (BTOF). To better understand how an individual responds to disruptions which differ in severity, with the main focus on the influence of the individual’s perception on risks and firm performance. The manager is, for instance, influenced by the aspiration levels of the firm, and how he perceive risks in uncertain and ambiguous situations such as a SC disruption ( Cyert & March, 1963; DuHadway et al., 2018).
refer to the minimum desired output of the firm based on the previous performance of the firm and competitors (Cyert & March, 1961). Aspiration levels of the individual are based on the previous performance of the firm and the individuals’ goals, acting as a reference point during the decision-making process. Consequently, influencing the level of risk a SC manager is willing to take during a disruption (Cyert & March, 1961; Kahneman & Tversky, 1979). Furthermore, the role of the managers cognition and its influence on decision making is analyzed in this paper. Gan et al., (2004) state that most research in the SCM field consider the managers as risk neutral, meaning that the degree of risk does not affect the decision of the SC manager. However other management fields (e.g. strategic management) do research the effects of risk prone/averse perception of managers and the influence this has on the decision making of managers. Both theories will be combined and analyzed in relation to SC disruptions.
By answering the following research question both the BTOF theory and theory on risk taking are combined in relation to SC disruptions. Allowing us to understand recent decisions of the individual SC manager during the COVID-19 pandemic and the influence of human cognition and perceptions on the decision-making process.
RQ: How do the risk perception and aspiration levels of Supply Chain Managers influence their risk-taking behavior during a different levels of supply chain disruption.
This research will conduct a scenario-based experiment to see how managers influenced by their bounded rationality react in different scenario’s based on disruptions. The research is quantitative in nature as the results of the surveys will be analyzed in a statistical manner. This study will contribute to current literature by extending the Behavioral Theory of the Firm developed by March and Cyert (1961). Analyzing how the aspiration levels, influenced by disruptions, affect risk behavior. In addition, the influence of risk perception on risk behavior, based on bounded rationality, during a disruption is analyzed. By taking the BTOF perspective on SCM disruptions we can better explain the influence of individual characteristics and perceptions, and how this influences the decision-making process during a disruption. Finding that overall a disruption does not increase the risk taken during the decision-making process.
LITERATURE BACKGROUND
This section consists of three parts. First, supply chain disruptions will be discussed, a difference will be made between minor and major disruptions. Second, the behavioral theory of the firm will be analyzed. Last, the concept of risk and risk taking will be reviewed.
2.1 Disruptions
Supply chain disruptions are unforeseen events which interrupt the flow of goods and materials in the supply chain, causing delay for consumers and loss of profit for the affected companies (Bradley, 2014; Craighead et al., 2007). A disruption for one company in the supply chain can negatively influence all the other organizations within the chain (Craighead et al., 2007). Disruptions can be caused by operational risks (e.g. machine breakdowns, hacked systems), natural disasters (e.g. pandemics, earthquakes, tsunamis), terrorism and political instability (Ivanov, 2020; Wakolbinger & Cruz, 2011).
In this paper we will consider two types of disruptions, minor and major disruptions, as discussed in the paper of Bradley (2014). The minor disruptions will include machine breakdowns, late deliveries and short-lived absenteeism and have more frequent occurrence compared to major disruptions (Bradley, 2014). For major disruptions we will in particular consider pandemics (e.g., COVID-19). COVID-19 is currently still ongoing, and was entirely unanticipated by the world but has since had a drastic effect on people not just on a personal level but on a global economic scale (Foss, 2020). In this paper a major disruption will be considered more severe than a minor disruption. Meaning that the major disruption can have a more severe and disastrous financial impact on the supply chain network compared to a minor disruption (Craighead et al., 2007).
Since the situation during a disruption is often uncertain and ambiguous, the decisions in the situation are more novel, complex and open-ended and therefore cause more risk for the firm (Foss, 2020). Most of the current research on supply chain disruptions looks at the decisions and risk-management at an organizational level ((Ellis et al., 2010). Furthermore, most research uses the resource-based theory, transaction cost theory, positioning view, or enactment theory to discuss the impact and reaction on supply chain disruptions (Bode et al., 2011; Ellis et al., 2010, 2011; Foss, 2020; Paul & Chowdhury, 2020). Rather this paper will look at disruptions through the lens of the Behavioral Theory of the Firm focusing on decision-making on an individual level.
2.2 The behavioral theory of the firm (BTOF)
The behavioral theory of the firm was developed in 1963 by Cyert and March and challenged the challenged the basic assumptions of the theory of the firm. The theory of the firm assumes that firms always seek to maximize their profit and that the firms operate with perfect knowledge. Whereas BTOF states profit maximization is not always the main goal of the firm, and individuals deal with bounded rationality and therefore the firm does not operate with perfect knowledge. Here, bounded rationality means that decisions are influenced by the limitations of the information available to the decision maker and the available time to make the decision.
