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Faculty of Economics and business
MSc Thesis Supply Chain Management
The Mind’s Eye:
Decision Making Through the Lens of Supply Chain
Managers in the Wake of Disruptive Events
Student: Daniël Jonker
Rug student number: S1920359
E-mail: j.jonker.7@student.rug.nl
First supervisor: dr. ir. N.J. (Niels) Pulles
Second supervisor: dr. K. (Kirstin) Scholten
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ABSTRACT
This research provides evidence that aspiration levels and perceived trustworthiness are predictors of a managers risk-taking propensity. This effect holds in the presence of minor supply chain disruptions but does not when the disruption brings forth more uncertainty. An examination of the direct relationship between supply chain disruption impact and managerial risk-taking propensity yielded no significant results. However, the role of supply chain
disruption severity is apparent in this research, as a different disruption severity impacts the relationship between perceived performance discrepancy and managerial risk-taking
propensity.
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INTRODUCTION
The year 2020 has presented major challenges, with the COVID-19 virus disrupting lives and economies worldwide. Like the 2011 Japan earthquakes and the 9-11 terrorist attacks, the disruption was unforeseen. However, unlike these events, the aftermath is long, with the COVID-19 virus still spreading and many countries entering a lockdown state, to prevent the virus from spreading between citizens and thus limiting more casualties. Amidst this humanitarian standpoint, companies are doing what they can to survive equally, with the country lockdowns heavily affecting them. Responding to such seldomly occurring high impact events, (Ivanov, Dolgui, Solokov and Ivanova, 2017) fast organizational responses are required (Mahajan and Tomar, 2020). Companies actively pursue strategies to cope with unforeseen circumstances like the COVID-19 virus, aimed at detecting incoming disruptions, dealing with disruptions when they occur and bringing the organization back to its original state. However, many companies were unable to adequately respond to the effects of the pandemic (Craighead, Ketchen and Darby, 2020). The effects of the COVID-19 crisis have spread like an oil spill to all levels of supplier networks (Fernandes, 2020). As of February 2020, 94% of the ‘Fortune 1000’ companies have suffered a major disruption (Fortune, 2020). The full extent of the pandemic has not yet been experienced (Barrot, Grassi and Sauvagnat, 2020), as new and more contagious mutations of the virus have been discovered and vaccinations are still in its testing phase.
The COVID-19 virus has emphasized the importance of supply chain management (SCM). The role of SCM is to detect and evaluate the disruptions and subsequently plan a course of action to react to the supply chain disruption (Ambulkar, Blackhurst and Grawe, 2015). How this process works in practice remains unknown to a large extent (Jüttner and Maklan, 2011). Organizational responses vary greatly among different companies towards similar disruption impact (Sheffi and Rice, 2005). Deployment of these responses to supply chain disruptions are usually viewed from an organizational point of view in literature (Bode, Wagner, Petersen and Ellram, 2011). Classic SCM literature views these decision makers as rational decision makers, making decisions towards optimal solutions (Sweeney, 2013). Little is known regarding the behavioral aspect of supply chain management and the managerial process leading up to organizational decisions (Schorsch, Wallenberg and Wieland, 2017).
6 theory of the firm (BTF). According to the BTF, decision makers lack the cognition to process all the information presented to them and therefore apply heuristics to filter and process information, which will be used towards finding a solution the decision maker finds acceptable. Decision makers maintain aspiration levels which reflect their minimum desired performance output. When these aspiration levels are not met, a more extensive search is triggered to increase performance above the aspiration levels (Cyert and March, 1963).
Incorporating the BTF into SCM literature can provide valuable new insights for the field of supply chain management. This study aims to do that, through analyzing risk-taking propensity in the light of supply chain disruptions. Decisions made to counter the negative effects of supply chain disruptions are made by only a small group of people within each firm. Yet very little is known about the underlying decision-making processes (Gino and Pisano, 2008). This is reflected by the variation in organizational responses towards these disruptions. The BTF examines firms as groups of individuals rather than one large decision-making entity, examining and learning from behavior rather than only examining the organizational response (Argote and Greve, 2007). Ultimately, this can contribute to our knowledge regarding these organizational responses to a great extent (Tokar, 2010). Using the lens of the BTF, this research aims to explore the role of aspiration levels in managerial decision making when dealing with supply chain disruptions, while also examining the potentially moderating role of perceived supplier trustworthiness. The research is guided by the following research question:
In the event of a supply chain disruption, how does a perceived performance discrepancy alter managerial risk-taking propensity and how is this effect mediated by perceived trustworthiness of the supplier?
