MANAGERIAL RISK-TAKING BEHAVIOUR IN SUPPLY BASE COMPLEXITY DECISION-MAKING FOLLOWING SUPPLY CHAIN DISRUPTIONS

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MANAGERIAL RISK-TAKING BEHAVIOUR IN SUPPLY BASE COMPLEXITY DECISION-MAKING FOLLOWING SUPPLY CHAIN DISRUPTIONS

Behavioral Theory of The Firm Wordcount: 10.434

Thesis, MSc. Supply Chain Management

University of Groningen, Faculty of Economics and Business

January 25, 2021

Martijn Pouwels 3851001

Supervisor dr. ir. N.J. (Niels) Pulles University of Groningen

Co-assessor dr. ir. T. (Thomas) Bortolotti

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ABSTRACT

Supply chain managers frequently cope with events that result in disrupted supply chains. A recent example is the COVID-19 pandemic, causing globally disrupted supply chains. The literature on disruptions is primarily taking a rational perspective towards decision-making processes and neglects the bounded rationality perspective. This thesis focuses on behavioural decision-making process of supply chain managers following disruptions. Particularly, whether disruption impact influence risk-taking behaviour to the supply base decisions. The behavioral theory of the firm is used as theoretical lens in this study. A vignette-based experiment is performed in which 80 participants take the role of a supply chain decision-maker during (or in absence of) a supply chain disruption. Through applying a multiple linear regression analysis I conclude that individual decision-makers tend to decrease the supply base complexity if there is a greater perception of disruption impact. Especially, managers prefer to downsize the total amount of suppliers and simultaneously favour suppliers that offer a short delivery time. Keywords: Supply base complexity, behavioral theory of the firm, supply chain disruptions,

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1. INTRODUCTION

Supply chain managers frequently cope with events that disrupt their supply chains, since sourcing processes become more globalized and complex (Macdonald & Corsi, 2013; Xu, Zhang, Feng, & Yang, 2020). A recent example is the COVID-19 pandemic, which is one of the most impactful events in supply chain management history, causing globally disrupted supply chains (Craighead, Ketchen, & Darby, 2020). Before, during, and after supply chain disruptions, decision-makers are responsible to determine organizational policies towards supply chain disruption management. In general, these decisions relate to the detection, the evaluation, and subsequently the organisational actions towards this possible disruption (Ambulkar, Blackhurst, & Cantor, 2016).

The behaviour of supply chain actors during disruptions is a promising topic in the field of behavioural operations (Craighead et al., 2020; Gurnani, Ramachandran, Ray, & Xia, 2014; Wang, Craighead, & Li, 2014). A recent example of this research is that cognitive biases are contributing to the bullwhip effect. Decision-rules such as anchoring (where individuals rely on the initial piece of information) influence the order size of a new order since purchasing managers will be influenced by previous orders. This historical information about order sizes acts as a cognitive bias in determining the next order size (Croson, Donohue, Katok, & Sterman, 2014). Furthermore, decision-makers tend to order in higher quantities in a context characterised by an uncertain supply chain (Di Mauro, Ancarani, Schupp, & Crocco, 2020). However, we still know very little about managerial cognition in the organisational responses during supply chain disruption (Reimann, Kosmol, & Kaufmann, 2017) and how individuals behave within the supply chain disruption management is still largely unknown (Macdonald & Corsi, 2013). This study explicitly focuses on the decision-making processes in the supply base. The decision concerning the optimal size of a supply base is “haunting” supply chain managers for years (Sarkar & Mohapatra, 2009). Also, decisions on whether suppliers should have the same culture, geographical location or work norms are part of the supply base decision-making process. The decisions that (individual) managers take, will indisputably affect the complexity of a supply base. The degree of complexity has severe implications for controlling and coordinating the supply base (Choi & Krause, 2006).