Cyert and March (1963) stated that in order to understand economic decision-making, research also needs to take the organizational structure of the firm, the development of goals, the formation of expectations, and the process of decision-making into consideration (p:8). In the BTOF theory they focus on two concepts: bounded rationality and aspiration levels. Aspiration levels refer to the minimum desired output of the firm of an individual, and is based on the previous performance of the firm and the competitors of the firm (Cyert & March, 1961). Here, bounded rationality means that decisions are influenced by the limitations of the information available to the decision maker and the available time to make the decision. This paper will focus on the influence of aspiration levels on the decision-making process of an SC manager as discussed by Cyert and March (1963). In addition to the influence of bounded rationality on risk perception.
and information (Cyert & March, 1963; Ketokivi & Schroeder, 2004; Shinkle, 2012). Satisficing here means that the decisionmaker is will not necessarily choose the best solution possible but rather one that meets the goals) or is considered ‘good enough’ (I.e., aspiration levels (Ketokivi & Schroeder, 2004; Shinkle, 2012).
Schneider (1992) states that an aspiration level is the smallest outcome (in terms of performance) that would be seen as satisfying by the decision maker during the decision-making process. The aspiration levels of managers are based on the previous performance of the firm (historical aspiration level) or of similar competing organizations (social aspiration level) (Greve, 2003). The current performance of the firm is then evaluated against the aspiration level (I.e. minimum desired performance of the firm) of the decision maker (Greve, 2003). During the early stages of the decision-making process, the individuals’ aspiration levels are the main reference point (Hoffmann et al., 2013). When the current performance falls below the aspirational levels of the mangers a problematic search is triggered. This results in either finding a solution which satisfies the aspiration levels or in revising the goals and subsequently the aspiration levels (Cyert & March, 1963; O’Brien & David, 2014). Shinkle (2012) summarizes the main key of the behavioral theory of the firm as; “a core premise of behavioral
theory is that past performance shapes strategic behavior, which in turn influences future performance” (p:4).
2.3 Risks
When looking at the risks affecting a supply chain, two broad categories can be distinguished. First is the risks which emerge from problems with coordinating the supply and demand. Second, are risks emerging from disruptions in the supply chain (Kleindorfer & Saad, 2005). In this paper the focus will be on the second category, risks emerging from disruptions.
Risk is often defined as the difference in the distribution of possible results, their probability, and their subjective values (March & Shapira, 1987). Several papers characterize supply chain risk as the uncertainty of information, meaning that there is more information needed in order to diminish the risk (Ambulkar et al., 2016; Bode et al., 2011; Wakolbinger & Cruz, 2011). The supply chain risks will become more severe when the uncertainty of information increases. This concept relates back to the bounded rationality discussed by the BTOF. The decisions made are influenced by the limitations of information available to the individual (Cyert & March, 1963).
In behavioral literature the supply chain risks are not discussed on an organizational level, but on the individual (e.g. managers) level (Ellis et al., 2010). Managers look at risks differently, as they are influenced by their own perceptions and preferences (March & Shapira, 1987). Ellis et al., (2010) define the supply chain risk from the individual’s perspective as: “an
individual’s perception of the total potential loss associated with the disruption of supply of a particular purchased item from a particular supplier” (p:36).
Current management literature uses different levels when discussing the influence of risk on the individual’s decision making namely, risk neutral, risk averse, and risk prone. In most SCM literature a manager is considered risk neutral, meaning that they would always want to maximize their expected profit (Gan et al., 2004). However other management streams (e.g., strategic management) discuss the possibilities of managers having a risk averse or risk prone preference and the influence of this on the decision-making process. When a person is risk averse they will prefer a certain profit over a possible higher but more risky profit (Gan et al., 2004). Furthermore, a manager is risk prone when they would be willing to take the option of higher profit and more risk. (Wolf & Houlihan, 2018).
HYPOTHESES DEVELOPMENT
Based on the literature reviewed above, we now develop hypotheses. In which we link the BTOF to risk behavior and risk perception. The first hypothesis is based on the aspiration discrepancy of the SC manager. The second hypothesis is based on the different severities of disruptions. Lastly, the third hypothesis is based on the risk propensity of the SC manager.
3.1 Aspiration level
The aspiration levels of the firm influence the decision-making process of the manager during a disruption (Cyert & March, 1963). As stated before, when the performance of the firm falls below the aspiration levels of the individual, a problematic search is triggered (Cyert & March, 1963). When the performance of the firm falls under the expected performance, the managerial tolerance for risk will increase (Kahneman & Tversky, 1979). This will lead to experimentation because the SC manager is willing to take on more risks and therefore new ways to do things and thus taking on more risk during the decision-making process (Baum & Dahlin, 2007). Thus when the performance is below the aspiration levels of the individual the willingness of managers to take on risky decisions will increase in order to reach the desired aspiration level (Cyert & March, 1961; Kahneman & Tversky, 1979).
The opposite is also the case, when the firm is still performing within the desired aspiration levels managers are less likely to take on more risks in order to increase performance. Furthermore, when the performance increases the willingness to take on risks falls, however this effect is weaker when the performance is still under the aspiration level (Greve, 2003). An individual should take less risk when things are going well in the firm, furthermore it is proposed that a riskier choice will be made when the firm is not performing well (MacCrimmon & Wehrung, 1986, as cited in March & Shapira, 1987). During a major disruption the performance of the firm often falls significantly as it affects both the supply and demand of the firm. This means that the performance will be below the aspiration levels set by the SC manager during the decision-making process. Therefore, we assume that the individual will show risk prone behavior during the decision-making process when the aspiration levels are high, and the performance is declining during a major disruption. Thus, trying to increase the performance of the firm, and meet the aspiration level, by taking on a riskier choice.