7 The organization of this paper is as follows. A literature review will shed light on the relevant concepts for this study after which several hypotheses will be presented. The methodology section describes the research setting, after which the results and discussion section will discuss the findings of this research.
THEORETICAL BACKGROUND
2.1 Supply chain disruptions
Supply chain disruptions are events that are unplanned, unannounced, characterized by high uncertainty and disrupt the regular flow of goods and service within the supply chain (Craighead, Blackhurst, Rungtusanatham and Handfield, 2007). A single disruption in a supply chain can affect other organizations within the chain (Sawik, 2016) and also cause a ripple effect, affecting neighboring supply chains with potentially long-lasting affects (Scheibe and Blackhurst, 2018; Bradley, 2016). Minor disruptions are the most likely to occur, with minor business impact. Contrarily, major disruptions have longer lasting effects and can severely impact operations. For example, after a flooding in Thailand HDD-drive producer Western Digital was unable to deliver to organizations in its extended supply chain when a flooding occurred in its Thailand plant. Both HP and Intel, two major customers, suffered a decrease in revenues as a result.
Supply chain disruption impact refers to the significance of loss an organization suffers (Zsidisin, Ellram, Carter and Cavinato, 2004) and can be quantified in different ways. Hendricks and Singhal (2005) studied disruption impact through long term stock prices, whereas Cavinato’s (2004) study regarding a union strike disrupting port operations focused on the time that is required to get operations back to normal. Tomlin (2006) analyzed impact through examination of contingency costs, analyzing the costs it takes to get operations back to normal. Whereas these three approaches offer a more longitudinal view of disruption impact, Handfield, Blackhurst, Elkins and Craighead (2007) rather focused on missed revenue to measure supply chain disruption impact, quantifiable at an organizational level and measured swiftly.
As supply chain disruptions are unannounced and unplanned, organizations implement resilience strategies to increase disruption resistance as well as recovering from them (Blackhurst, Dunn and Craighead, 2011). Supply chain resilience is “the capability of the firm
to be alert, adapt to, and quickly respond to changes brought by a supply chain disruption”
8 adequately respond to a disruption in three phases: “readiness, responsiveness and recovery” (Sheffi and Rice, 2005). Constituting these three distinct phases are four formative resilience capabilities: flexibility, velocity, visibility and collaboration (Jüttner and Maklan, 2011). Flexibility is the ability to absorb the changes brought by a supply chain disruption and effectively respond to them (Richey, Skipper and Hanna, 2009) and refers to the robustness of the supply chain. Velocity refers to the time and efficiency required to respond to disruptions (Smith, 2004). Visibility includes all the relevant information regarding activities throughout the chain, which prevents unnecessary responses when disruptions occur and provides confidence (Christopher and Lee, 2004). Lastly, collaboration contributes to reduction of uncertainty levels, as supply chain collaboration can provide aid in case of a disruption. Contrarily, inadequate collaboration can increase the severity of supply chain disruption impact.
Supply chain disruptions have been analyzed through notable theory frames such as the resource-based view (Blackhurst et al, 2011), contingency theory (Brandon-Jones, Squire, Autry and Petersen, 2014) and systemic risk theory (Kaufman and Scott, 2003). Resilience strategies implemented to counter the negative effects of supply chain disruptions often fail to meet their desired results (Bendoly, Donohue and Schultz, 2006), but an explanation for this remains uncertain. As supply chains are becoming highly complex and human behavior is a central element in supply chain decision making, analysis at the individual level is essential (Gino and Pisano, 2008). However, the behavioral component of SCM is often overlooked in research. This is worth examining, as the decision outcome is greatly influenced by personality traits and norms and values (Strohhecker and Großler, 2013). Ultimately, the immediate decisions when faced with a supply chain disruption are made by supply chain managers. Yet, little research has been conducted to examine their decision-making processes.