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5 in an organisation develop aspirational performance levels and subsequently adjust their risk-taking preferences on these aspiration levels. Organisations appear to be risk-seeking when they perform under their aspiration level while being risk-averse above this aspiration level (Cyert, & March, 1963; Hoskisson, Chirico, Zyung, & Gambeta, 2017). Likewise, organisations search for solutions when the performance is below the aspired level, while this search diminishes when performing above this desired performance level (O’Brien & David, 2013).

The bounded rationality perspective of the behavioural theory of the firm manifest satisficing behaviour in which managers search for a “good enough” solution (Argote & Greve, 2007; Cyert, & March, 1963). This perspective is in contrast to neoclassical economics that hypothesizes a maximisation model, indicating that managers should find an optimal solution (O’Brien & David, 2013). The behavioural operations research stream aims to understand the decision-making of bounded rational managers and use this approach to improve (supply chain) operations processes (Katsikopoulos & Gigerenzer, 2013). It is defined by Carter et al. (2007, p.634) as: “The study of how judgment in supply management decision-making deviates from

the assumptions of homo economicus”. This bounded rationality principle affects the

decision-making process of supply chain managers (for example, decisions regarding the supply base) which eventually may alter the effectiveness on the disruption response (Polyviou, Rungtusanatham, Reczek, & Knemeyer, 2018).

Prior studies regarding decision-making in supply chain context assume that decision-makers are able to find the optimal solution by using a risk-neutral perspective (Gan, Sethi, & Yan, 2004). This thesis aims to integrate human behaviour into the context of supply chain management. Especially since individuals are bounded rational that may cause systemic biases, such as risk-aversion, which influence the making process of supply chain decision-makers (Kahneman & Tversky, 1979; Polyviou et al., 2018). This integration of human behaviour seems essential to understand organisational responses with regards to supply chain disruptions (Carter, Kaufmann, & Michel, 2007; Tokar, 2010). In addition, behaviour in sourcing decisions has been neglected in the literature (Chae, Lawson, Kull, & Choi, 2019). This results in the following research question:

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6 A scenario-based experiment will be used to answer the research question. Participants of this experiment fill in a questionnaire in which they indicate their preferred decisions, based on the scenario that is presented to them (Rungtusanatham, Wallin, & Eckerd, 2011). Subsequently, the data obtained from this experiment will be quantitatively analysed.

The first theoretical contribution of this research is that supply chain disruptions negatively relate to supply base size. Indicating that managers prefer risk-taking above the reduction of operational costs. Secondly, disruption impact is likewise negatively related to supplier lead time. This indicates an opposite effect regarding the first theoretical contribution, namely that manager prefer avoiding risks towards the sourcing process. Managers likely want to safeguard the production process at all costs to keep it up and running. Even though it increases operational costs. The third and last theoretical contribution of this study is that it provides a new theoretical perspective by studying the effect of disruptions supply base complexity decision making. Other studies regarding decision-making and supply chain disruption use disruption impact as a moderator or dependent variable (e.g. Brandon-Jones, Squire, & Van Rossenberg, 2015; Sarafan, Squire, & Brandon–Jones, 2020). This research’s perspective is deviating from previous studies that use supply base complexity as the predictor variable of the frequency of disruptions. This new theoretical perspective indicates that supply chain disruption reduces supply base complexity.

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2. LITERATURE BACKGROUND

In this literature background, three main components are discussed. The first section will focus on supply chain disruptions. Subsequently, the behavioural theory of the firm will be reviewed, considering that it is the theoretical lens in this thesis. In the last section, supply base complexity will be discussed in-depth.