3.2 Disruptions and risk behavior
Ellis et al., (2011) state that the individual’s perception of risk instead of the actual risk influences the decision-making process of the manager (p:66). The manager will increase the degree of risk taken in supply chain strategies when they perceive a lower level of risk (DuHadway et al., 2018). Furthermore, when the individual recognizes the situation or feels safe they will be willing to take on greater risks (Mena et al., 2020). When there is a higher risk the individual will be more cautious to take high risk decisions in order to not fail (Mena et al., 2020). Since a minor disruption (e.g., machine breakdown, short delay in production) occurs more often compared to a major disruption (e.g., long delay in production), a manager would be more willing to take on risk during a minor disruption. As the minor disruption is a situation more familiar to the manager due to a higher occurrence rate. Therefore, we assume that a major disruption will have a negative relation with risk behavior and a minor disruption will have a positive relation with risk behavior.
Hypothesis 2: A major (minor) supply chain disruption will be negatively (positively) associated with risk behavior during a disruption
3.3 Risk-taking propensity
When a person has a risk averse perception they will prefer a certain profit over a possible higher but more risky profit (Gan et al., 2004). Meaning that the SC would rather go for the safe choice than for the choice with a potentially higher profit. The manager is affected by the bounded rationality, meaning that they do not have all the information regarding the supply chain and the disruption (March, 1994). When there is less uncertainty surrounding the disruption (e.g. minor disruption) the individual will take a higher level of risk during decision-making (Cantor et al., 2014). The previous experiences and information available regarding the disruption will result in a manager with a risk averse perception to be willing to take on more risk.
SC manager is more willing to take the option of higher profit and more risk. (Wolf & Houlihan, 2018). For this hypothesis we assume that the risk-taking propensity or preference of the individual has an influence on the risk behavior during a disruption. Additionally, we assume that the type of supply chain disruption (minor/major) has a moderating effect on the relation between a risk averse perception and the risk behavior of a SC manager.
Hypothesis 3. The level of supply chain disruption has a moderating effect on the relation between risk averse and the risk behavior during a disruption
FIGURE 3.1 Conceptual Model + -+ (-)
Aspiration levels
Risk behavior
during a disruption
Being risk averse
Supply chain disruption:
METHODOLOGY
The aim of this paper is to research the potential effects individual risk perceptions and the decisions making process during disruptive events have on the behavior of an individual. A scenario-based experiment was used in order to study the behavior of the participants during a disruption. This helps to understand why supply chain managers make decisions or why they have a preferences for certain types of decisions (Rungtusanatham et al., 2011). A vignette study help understand actions in a specific context (e.g. disruptions) clarifying the judgement of individuals (Barter & Renold, 1999). By showing two descriptive vignettes it is possible to find if the variables in the hypothesis have an effect on each other.
4.1 Experimental design
A scenario was developed describing the disruption severity and the decision that has to be made. The unit of analysis of this study is at a firm level in a supply chain, with individuals acting as a decisionmaker. The scenario assigned the respondents to the role of supply chain manager (purchasing manager) for a fictional manufacturing firm. In the study a scenario was described of a situation where a disruption took place, and the managers are faced with a decision regarding the supply chain. The company information was identical for each respondent, only the degree of the disruptions differed. Each respondent received the same set of vignettes, resulting in a between-subjects design.
The company described in the study is an electric car manufacturer, were the respondent acts as a purchasing manager. The company is based in the Netherlands and sources its component globally. The main goal of the purchasing manager is to have sufficient stock available and to keep the cost as low as possible. A situation is described where the purchasing manager has just placed an order, since the stock of the components is running low. The following morning the purchasing manager is informed that an event occurred which affected this last order. The precise situation is currently unclear, and the consequences are not known. At this point the purchasing manager decides to contact the supplier to receive more information regarding this event. The full description of the company introduction can be found in Appendix A.
TABLE 4.1
Description of Scenario’s
Minor disruption Major disruption No disruption
The supplier stated that they experience a problem in their production process. Only a small part of the ordered components cannot be delivered on time, which results in a production delay of two
days. This will have a negative
impact on the financial situation of your firm, but it does not threaten the future of the company. Additionally, competitors and similar companies do not
experience this supply disruptions. Meaning that they do not experience a production delay.
The supplier stated that they are affected by lockdown restrictions due to COVID-19. A large part of the ordered components cannot be delivered on time, which results in a production delay of ten days. This will have a severe negative impact on the financial situation of your firm, and possibly threatens the future of the company. Additionally, competitors and similar companies do not
experience this supply disruptions. Meaning that they do not experience a production delay.
The supplier stated there was a case of miscommunication. There is no supply disruption, and all parts are delivered on time. Therefore, there is no negative impact on the financial situation of your firm, and there is no threat to the future of the company. Additionally, competitors and similar companies most likely do not experience a supply disruption. Meaning that they do not experience a production delay.