2.2 The behavioral theory of the firm
9 use of standard operating procedures and heuristics. Additionally, the BTF acknowledges that decisions are made by small groups within the organization, as opposed to treating the firm as a single entity (Argote and Greve, 2007).
The BTF states that managers use aspiration levels in evaluating the outcome of decisions, which serve as an organizational target or goal and helps to simplify the cognitive process and depicts the minimum satisfactory outcome (Schneider, 1992). Organizations have a wide spectrum of aspiration levels, set for a variety of goals regarding profitability, sales and production goals (Cyert and March, 1963). However, profitability goals have received the most attention by scholars, as these are often linked to financial incentives for managers. Although aspiration levels can differentiate between individuals, subordinates are likely to have similar goals as managers, as reaching or failing to reach these goals has consequences for their careers (Greve, 2003). Aspiration levels are predominantly associated with comparison to other organizations (Greve, 2003), past goals and achievements (Cyert and March, 1992) as well as organizational experience (Greve, 2008). Although aspiration levels are influenced by individual characteristics, they are mainly subject to situational events and immediate needs (Schneider, 1992).
When aspiration levels are met or exceeded through company performance, theory expects firms to maintain the status quo and continue their successful path (Bromiley, Miller and Rau, 2001). Organizations continue to aim for an incremental increase in performance (Cyert and March, 1963) and avoid actions that can get performance below aspiration levels (March and Shapira, 1987). When performance does decrease, it triggers problemistic search aimed towards bringing performance back towards its aspired level (Greve, 2003). This becomes increasingly complex when performance falls further behind aspiration levels, searching for solutions that are increasingly different from the last solution (Cyert and March, 1963) as well as considering options far beyond their normal reach (Park, 2007) thus increasing and complicating the search scope.
2.3 Managerial risk-taking propensity
10 Sitkin and Weingart (1995) modeled risk behavior as being determined by a decision makers risk-taking propensity and the risk perception of the situation. Risk propensity stems from the decision maker itself, based on risk preferences, handling of risk-related situations and experience with making risky decisions. Risk perception is determined by how the problem is framed towards the decision maker, top management team homogeneity, social influence experienced from leaders and the organizational culture, familiarity with the situation and organizational control systems that either reward or punish decision outcome (Sitkin and Pablo, 1992).
Recent behavioral research has further emphasized the importance of behavioral attributes in decision making processes. Individual perceptions and preferences are highly differentiated among managers (March and Shapira, 1987). Experience with past disruptions as well as training received influence supply chain managers capabilities in dealing with disruptions (Sjoberg, Wallenius and Larsson, 2006). A greater impact of a supply chain disruption prompts greater motivation to act (Bode et al, 2011), as negative effects increase the need to change current behaviors to restore organizational stability (Ford, 1985). A more severe supply chain disruption can influence the decision makers recovery efforts, as factors such as time pressure and emotional intensity increase (Hastie and Dawes, 2001). How a company responds to disruptions depends to a great extent on managerial behavioral attributes (MacDonald and Corsi, 2013).
Several studies have found evidence that the interplay between aspiration levels and actual performance levels are an important predictor of managerial decision making, with performance falling further below aspiration levels inspiring organizational change (Park, 2007; Greve, 2011; Iyer and Miller, 2008). These increasing deviations from the desired level are likely to be met with increasing levels of risk-taking behavior with the aim of restoring company performance to its status quo (Greve, 2003). Contrarily, performance above aspiration levels decrease risk-taking behavior (Hoskisson, Chirico, Zyung and Gambeta, 2017). Greve (1998) found that higher performance above aspiration levels appears to have a stronger effect in reducing risk than underperforming below aspiration levels has on increasing risk.
2.4 Perceived trustworthiness of supplier
11 defined by Mayer, Davis and Schoorman (1995) as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control the other party” (Mayer et al, 1995, p712). It facilitates management of conflicts, is a source of communication (Blomqvist, 2002) and increases strategic flexibility, predictability, and adaptability (Seppänen, Blomqvist and Sundqvist, 2007). As figure 2.1 by Currall and Inkpen (2006) highlights, one act perceived as negative to the business partner can diminish trust. Trust is an evolutionary concept, but also highly volatile.