2.1 Supply chain disruptions

Supply chain disruptions are unexpected events that disrupt the normal supply chain operations processes (Craighead, Blackhurst, Rungtusanatham, & Handfield, 2007). These supply chain disruptions can either be caused by natural disasters (e.g. earthquakes, cyclones, floods) or human actions (terrorist strikes, corruption, political instability). Organizations that get affected by a supply chain disruption may experience this differently, even when firms face the same disruption (Bode & Macdonald, 2017). Differences may be explained by the availability of slack resources such as inventory and time buffers that mitigate the disruption effects (Rice & Caniato, 2003). Also, the skills and experience of managers may be a crucial factor (Ritchie & Marshall, 1993). In contrast, there are numerous reasons not addressed in the literature regarding the impact of differences during disruptions (Bode & Macdonald, 2017). For example, supplier relationships and the complexity of the supply chain during the disruption is not addressed in the literature (Grewal, Johnson, & Sarkar, 2007). Bode and Macdonald (2017) state that several authors assume the link of managerial decision making and the impact of a supply chain disruption, while they mainly use anecdotical examples instead of tested theories (Macdonald & Corsi, 2013; Bode et al., 2014; Sodhi & Tang, 2009). This thesis will link behavioural decision-making and supply chain disruptions. In particular, the choices that individual managers make towards the supply base.

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8 supply risk increases (Choi & Krause, 2006; Sodhi, Son, & Tang, 2012). This global sourcing results in a geographical diverse supply base, hence a more complex supply base. However, another perspective is that multiple suppliers in different locations in your supply base may increase transactions costs. But reduces disruptions risks since there are alternative suppliers available in case of a supply disruption that is geographically related (Ye & Abe, 2012). Managerial decisions on how these supply chain bases are structured can affect how a firm response to these supply chain disruptions. These decisions can have a substantial impact on the recovery from disruptions. Managerial behaviour and decision-making during disruptions can be different and result in contrasting outcomes, as made clear in the Albuquerque fire case (Latour, 2001). Research on behavioural decision-making during disruptions is rather limited (Bode et al., 2011). There are only a few studies that involve both the behavioural operations and the supply chain disruption literature streams. For example, Croson, Donohue, Katok and Sterman (2014) studied the bullwhip effect and showed that this effect is (partly) caused by the decision-makers in the supply chain that perceive the entire supply line perspective of minor importance when making ordering decisions. In addition, the decision-makers’ experience is not sufficient to reduce the bullwhip effect (Wu & Kator, 2006). Supply chain disruptions cause decision-makers to deviate from their usual predictable ordering behaviour (Sarkar & Kumar, 2015). That indicates that human behaviour is a key element in complex systems such as supply chains (Gino & Pisano, 2008). However, the behavioural aspect is often not explicitly integrated into research. Most research takes the assumption of a rational, and hence a risk-neutral decision-maker (e.g. Nam, Vitton, & Kurata, 2011). In this research, I deviate from that perspective and take a bounded rational perspective in individual decision making.

2.2 Behavioral theory of the firm

The behavioural theory of the firm provides a theoretical lens to explain why boundedly rational managers make strategic organizational choices (Cyert, & March, 1963). The theory addresses several key assumptions that deviate from the classic standard economic theory. The behavioural theory of the firm is applied in various research settings and is often used as a starting point of decision-making under the bounded rationality assumption (Argote & Greve, 2007). This research will use aspiration levels as described by Cyert and March (1963).

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9 influenced by the firm’s aspirational level (see: Shinkle, 2012 table II). According to the behavioural theory of the firm, performance below the aspiration level triggers problemistic search for solutions to regain the desired aspiration level (Cyert & March, 1963; Gavetti et al., 2012). This implies that when the firm’s performance is below the aspiration level, it is more likely that organizational change occurs (Greve 2003; Shinkle, 2012).

Performance below the aspirational level increases the risk tolerance for managers. They perceive this situation as a ‘loss’ and are willing to take risks to reach the desired aspiration level (Cyert, & March, 1963; Greve, 2003; Kahneman & Tversky, 1979). Therefore, performance below the desired aspiration triggers risk-taking behaviour (Greve, 2003). Individuals take more risk when they perform under the aspirational level of the firm and most previous study on risk-taking behaviour support this effect (Hoskisson et al., 2017). In contrast, managers become risk-averse when the organization is performing above the aspiration level (Cyert & March, 1963). Previous studies examined the discrepancy between a firm’s performance and its historical and/or social aspiration levels (Hoskisson et al., 2017). Studies that used experiments come to the same conclusion as they state that managers take more risk when their firm is performing under the aspirational level (Lant and Montgomery, 1987; Wehrung, 1989).