Following, the participant had to make several decisions regarding the supplier. For instance, if they wanted to stay with the same supplier or if they would choose to contract another supplier for the parts. Additionally, questions were asked regarding the base of this decision and regarding the risks experienced by the participant.
4.2 Sample
4.3 Treatment checks and Measurements
First, it is necessary to ensure that the vignette is realistic and clear for the participants (Rungtusanatham et al., 2011). As suggested in the paper of Rungtusanatham et al., (2011) the vignettes were reviewed on their overall clarity, the instructions provided and whether all necessary information is included. Furthermore, we need to confirm whether the participants need to find the vignettes realistic and plausible (Rungtusanatham et al., 2011). In order to ensure that the vignettes are realistic items with a five-point Likert scale (strongly disagree – strongly agree) were used to assess the realism of the scenarios (Pulles & Loohuis, 2020; Rungtusanatham et al., 2011). The overall mean response was (4.16; 4.34) which is comparable to previous studies (e.g. Pulles & Loohuis, 2020), suggesting that the scenarios are realistic. The mode shows that the item 1 the most frequently occurring value is ‘’somewhat agree’’ (4), for item 2 the most frequently occurring value is “strongly agree” (5) as found in Table 4.2.
TABLE 4.2
Items Realism descriptives
Items Realism Mean Median Mode
The situation described in the scenario is realistic 4.16 4 4 I can imagine myself in a situation like described 4.34 4 5 4.3.1 Dependent variable
TABLE 4.3 Survey Items
Measure Survey item
Risk behavior during a disruption Given the scenario, I would take more risk than I would normally do 1= strongly disagree In the described situation it is OK to take more riskier decisions 5= strongly agree
(Cantor et al., 2014)
Taking into account the situation described, I am pre-pared to take risks to meet my aspired goals.
Aspiration discrepancy I feel that my firm’s performance is falling behind of that of competitors 1= strongly disagree I feel that in the past we performed better
5= strongly agree The performance of my firm is not what I had envisioned
Risk taking propensity I prefer to avoid risks
1= strongly disagree I really dislike not knowing what is going to happen 5= strongly agree I view myself as a risk avoider
(Chae et al., 2019)
Realism of the study The situation described in the scenario is realistic (Sarafan et al., 2020) I can imagine myself in a situation like described
4.3.2 Independent variables and moderator
Aspiration discrepancy. The construct measures how the participant perceives their current
aspiration level. The aspiration level is based on previous performance of the firm and influences the decisions the manager is willing to take (Cyert & March, 1963). In order to the measure the influence aspiration levels have on the decision making during a disruption. The aspiration levels of the participants were measured using a five-point Likert scale from 1 (“Strongly disagree”) to 5 (“Strongly agree”). The construct and items can be found in Table 4.3.
Risk-taking propensity. The degree of risk averse perception of the participant, which
could influence their decision during a disruption. Referring to the tendency of the participant to avoid risks (Weingart & Sitkin, 1995). The risk adverse perception of the participants was measured using a five-point Likert scale from 1 (“Strongly disagree”) to 5 (“Strongly agree”). The items are based on measures used by Chae et al. (2019). The construct and items can be found in Table 4.3.
Type of disruption. This depended on which scenario the participant has to follow. This
4.3.3 Control variables
Since multiple factors can influence the behavior of the participants, several control variables are included in this study. First the gender of the participant is included, since men are more likely to engage in risky behavior than women (Betz et al., 2002). Furthermore, age as well as
tenure are included as a control variables as they both have an influence on the risk behavior of
a participant (Weingart & Sitkin, 1995). Lastly, the type of industry the participant works in is included as a control variable
The survey items and corresponding questions can be found in Table 4.3. Furthermore, a summary of the variables and their corresponding descriptions is provided in Table 4.4. Additionally, the respondents were asked for information on their age. gender, industry, and tenure, which will act as control variables for the models. The distribution of the control variables can be found in Appendix E.
TABLE 4.4 Variables Summary
Variable name Variable type Description
Risk behavior Dependent The level of risk an individual is willing to take in the decision-making process during the scenario
Aspiration discrepancy Independent The aspiration level of an individual based on previous firm performance during the scenario
Risk taking propensity Independent Degree of risk averse perception of an individual
Type of disruption Independent/moderator Based on the three scenarios’: A minor disruption, A major disruption, No disruption
Age Control Age of the respondent Gender Control Gender of the respondent Tenure Control Tenure of the respondent
Type of industry Control Type of industry respondent works in
4.3.4 Manipulation and unidimensionality check
First, a manipulation check was performed in order to see if the respondent’s understood the survey. The independent samples t-test was significant (p<.000) for both tests between major disruption and minor disruption, and between major disruption and no disruption. This means that there is a significant
Second, in order to test the unidimensionality of the study the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and the Bartlett's test of sphericity test were performed on the entire dataset. The KMO test is used to adequacy of the sample when using it for Confirmatory Factor Analysis. The results indicate that it is appropriate to do a factor analysis (KMO= .717, Bartlett's Test of Sphericity: p <.00). As the KMO should be > .50 in order to be suitable for factor analysis and the Bartlett's Test of Sphericity should be significant for a factor analysis to be performed (Williams et al., 2010). The pattern matrix shows the factor loadings of the constructs (Table 4.5). The table (4.5) shows that the constructs load on different components, indicating that the constructs are unidimensional. Therefore, the overall scores of the constructs could be calculated.