Figure 2.1: Trust over time
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HYPOTHESES
3.1 Supply chain disruptions
Dealing with supply chain disruptions is a recurring theme for supply chain managers. Disruptions influence the financial performance as it often disturbs the daily order of operations, as it influences both demand and supply (Bode and Wagner, 2015). As supply chain disruptions occur suddenly and are unintentional, they require immediate attention (Bode and Wagner, 2015) as disruption can heavily impact financial performance (Handfield et al (2006) as well as disrupt the day-to-day operations.
Organizations and individuals benchmark themselves compared to competitors, as well as their own previous performance to evaluate their own performance (Park, 2007). Through this process of comparison, aspiration levels are formulated (Durach and Wiengarten, 2020). When a supply chain disruption affects financial performance, aspiration levels will likely not be met, especially when competitors are not subjected to the effects of the disruption. Furthermore, a larger disruption impact will influence performance even more, leading to a greater discrepancy between actual performance and aspiration level performance (Mezias, Chen and Murphy, 2002).
H1: An increase in supply chain disruption severity will increase managerial risk-taking propensity.
3.2 Managerial risk-taking propensity
Decision makers perceive performance under a reference point as a loss (Kahneman and Tversky, 1979). This loss in revenue arises from goods not being able to flow through the supply chain (Bradley, 2016). When performance drops further below desired standards, the motivation to act increases (Bode et al, 2011). The decision maker is then challenged with restoring organizational stability and is willing to change current behaviors (Ford, 1985). According to the BTF, to restore organizational stability, decision makers evaluate options beyond their normal reach, as problemistic search is conducted to overcome the gap between actual performance and aspiration levels. To overcome this discrepancy, individuals are willing to take more risks (Greve, 2003).
13 preferences and perceptions are highly differentiated (March and Shapira, 1987), risk-taking propensity is different for each decision maker. The behavioral theory of the firm emphasizes that the likeliness of risk-taking increases when performance falls below aspiration, despite personal characteristics.
H2: A greater perceived performance discrepancy will increase managerial risk-taking propensity.
3.3 Perceived trustworthiness of the supplier
Close relational ties between buyer and supplier can mitigate the perception of risk. It serves as an expectation that business partners look out for each other, without showing signs of opportunistic behavior (Chiles and McMackin, 1996). It contributes to the long-term stability of organizations (Spekman, Kamauff and Myhr, 1998). When a business partner is perceived to have ability, benevolence and integrity, trust between partners is established (Mayer et al, 1995). As a result, the trusting party accepts vulnerability towards the trustee (Kim, Ferrin and Rao, 2007).
There is a consensus that trust is a risk-mitigating factor, despite this premise not being actively tested (Braunschneidel and Suresh, 2009). However, for high levels of trust to lead to a competitive advantage, the trusted supplier’s actions pose an uncertain factor. When the perceived risk of opportunistic behavior is low, decision makers will perceive less risk when making decisions regarding that supplier (Chiles and McMackin, 1996). Furthermore, collaboration efforts can reduce uncertainty and increase readiness when a supply chain disruption occurs, if risk-related information is shared between the parties involved (Faisal, Banwet and Shankar, 2006). The concept of perceived trustworthiness greatly influences managerial decision making (Svensson, 2001). Gambetta (2000) suggests that trust is especially relevant during conditions characterized by high uncertainty.
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Figure 3.1: Conceptual Model
METHODOLOGY
This study is based around a scenario-based experiment, using a vignette study through which hypotheses will be tested (Rangtusanatham et al, 2011). All participants read the same introduction, after which they are subjected to a scenario or vignette, in which a single element will be manipulated. A vignette is composed of focused descriptions, short scenarios or hypothetical situations, which are then presented to participants. The participants will consequently describe their actions under these circumstances described by the vignette (Ashill and Yavas, 2006), which will give insight in their beliefs, norms, attitudes, and perceptions (Finch, 1987). Through this approach, the vignette allows for a robust study of causality, as the different vignettes can yield different outcomes (Taylor, 2006). Furthermore, using vignettes provides less complex and less abstract data compared to regular survey research (Alexander and Becker, 1978).