2.3 Supply base complexity

Supply base complexity is one of the various conceptualizations of the broader concept of supply chain complexity (Manuj & Sahin, 2011). Choi and Krause (2006, P. 638) take the perspective of the supply base to define sources of supply chain complexity. Their definition of a supply base complexity is: “The degree of differentiation of the focal firm’s suppliers, their

overall number, and the degree to which they interrelate”. Their definition consists of three

key components:

1) Total number of suppliers in the supply base

2) The degree of differentiation of suppliers in the supply base 3) The degree to which suppliers interrelate in the supply base1

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10 Bozarth et al., (2009) build upon this definition to define upstream supply chain complexity. However, these researchers introduce an additional element in their definition of upstream supply chain complexity, which is supplier lead time. Lead time can increase complexity in the supply chain because long or unreliable lead time will force buyers to adapt their planning and material processes as uncertainty increases (Bozarth et al., 2009). Another example is that lead time influences the magnitude of the bullwhip effect in a supply chain (Chen et al., 2000). I integrate elements of both papers that may be relevant in managerial decision-making in a supply chain context, especially following disruptive events. Table 2.1 presents the elements of supply base complexity regarding managerial decision making in the supply chain that will be used in this study.

TABLE 2.1 Supply Base Complexity

Elements of supply base complexity Supporting literature

Number of suppliers Choi and Krause (2006)

Supplier differentiation Choi and Krause (2006)

Supplier lead times Bozarth et al., (2009)

Number of suppliers.

From the perspective of supply chain management, the number of suppliers refers to the total number of current suppliers with whom the focal company (the buyer) is doing business with. The buyer experiences more complexity when dealing with ten suppliers, then when dealing with only two suppliers (Choi & Krause, 2006). A large supply base results in more physical and information flows. Additionally, more relationships should be managed, which increases costs (Bozarth et al., 2009). A smaller supply base is likely to obtain higher quality products while at the same time decrease the total purchasing costs. However, fewer suppliers will increase an organization’s dependence on their suppliers and also it increases supply disruption risks (Sarkar & Mohapatra, 2009; Tang & Tomlin, 2008; Yu, Zeng, & Zhao, 2009). There has been a lot of previous studies regarding the supply base, and most scholars agree on downsizing the supply base to a manageable size (Sarkar & Mohapatra, 2009).

Supplier differentiation

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11 be. For example, a supply base that is geographical very differs will often imply that suppliers speak different languages, have different cultures, work norms, and longer distances to transport materials. When suppliers are located close to the geographical location of the buyer, it will be less complex than dealing with suppliers that are globally distributed (Choi and Krause, 2006). In this thesis, supplier differentiation will consist of 1) geographical location of suppliers and 2) organizational culture. The assumption is that geographical location in the supply base will be an important decision, in the context of supply chain disruptions. An event such as a natural disaster occurs at a specific geographical location and this will affect suppliers in one specific region, while other suppliers, outside the geographical boundaries of the event, are not affected (Berger et al., 2004). Additionally, a differentiated culture is often overlooked in the supply chain since it is an invisible concept. Misalignment of culture between organizations can potentially be damaging in the buyer-supplier relationship (Gattorna, 2006). In contrast, differentiation in culture may also be the foundation of innovation (Dooley & Van de Ven, 1999). A greater variation in culture in a supply base will increase supply base complexity. From the focal company’s perspective, it would easier to manage a supply base that shares common organizational culture and have suppliers that are geographically located nearby Choi & Krause, 2006).