TABLE 4.5 Pattern Matrix
Note: “Extraction Method: Principal Component analysis.
Rotation Method: Oblimin with Kaiser Normalization.”
Construct and items Component
1 2 3
AspirationLevel_1 .841 .004 .066
I feel that my firm’s performance is falling behind of that of competitors
AspirationLevel_2 .891 -.036 -.067
I feel that in the past we performed better
AspirationLevel_3 .812 .078 .057
The performance of my firm is not what I had envisioned
SCDisruptRiskTaking_1 .093 -.013 .833
Given the scenario, I would take more risk than I would normally do
SCDisruptRiskTaking_2 -.129 .047 .945
In the described situation it is OK to take more riskier decisions
SCDisruptRiskTaking_3 .147 -.078 .758
Taking into account the situation described, I am pre-pared to take risks to meet my aspired goals.
RiskAverse_1 .048 .880 .030
I prefer to avoid risks
RiskAverse_2 -.042 .799 .052
I really dislike not knowing what is going to happen
RiskAverse_3 .030 .862 -.109
Calculating the overall scores of the constructs resulted in the variables Risk behavior,
Aspiration discrepancy and Risk-taking propensity. Table 4.6 reports the descriptive statistics
such as the mean and the standard deviation as well as the correlations for each pair of variables included in the analysis. The missing values were pairwise deleted resulting in a total sample of N=94 for the descriptive statistics.
TABLE 4.6
Descriptive Statistics and Correlations of Variables
Variable 1 2 3 4 5 6 7 8
1 Risk behavior .424*** -.052 .115 .054 -.017 -.161 -.200* 2 Aspiration discrepancy .424*** .262** .285*** .061 .088 -.061 -.308*** 3 Risk taking propensity -.052 .262** -.030 .129 .200* .066 -.106 4 Type of disruption .115 .285*** -.030 -.068 -.038 .040 -.033 5 Age .054 .061 .129 -.068 .265*** .770** .259** 6 Gender -.017 .088 .200* -.038 .265*** .115 .191* 7 Tenure -.161 -.061 .066 .040 .770** .115 .335*** 8 Type of industry -.200* -.308*** -.106 .302 .259** .191* .335*** Mean 3.6844 3.8369 3.8582 2.01 36.63 1.43 10.93 5.16 Std. deviation .88579 .86308 .83170 .810 10.258 .497 9.215 1.655 Notes: *** p<0.01, ** p<0.05, * p<0.1; N= 94 4.3.5 Multicollinearity check
4.3.5 Reliability of the study
Last, the reliability of the study was tested, starting with the Cronbach’s alpha which measures whether items measure the same construct consistently. A Cronbach’s analysis was conducted on the variables Risk behavior, Aspiration discrepancy and Risk-taking propensity. It was found that the variables alphas are all above >0.8 which suggests that there is an adequate level of inter-item reliability for the three constructs (Table 4.7). The individual Cronbach’s alphas of the constructs can be found in Table 4.7. Additionally, the KMO value of the variables are above the minimum threshold of >.5 meaning that the sample is adequate, although there are all <.8 which suggest a bigger sample is needed (Table 4.7) (Williams et al., 2010). The Bartlett's Test of Sphericity is p <.00 for all three variables (Table 4.7).
TABLE 4.7
Measurement quality of constructs
Variable Cronbach’s alpha KMO Bartlett’s test
Aspiration discrepancy .824 .718 α = .000 Risk taking propensity .818 .686 α = .000 Risk behavior .823 .722 α = .000
FINDINGS
In this section the results of the hypotheses analysis performed by using SPSS are discussed. First, hypothesis 1 was tested by splitting up the data into the according disruption types. Second, a linear regression was performed for both hypothesis 2 and hypothesis 3. In order to test the moderating effect of the supply chain disruption on risk behavior an interaction effect was used. The results of the regression analyses can be found in Table 5.1 and Table 5.3.