4.1 Experimental design
15 well as an experimental cues model, reflecting the factors of interest and is different for each of the scenarios. In the scenarios presented to the participants, the company they work for as a purchasing manager has incurred a supply chain disruption. The disruption severity is different across the different scenarios. The aim of these different scenarios is to examine a difference in beliefs, norms, attitudes, and perceptions between the different groups (Finch, 1987). Through this method, the impact of the supply chain disruption can be reflected upon by examination of the respective vignette outcomes. The introduction and vignettes, as presented to the participants, is presented in Appendix A.
4.2 Sample
The crowdsourcing service mTurk, which stands for Amazon Mechanical Turks service, will be used for this experiment. Through this service, a sufficiently large group of prospective participants can be identified. Subsequently, specific research subjects can be selected at random to participate in the study, which minimizes researcher-introduced biases (Rungtusanatham, Wallin and Eckerd, 2011). It requires participants to complete predetermined tasks (Paolacci et al, 2019) and has been used actively in the fields of marketing (Maulana, 2020) and psychology (Sussman and Olivola, 2011). It shows great promise for the field of supply chain management (Knemeyer and Naylor, 2011), as it efficiently gathers information in exchange for a small fee, through a trusted database. Ultimately, 100 participants were gathered to conduct the scenario-based experiment, operating in different industries as supply chain managers, which is a prerequisite for this study.
4.3 Constructs and variable measurement
The questions the participants were subjected to include different research topics by different researchers, as multiple studies are combined in this scenario-based experiment. The common denominator of these studies is the interest in supply chain disruptions and the effect it has on individual supply chain managers.
16 gender. A detailed overview of the concepts, measures and survey items used throughout this study can be found in figure 4.1 and 4,2.
Construct Variable type Description
Change in risk-taking behavior Dependent Measures the change in risk-taking propensity as a result of the presented scenario
Perceived performance discrepancy
Independent Measures the participants attitude towards performance in the presented scenario
Scenario Independent Displays the disruption severity the participant is subjected to in the presented scenario
Perceived trustworthiness of the supplier
Moderator Measures the participants trust towards the supplier as a result of the presented scenario
Gender Control variable Gender of the participant Tenure Control variable Tenure of the participant
Age Control variable Age of the participant
Table 4.1: Variable overview
Variable Survey Item
Change in risk-taking behavior (5-point Likert Scale)
1= strongly disagree 5= strongly agree
(Greve, 1998; Cantor, Blackhurst and Cortes, 2014)
Given the scenario, I would take more risk than I would normally do. In the described situation it is OK to take more riskier decisions
Taking into account the situation described, I am pre-pared to take risks to meet my aspired goals.
Perceived performance discrepancy (5-point Likert Scale)
1= strongly disagree 5= strongly agree (Park, 2007)
I feel that my firm’s performance is falling behind of that of competitors. I feel that in the past we performed better.
The performance of my firm is not what I had envisioned
Perceived trustworthiness of the supplier (5-point Likert Scale)
1= strongly disagree 5= strongly agree
(Norman, 2002) (Gassenheimer and Manolin, 2001)
There is a high level of trust in the working relationship with our supplier. I trust that our supplier’s decisions will be beneficial to my firm.
I would be willing to let this supplier make important supply decisions without my involvement.
I believe that suppliers would make sacrifices to support our firm.
Table 4.2: Variable measurement
4.4 Scenario realism and data preparation
17 scenario-based role-playing experiments, as it indicates that participants were able to maintain their attention towards the presented scenarios (Rungtusanatham et al, 2011). Furthermore, a realism check reflects how the participants can see themselves in the role assigned to them vignette and consequently how serious they take their role (Rungtusanatham et al, 2011). Two questions were used to measure scenario realism on a 5-point Likert scale, with a mean score of 4,25. This is comparable to results from earlier research (e.g., Pulles and Loohuis, 2020) indicating scenario realism (Bachrach and Bendoly, 2001).