Supplier lead time

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3. HYPOTHESES

This section will briefly introduce a key mechanism in the behavioural theory of the firm which is applied in this study. In particular, the aspiration levels and how this relates to supply chain disruptions will be addressed. Subsequently, three developed hypotheses will be discussed separately.

Individuals perceive performance under a certain reference point as a ‘loss’ situation (Kahneman & Tversky, 1979). In supply chain context, managers are likely to perceive disruptions as a ‘loss’ situation, as disruptions may negatively influence a firm’s performance (Bode & Wagner, 2015; Golgeci & Ponomarow, 2013; Hendrics & Singal, 2015). Cyert and March (1963) refer to this loss situation as performing under the desired aspiration level. The assumption in this study is that; a supply chain disruption makes managers perceive that their firm is performing below the aspiration level (financial) when facing a supply chain disruption. Therefore, my hypotheses hinge on the cost vs risk trade-off that individual may experience during decision-making processes.

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13 Conversely, managers perceive a reduction in the number of suppliers in the supply base as a risk-taking decision. A reduction of supply base complexity is a cost-efficient strategy but increases supply risk (Choi & Krause, 2006). Thus, managing and coordinating fewer suppliers would in general be a more risk-taking option. According to the behavioural theory of the firm, when a firm is performing below the desired aspiration level, they will seek more risk in their decision making. When a firm is performing above the aspiration level, they are risk-averse in their decision making. Therefore, I hypothesize that:

H1. Supply chain managers are more risk-seeking when they perceive a disruption as high

impactful, and therefore are more likely to decrease their total number of suppliers in the supply base.

Secondly, supply chain managers have an influence on the geographical location of a supplier if there are multiple comparable suppliers available. In this specific situation, the supply chain manager has the freedom to select suppliers in their supply base based on geographical location criteria. Outsourcing activities may increase the geographical location and thereby increases supply risks, but it offers cost advantages for the buying firm (Tang, 2006). For instance, early 2018, the Trump Administration imposed high tariffs on products from the Chinese market. In response to this occurrence, many firms searched for suppliers out of China since purchasing costs skyrocketed (Fujita, 2019). Prior to these import tariffs, many organizations benefitted from their global sourcing strategy because it provided substantial cost advantages to contract Chinese suppliers. However, due to this new legislation, some organisations became more vulnerable given that they did not have suppliers located outside of China borders. Again, decisions concerning the geographical location of the first-tier supplier may conceivably posit costs vs. risk trade-offs (e.g. Norrman & Jansson, 2004).

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14 organisations to search for new suppliers, since the old suppliers were not appealing anymore due to the import tariffs.

Consequently, when a (natural) disaster occurs in a specific region, it could affect multiple suppliers, or even the entire supply base if they are in close geographical proximity. For example, a region could be prone to disasters which are a threat to supply chains (e.g. earthquakes, hurricanes, floods, terrorism, legislation) (Sheffi & Rice, 2005). A decision-maker is therefore risk-seeking when the supply base is geographical undifferentiated. Therefore, to return to the desired aspiration level as before the supply chain disruption:

H2. Supply chain managers are more risk-seeking when they perceive a disruption as high

impactful, and therefore are more likely to decrease the degree of differentiation in their supply base

Lastly, A well-known and often primary benefit of accepting increasing delivery lead time is that it decreases the costs of goods (e.g. global sourcing). Nevertheless, this cost-related benefit is often also negatively associated with an increased disruption risk (Habermann, Blackhurst, & Metcalf, 2015), supply chain instability (Nassimbeni, 2006), and increased supply chain complexity (Bozarth et al., 2009). Implicating that managers may face a trade-off between costs and risk when deciding to agree on the delivery lead time that their (potential) suppliers offer. According to the behavioural theory of the firm managers demonstrate risk-seeking behaviour when the performance level is below the aspiration level (Cyert, & March, 1963). Therefore, one could argue that following a disruption the buyer would be more risk-seeking. Specifically, accepting longer delivery lead time to save operational costs, that subsequently increases the (financial) performance level. In theory, this will ultimately lead to regaining the desired aspiration level since a decrease of operational costs will increase the (financial) performance level.