5.1 Aspiration discrepancy
To test of aspiration discrepancy has a positive association to risk behavior the data was split into the different disruption degrees. Three separate linear regressions were performed on the major, minor and no disruption data in order to see whether aspiration discrepancy has an effect on the risk decisions. H1 states; “Aspirations levels will be positively associated to risk
behavior during a major disruption.” The following table (5.1) shows the results of the linear
TABLE 5.1
Regression for Risk Behavior During a Disruption
Major Minor No disruption
β t-value β t-value β t-value
Constant 2.861 1.983* 3.965 2.401** 1.198 2.570**
Control Variables
Tenure dummies (ref group: 15-45 years)
0-3 years .308 .435 1.008 1.065 -.142 -.219 4-6 years .593 .960 .519 .798 -.057 -.076 7-14 years .598 1.162 1.621 1.885* -.004 -.012
Age dummies (ref group: 41-74 years old)
23-28 years old -.817 -1.070 -1.559 -1.933* -.808 -.1.114 29-34 years old -.424 -.633 -.584 -.782 .319 .419 35-40 years old -.173 -.301 .017 .025 .020 .055 Gender dummy (ref group: female) .592 1.383 -.018 -.036 .613 1.958*
Industry dummies (ref group: Automotive)
Chemicals/Pharmaceutic - - .811 .574 -.192 -.425 Consumer goods .912 1.100 -.893 -.736 -.821 -1.658 Electronics -.803 -1.161 .245 .233 .261 .707 Industrial machinery -.381 -.593 .336 .262
Services including financial services .117 .193 -1.429 -1.240 .967 3.191*** Other .237 .322 -1.632 -1.523
Independent Variables
Aspiration discrepancy .177 .689 .029 .082 .603 5.281*** Risk taking propensity
R .679 .461 .049 1.118 .642 .412 -.022 .949 34 .883 R2 .779 Adjusted R2 .653 Model F 6.185 Number of observations (N) 31 34
Note: *** significant at 1%; ** significant at 5% level; * significant at 10% level
From Table 5.1 we can conclude that H1 is rejected, the results for a major disruption are not significant. The major disruption model shows that aspiration discrepancy does not have a significant influence (F 1.118 p = .407) on the risk behavior during a disruption. The data for a minor disruption and no disruption were also tested in order to see if the results would differ. The minor disruption model (F= .949, p=.531) also does not show a significant result. However, the results of the no disruption model show a significant result (F=6.185, p<.01). Suggesting that the aspiration levels are positively associated to risk taking behavior when the firm does not experience a disruption. Moreover, the adjusted R2 (.653) is the highest for the no disruption
model.
Additionally, the Durbin-Watson test for the models showed no concern for
TABLE 5.2
Durbin-Watson Test Hypothesis 1
Disruption degree Major Minor No disruption Durbin-Watson 2.173 1.603 1.865
5.2 Disruptions and risk-taking propensity
The second and third hypotheses are tested by using the whole data set, thus including minor, major and no disruption data. Table 5.3 shows the results of both hypotheses analysis. Using a linear regression H2 was tested in order to see if; A major (minor) supply chain disruption will
be negatively (positively) associated with risk behavior during a disruption. Model 1 shows
that the results are not significant (F= 1.441, p= .148), and the direction of the coefficient is opposite of the hypothesis. A major disruption having a positive association (β = .304) and a minor disruption having a negative association (β = -.91) with risk taking behavior during a disruption.
Before testing the moderating effect of the type of supply chain disruption in H3, the effect of risk-taking propensity on risk behavior during a disruption was analyzed. The regression of model 2 is significant (F=1.558, p<.1).
An interaction effect was created between the risk-taking propensity and the major disruption dummy in order to test H3: The type of supply chain disruption has a moderating
effect on the relation between risk averse and the risk behavior during a disruption.
A linear regression analysis was performed with the interaction variable regressed on risk behavior during a disruption. The regression shown in model 3 was not significant, therefore H3 is rejected (F =1.508, p=.113).
TABLE 5.3
Regression analysis for Risk behavior during a disruption
Model 1 Model 2 Model 3 Model 4
β t-value β t-value β t-value β t-value
Constant 4.128 6.907*** 5.050 6.266*** 5.403 5.995*** 3.841 4.152***
Control Variables
Tenure dummies (ref group: 15-45 years)
0-3 years .284 .696 .297 .737 .305 .755 .327 .875 4-6 years .332 .914 .362 1.006 .384 1.062 .390 1.164 7-14 years .210 .723 .234 .812 .252 .872 .232 .869
Age dummies (ref group: 41-74 years old)
23-28 years old -.781 -2.025** -.826 -2.161** -.810 -2.112** -.755 -2.120** 29-34 years old -.227 -.628 -.256 -.713 -.272 -.757 -.212 -.636 35-40 years old -.347 -1.241 -.337 -1.219 -.361 -1.297 -.183 -.695 Gender dummy (ref group: female) .126 .605 .056 .265 .043 .201 .092 .464
Industry dummies (ref group: Automotive)
Chemicals/Pharmaceutic .768 .733 .748 .721 .749 .721 .510 .530 Consumer goods -.175 -.316 -.266 -.485 -.253 -.460 -.442 -.865 Electronics -.461 -.838 -.579 -1.055 -.590 -1.073 -.600 -1.181 Industrial machinery -.283 -5.42 -.440 -.839 -.456 -.866 -.489 -1.003 Services including financial services -.361 -.688 -.562 -1.054 -.556 -1.042 -.502 -1.027 Other -.867 -1.641 -.973 -1.848* -.985 -1.869* -.734 -1.492
Independent Variables
Disruption dummies (ref group: no disruption)
Major disruption (dummy) .304 -.824 -.226 -.983 -.241 -1.041 -.334 -1.554 Minor disruption (dummy) -.91 1.290 .293 1.255 -.460 -.519 -.550 -.623 Aspiration discrepancy .433 3.831*** Risk taking propensity -.191 -1.683* -.279 -1.844* -.305 -2.181**
Interaction effect
Major disruption* Risk taking propensity .193 .880 .139 .633
R .425 .205 .063 1.441 .481 .231 .083 1.558 100 .488 .238 .080 1.508 100 .598 .357 .213 2.469 99 R2 Adjusted R2 Model F Number of observations (N) 100
Note: *** significant at 1%; ** significant at 5% level; * significant at 10% level
In order to see which model is best, the adjusted R2 is included in Table 5.3. The adjusted R2
corrects the value of R2 when an additional variable is added to the model. This value shows
the exploratory power of the model. As can be seen in the table (5.3) model 4 has the highest adjusted R2 suggesting that this is the strongest model with an exploratory power of .213.