The dependent variable for this research, the change in risk-taking propensity, was measured on a 5-point Likert scale containing 3 scales and adapted from an earlier study by Cantor et al. (2014) in their research regarding risk-taking behavior. To measure perceived performance discrepancy, earlier work by Mezias, Chen and Murphy (2002) offered conceptualizations to measure a change in aspiration levels in American financial organizations. The final item of perceived trustworthiness of suppliers is measured by combining conceptualizations of two research papers. The first three questions are from Norman (2002) regarding her research on protecting knowledge in strategic alliances. The last question is derived from the paper and Gassenheimer and Manolis (2001) regarding their research about trust and its mediating effect on future intentions towards the supplier relationship.
The scenarios in the experiment, described in table 4.3 below, display different levels of supply chain disruption impact. As participants partake in one out of three distinctly different scenarios, the three scenarios are dummy coded to enable analysis of each scenario.
Scenario 1: No disruption impact No disruption, good financial health
Scenario 2: Minor disruption impact Two day delay, light negative financial impact, not endangering the company
Scenario 3: Major disruption impact Ten day delay, severe negative financial impact, could possibly endanger company future
Table 4.3: Disruption scenarios
18 Confirmatory factor analysis was conducted to test for unidimensionality. The Kaiser-Meyer-Olkin measure of sampling adequacy (KMO) test was performed for the independent variable as well as the moderator. With a value of 0,658, above the 0,5 threshold, the data is fit for sample size is sufficient for factor analysis. Bartlett’s test of Sphericity is significant, which is the second precondition for conducting factor analysis, as it displays a sufficient correlation between variables. The resulting factor analysis displays the two independent variables loading high (>0,4) on their respective items, distinctly separating perceived trustworthiness of the supplier and perceived performance discrepancy.
Before testing the data, data quality will be assessed on assumptions commonly used before regression analyses. Independence of observations is guaranteed as the crowdsourcing service Amazon Mechanical Turk, or mTurk, is used to gather respondents. Initial data analysis shows a linear relationship between independent and the dependent variables. Thirdly, the conditional standard deviation standard deviation is constant, which means that the homoscedasticity assumption has not been violated. As a rule of thumb, a VIF value above 5 is considered high and implies that a variable should be deleted due to multicollinearity. Analysis shows VIF scores are all below 5 and thus there is no concern regarding multicollinearity (Gareth, Witten, Hastie and Tibshirani, 2013). A Cronbach’s Alpha analysis was conducted to measure internal consistency of the data. The results are above the 0,7-threshold reflecting good internal data consistency. Looking at the normal distribution of the dependent variable, change in managerial risk-taking propensity, a significant Shapiro-Wilk test shows that the dependent variable is not normally distributed. This indicates that answers are skewed towards one side.
Construct Cronbach’s Alpha
Change in risk-taking propensity 0,832 Perceived performance discrepancy 0,796
Perceived trustworthiness of the supplier 0,707
Figure 4.3: Cronbach’s Alpha per construct
RESULTS
5.1 Research participants
19 (M=10,40, SD = 9,26). Furthermore, 66 participants are male, and 34 participants are female. The participants are evenly distributed between the scenarios (34-34-32).
5.2 Hypotheses
A one-way ANOVA test was performed to examine the effect of disruption impact on the change in risk-taking propensity. Research participants did not exhibit signs of increasing risk-taking likeliness when disruption impact increased (mean no-disruption 3,5882, mean minor-disruption 3,3922 and mean major disruption 3,9479). There was no significant
difference between groups (F (2,97) = 3.012, p = ,054) For this reason, H1 is rejected. A post-hoc test shows that the means of the minor disruption and major disruption group are
significantly different from each other. This is the only significant difference between groups. A hierarchical regression analysis was performed to analyze H2. For this analysis, the variables age, tenure and gender are controlled for. The relevant results of the hierarchical regression analysis can be found in table 5.1 below. The hierarchical regression shows that in model 2, perceived performance discrepancy explained 18.3% of variance accounted for, in addition to the first model. This change in R² was significant (F (4,93) = 8.504, p < .001). Adding perceived trustworthiness of suppliers to the regression model explained an additional 17.4% of the variation in risk-taking propensity and this change in R² was significant (F (5,89) = 13.486, p < .001). Adding a moderating effect between perceived performance discrepancy and perceived trustworthiness of the supplier, as shown in model 4, explained an additional 0.1% of the variation in risk-taking propensity and this change in R² was not significant (F (6,88) = 11.172, p = .65).
Model 1 Model 2 Model 3 Model 4
β t-value β t-value β t-value β t-value Constant 2.947 6.545*** 1.440 2.816*** .076 ,143 -.800 -,398 Control Variables
Age ,042 2.782 *** ,034 2,447** ,025 1.992** ,025 2.018** Tenure
Gender (female = reference group) -,041 -,300 -2.541** -1.521 -,034 -,271 -2.351** 1.529 -,024 -,335 -1.842* -2.093** -,025 -,338 -1.874* -2.102** Independent Variables Perceived performance discrepancy ,450 4.820*** ,368 4.324*** ,587 1.190 Perceived trustworthiness of the supplier ,539 5,222*** ,783 1.424 Interaction effect Perceived performance discrepancy * perceived trustworthiness of the supplier
20 N R R2 98 ,291 ,085 ,056 ,085 2.908 2.908 98 ,518 ,268 ,236 ,183 23.230 8.504 95 ,657 ,431 ,399 ,174 27.267 13.486 95 ,658 ,432 ,394 ,001 .204 11.172 Adjusted R2 R2 change F change F
Table 5.1: Regression analysis for risk-taking propensity during a disruption *** significant at 1%; ** significant
at 5% level; * significant at 10% level:
5.3 Within groups
Consequently, the effects for h2 and h3 were examined for each individual scenario. The result can be found in table 5.2 below. The “no-disruption’’ group shows a significant effect for Perceived performance discrepancy on risk-taking propensity (t (31) = 2.429, p < .05). The “minor disruption’’ group shows a significant effect for perceived trustworthiness of the supplier on risk-taking propensity (t (33) = 5.828, p < .001). No significant results were found for the independent variables in the “major disruption’’ group
Disruption Impact: No disruption Minor Major
β t-value β t-value β t-value
Constant ,132 ,131 -,439 -,429 2,247 1,782*
Control Variables
Age ,033 1,612 ,004 ,182 ,033 1,018
Tenure
Gender (female = reference group)
-,036 -,421 -1,565 -1,442 -,020 -,129 -1,034 -,477 -,024 -,586 -,693 -1,683 Independent Variables
Perceived performance discrepancy ,338 2,429** ,273 1,607 ,154 ,641 Perceived trustworthiness of the supplier ,511 1,777* ,855 5,828*** ,234 1,336
N R 32 ,714 ,509 ,415 8.717 5.396 34 ,796 ,633 ,568 20.381 9.663 29 ,416 R2 ,173 Adjusted R2 -,007 F Change F 1.165 ,960
Table 5.2: Individual scenarios *** significant at 1%; ** significant at 5% level; * significant at 10% level
DISCUSSION
6.1 Theoretical implications
21 risk-taking propensity is significantly increased when the decision makers perceive performance as below standard compared to the aspiration levels. As reflected in the third model in table 4.1, it shows the most variance in the dependent variable (Adjusted R2 ,399). This is in accordance with research (Kahneman and Tversky, 1979; Greve, 2003). However, when examining the individual groups subjected to different scenarios, it shows that the ‘no disruption’ is the only group that shows an increase in risk-taking propensity. This disputes earlier research by Hoskisson et al. (2017). Contrary to the general analysis in table 4.1, the individual scenario analysis incorporates perceived trustworthiness in the same model, which shows a substantially larger effect on risk-taking propensity compared to perceived performance discrepancy. I expected a higher perceived trustworthiness of the supplier would increase the strength of the relationship between a perceived drop in performance and risk-taking propensity. This was not supported in the findings. Rather, perceived trustworthiness shows to have a direct effect on risk-taking propensity. Although not incorporated into the theoretical framework of this research, it is a promising finding for future research. A possible reason behind this effect can be that trust in the supplier diminishes the perceived chance the suppler will engage in opportunistic behavior (Gassenheimer and Manolis, 2001). According to Gambetta (2000), trust is especially relevant in times characterized by uncertainty, as the decision maker can focus attention towards countering negative disruption effects. In this case, trust acts as a risk-mitigating factor for the supply chain manager (Braunschneidel and Suresh, 2009). Examining the different scenarios, the role of perceived trustworthiness is only significant for the ‘no disruption’ and the ‘minor disruption’ group. This implies that in times of little perceived uncertainty, risk-taking propensity is higher.