An interesting observation can be seen in all four models, which is that the age dummy
DISCUSSION AND CONCLUSION
The purpose of this paper was to study the influence of risk-taking propensity and aspiration discrepancy of supply chain managers on their personal risk-taking behavior during different degrees of supply chain disruptions. The findings of this paper confirm some earlier findings stated in literature, but overall show that a disruption (i.e., minor and major) does not influence the SC manager to take on more risks. This section will discuss the theoretical and managerial implications of the findings, and suggestions for future research.
6.1 Theoretical implications
This research has developed and tested a theoretical model combining aspiration discrepancy from the Behavioral Theory of the Firm, risk perception theory, and disruptions. This study contributes to literature on supply chain risk decision behavior, focusing on the individual level of the decision-making process during a disruption. While previous research mainly focused on the organizational level of decision making (DuHadway et al., 2018; Ellis et al., 2010).
The findings show that aspiration discrepancy is only positively associated when there is no disruption occurring. This shows that SC managers will not partake higher risks during a minor or major disruption. This goes against the notion of the BTOF that the individual is willing to take on more risk when the aspiration levels fall below the performance of the firm (Cyert & March, 1961; Kahneman & Tversky, 1979). Moreover, MacCrimmon & Wehrung (1986) stated that the individual will take on less risk when the firm is performing well and that riskier choices will be made when the firm is not performing well (I.e., below aspiration level) (as cited in March & Shapira, 1987). The finding of this study could be explained by previous literature stating that organizations close to failure often show risk averse behavior ((Miller & Bromiley, 1990; Wiseman & Bromiley, 1996). Additionally, Chen and Miller (2007) found that firms with higher performance (I.e., above aspiration level/no disruption) will partake in higher risk taking and change.
a study comparing the reaction of a participant to both a minor disruption and a major disruption will gain results that are aligned with previous literature.
Third, the moderating effect of the degree of supply chain disruption on the relation between risk taking propensity and risk behavior is rejected in this study. Meaning that according to the results of this study, a supply chain disruption (e.g. major disruption) does not have an effect on the relationship between risk taking propensity and risk behavior. Which is not in line with previous findings of DuHadway et al. (2018), and March & Shapira (1987). Who state that the uncertainty of future risk levels of a disruption lead an individual to make different risk-decisions based on their individual risk perception (DuHadway et al., 2018). Likewise, when SC managers perceive that their job is in danger, they are willing to take on more risks (March & Shapira, 1987). A reason for the results of this study to not be in line could be that the SC manager does not feel that his job is in danger. In addition, it could be possible that the participant did not experience the situation as really uncertain since the delay is already stated in the scenario.
When testing a model with all the independent variables and controls included, the results are significant. Showing that both risk taking propensity and aspiration discrepancy have a significant effect on the risk behavior. It shows that a risk averse perception has a negative influence on the risk behavior of the SC manager. This is in line with previous findings of Gan et al. (2004) and Sarafan et al. (2019). In addition, the aspiration discrepancy has a positive coefficient in relation to risk behavior. Meaning that the aspiration levels do influence the risk behavior of a SC manager, although this result is only visible with the no disruption data set and the complete data set. A reason to explain this could be that the sample of the separated data sets are not big enough (N= 31, N=34, N=34). The results of this model are in line with the findings of Cyert & March (1961) and Kahneman & Tversky (1979) and show that the tolerance for risk increases when the aspiration levels are unsatisfactory compared to the current performance of the firm.
Lastly, some interesting observations can be noticed in the data. The male dummy has a significant positive coefficient in the no disruption regression analysis (Table 11). This result is in line with the findings of Deditius Island et al. (2007) who found that woman are often more risk averse compared to males. The age dummy (23-28 years old) is significant for all the models in Table 13 and for the minor disruption model in Table 11. The negative relation to risk behavior, can be explained by a lack of previous experience. Since people of this age often just entered the workforce, don’t have a lot of experiences and thus might be reluctant to take on risks. Previous experiences with disruptions have positive influence on the SC manager to take on risks (Cantor et al., 2014; March, 1994; Sarafan et al., 2019).
6.2 Managerial implications
The results of the data analysis led to several practical implications. First, the analysis shows that the individual aspiration level has an influence on the decision-making process of a SC manager when there is no disruption occurring. Additionally, there is a difference between the risk perception of female and male employees within the firm. Likewise, younger employees are also shown to be less willing to take on risks during the decision-making process. Last, employees that have a risk averse perception or character are too less willingly to take on risks.
These findings show that the human behavior has a significant influence on the organizational response to risk and on the risk strategy. Therefore, firms should take into account that individual characteristics influence the overall risk the firm is taking. Clear communication within the firm and establishing a risk strategy plan could create a cohesive organizational risk response. This is also in line with findings of DuHadway et al. (2018) who state that communication within the firm regarding risks, can create a ‘rebalancing’ of the individual risk perception. Leading to a balanced risk perception within the employees of the firm.
Additionally, the aspiration levels are too based on the perception of the individual. The goals and aspired future performance of the firm should be communicated within the firm. Leading to all the employees of the firm having a coherent idea of what the future performance of the firm should be. Resulting in similar aspiration levels, and therefore in similar responses in the decision-making process.
6.3 Future research and limitations
opportunities for future research. The first limitation is that the sample size of the survey after deletion is not adequate. For the different scenario datasets, the sample sizes where around +/- 30. The KMO test for the data was .717, which is middling. A value between .8 and 1 is preferred.
The data of this study was acquired through MTurk. This can be a limitation since there is some disagreement on the use of MTurk. Some of the respondents on MTurk even claim false identities, ownership or activity in order to be able to participate in a study (Wessling et al., 2017). Moreover, it does not give the full impact compared to actually experiencing financial impact from a disruption at the firm. Since the participants are just at home filling out the survey instead of actually living the scenario and having to deal with the consequences.
This study focuses on the effect of COVID-19 (I.e., major disruption). It is possible that the answers of the participants are influenced by their personal experiences with COVID-19. Unfortunately, the data of this study was too limited to include this as a control variable. It is thus possible that negative or very positive experiences with COVID-19 influenced the results of the analysis. Future research could add controls for the participants personal experiences with the virus in a study.
Similarly, the culture of the participant is not taken into account in this study. Although previous research has shown that the culture of the participant can influence their risk perception Since some cultures are more prone to risk averse behavior (Sarafan et al., 2019, 2020). An interesting extension to literature on risk behavior and BTOF with a focus on disruptions, is to use the culture or nationality as a control variable. In order to see if this would create different results.
A future opportunity for research is to study the reaction of the same individuals to different disruption scenarios by doing a multiple case study. It would be interesting to see if the same participant would react differently to different disruption scenarios. The effect of different disruption types could then be researched more extensively.
6.4 Conclusion
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APPENDIX Appendix A: Company introduction
Company introduction
You are the purchasing manager of an electric car manufacturer called Voltage. This car manufacturer purchases its components globally from different suppliers. Your main goal is obviously to have sufficient car components in stock available for manufacturing. However, currently there is not much capital in your organization, meaning that there is a strong urge to keep costs as low as possible. The other day you placed an order with one of your suppliers of 2,000 car seats that are intended for the newest electric model. You placed this order since the stock of this component (car seats) is running low. Your supply base of car seat components consists of 15 different car seat suppliers and these suppliers are globally spread. You, as a purchasing manager, are solely responsible for managing the relationships with this entire supply base. You are able to select different suppliers. These suppliers differ in their offer regarding the price, quality, delivery time, service etc.
This morning you are informed by one of your colleagues that an event occurred which affected the latest purchase order of car seats. It is not exactly clear what happened and what the consequences are, but you recognize that the supplier is fully accountable for this supply disruption. You decide to directly contact the supplier…
Appendix B: Minor disruption
Minor disruption
After contacting the supplier, it became clear that your supplier experiences a production problem. A minor part of the ordered car seats will not be delivered on time. Resulting in a production delay of two days. This delay has negative financial implications for your company. However, these effects are only mild and does not endanger your organization's future.
Furthermore, competitors and similar organizations in the automotive industry do
not experience this same supply disruption. Therefore, competitors and similar organizations
do not have a production stop/delay in their manufacturing plant.
Please click on the next button to continue
Appendix C: Major disruption
Major disruption
After contacting the supplier, it became clear that your supplier is affected by lockdown restrictions in its country due to a pandemic (COVID-19) outbreak. A large proportion of car seats will not be delivered on time. Resulting in a production delay of ten days. This delay has severe negative financial implications for your company. These negative effects are alarming and could possibly endanger your organization’s future.
Furthermore, competitors and similar organizations in the automotive industry do
not experience this same supply disruption. Therefore, competitors and similar organizations
do not have a production stop/delay in their manufacturing plant.
Appendix D: No disruption
No disruption
After contacting the supplier, it became clear that it was a simple communication mistake. The supply disruption did not exist in the first place. The supplier will deliver 100% of the order on time. This means that there will be no delay of the production process. Subsequently, there is no negative financial implication, and the organization is still in good
financial health.
Furthermore, competitors and similar organizations in the automotive industry likewise do
not experience a supply disruption. Therefore, competitors and similar organizations too do
Appendix E: Distribution of Variables TABLE 7.1 Distribution of Variables Category Percent Age distribution 23-88 years old 26% 29-34 years old 24% 35-40 years old 24% 41-74 years old 26% Tenure distribution 0-3 years 23% 4-6 years 23% 7-14 years 25% 15-45 years 28% Missing value 1% Gender distribution Female 44% Male 56%
Industry type distribution
Automotive 4%
Chemicals/Pharmaceutic 1% Consumer goods 13% Electronics 11% Industrial machinery 21% Services including financial services 22%
Other 28%
Manipulation distribution