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6.2 Managerial implications
There is still a long way to go before behavioral SCM finds a bigger support base in SCM literature. This research has partly shown that behavioral SCM is a factor that should be researched more in SCM literature. Whereas the traditional theory of the firm views the decision makers as rational actors being mindful and future oriented, making decisions based on known outcomes (Sanders et al, 2016), this research shows that decisions are much more complex and derived from subjective experiences. Although not discarding the traditional economic theory of the firm, examination through the lens of the behavioral theory of the firm provides additional insights for what prompts supply chain managers to act.
Thus far, behavioral SCM has already highlighted its use by providing a better understanding of the bullwhip effect (Ancarani, Mauro and D’Urso, 2013). However, this is one of the few instances that behavioral SCM received attention, as the field does not get much attention from scholars (Schorsch et al, 2017). Decision making behavior plays a big role in supply chain management (Gino and Pisano, 2008) and therefore deserves more attention from scholars. For this to happen, it requires both managers and SCM scholars to not look at firms as a single entity but rather as coalitions of groups or individuals (Sanders et al, 2016).
6.3 Limitations and future research
This body of research shows some promising findings but is limited in its generalizability. For one, the sample size is relatively low compared to other survey research. Furthermore, the participants are answering out of their personal preferences, which could yield different outcomes when analyzing different cultures and areas and should therefore be investigated before results can be applied towards firm-to-firm relationships. Another factor to consider is the global pandemic the world is currently facing. Moynihan (2008) found that such unprecedented events require time to get accustomed to as managers are unfamiliar with the situation.
23 behavioral experiment is completed far from the workplace without any feeling of urgency or pressure. When managers interact with others in their working environment, psychological factors arise that influence decision making (Donohue and Siemsen, 2011). As a result, the participants actions in the experiment can greatly differ from their actions in their working environment. Integrating a psychological point of view into behavioral SCM can prove useful in further learning about workplace behavior.
The BTF, with its focus on organizational learning can complement SCM literature and consecutively improve supply chain operations. Examination of aspiration levels and their influence on managerial risk-taking only compile a small fraction of what the BTF has to offer the field of SCM. The theory can benefit SCM literature by further differentiating between firm-level analysis and individual- or group-firm-level analysis. Using new variables, it can examine other factors affecting risk-taking propensity. This research provides new insights regarding the role of perceived supplier trustworthiness. As this relationship remains is understudied in literature, it can proof to be a promising field for future research. Will the findings of this research still hold when a different sample and sample size are used? Will results be similar when examining this to an even greater extent in the light of the COVID-19 crisis? Or is there a boundary level after which risk-taking stabilizes?
6.4 Conclusion
This research was guided by the following research question:
In the event of a supply chain disruption, how does a perceived performance discrepancy alter managerial risk-taking propensity and how is this effect mediated by perceived trustworthiness of the supplier?
24 Although supply chain disruption severity does not directly influence risk-taking propensity, it does mediate other relationships and should be taken into account in supply chain disruption research.
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Appendix A: The scenario based experiment
Company Introduction:
You are the purchasing manager of an electric car manufacturer called Voltage, based in The Netherlands. This car manufacturer globally sources its components. 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 new electric model. You placed this order since the stock of this component (car seats) is running low. This supply base 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. For the car seat component, 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 employees 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…
SCENARIO’S
Minor disruption Major disruption No 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 the organisation’s future. Furthermore, competitors and similar organisations in the automotive industry do not experience this same supply disruption. Therefore, competitors and similar
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 severe and could possibly endanger the organisation’s future. Furthermore, competitors and similar organisations in the automotive industry do not experience this same supply chain disruptions. Therefore, competitors and similar organisations
33 organisations do not have a production
stop/delay in their manufacturing plant.
do not have a production stop/delay in their manufacturing plant.