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15 below aspiration level, decision-making shifts to a survival mode (e.g. Lopes, 1987). This shifting occurs when potential losses are extremely large which would endanger the survival of the organisation (Staw, 1981). Subsequently, in these endangering situations, managers prefer decisions that provide certainty (Laughhunn, Payne, & Crum, 1980). Firms that perform extremely poor, will simply aspire to survive and diminish risk-seeking behaviour in decision making (Lyer & Miller, 2008).

This may indicate that managers prefer to secure the continuing of the production following disruptive situations. This is done by preserving a short lead time, despite increased operational costs. A disruptive event while the delivery lead time is relatively long can potentially have enormous consequences regarding the inventory level and consequently the production process. Following a disruption, the manager wants to secure the production process to prevent a potentially destructive situation, such as a complete production stop. Therefore, I hypothesize that:

H3. Despite that supply chain managers are risk-seeking when they perceive a disruption as

high impactful, they will prefer a decrease of delivery lead time in their supply base

The hypotheses are integrated into the conceptual model (see Figure 3.1). This model specifies that the occurrence of a supply chain disruption negatively relates to the three components of supply base complexity.

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4. METHODOLOGY

In this study, I use a scenario-based experiment (i.e. experimental vignette study) to test the developed hypotheses (Rungtusanatham et al., 2011). Vignettes are “short descriptions of a

person or a (social) situation which contain precise references to what are thought to be the most important factors in the decision-making or judgment-making processes of respondents”

(Alexander & Becker, 1978: 94). Implicating that participants take a prior defined role. Specifically, participants in this experiment take the role of purchasing manager. In comparison to surveys, scenario-based experiments generate more reliable data when studying respondents behaviour (Alexander & Becker, 1978). It minimizes memory loss and retrospective biases since respondents indicate their response immediately after reading the vignette (Wathne, Biong, & Heide, 2001).

Scenario-based experiments are useful to evaluate behavioural decision-making processes (Bendoly & Eckerd, 2013). Several studies used scenario-based experiments to study human behaviour in an operations management setting. An example of this type of research is from Ro, Su and Chen (2016), who study if organizations have a different perspective regarding opportunism and relationship continuance when facing the same supply chain disruption. Specifically, the study participants behaviour during various decision-making situations (during disruptions). In addition, by using a scenario-based experiment I can accurately manipulate those variables that matter in this specific research. Therefore, a scenario-based experiment is most appropriate. The unit of analysis is at the firm level within a supply chain.

4.1 Experimental design

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TABLE 4.1 Vignette Description Vignette introduction

You are the purchasing manager of an electric car manufacturer called Voltage, based in The Netherlands. This car manufacturer sources 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. 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…

Vignette scenarios

1. No disruption 2. Minor disruption 3. Major 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 organisation is still in good/sufficient financial health. Furthermore, competitors and similar organisations in the automotive industry likewise do

not experience a supply chain

disruption. Therefore, competitors and similar organisations too do not have a production stop/delay in their manufacturing plant.

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 organisations do not have a production stop/delay in their manufacturing plant.

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

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18 After reading the vignette, a questionnaire is presented to each participant. Participants are asked to indicate their preferences regarding each of the supply base complexity components, while simultaneously considering the cost vs risk trade-off. I explicitly clarified that each decision that the participant takes, has implications for operational costs and supply risks. For clarity I provide the cost vs risk trade-off preface that is presented to participants regarding their supply base size decisions-making: “First, decide on supply base size, you can either reduce the number of suppliers which will reduce costs, but will increase future supply risks. Or you can increase the number of suppliers which will reduce the supply risks but will increase operational costs.” Subsequently, participants decide on the number of suppliers that they prefer in their supply base. Finally, regarding the other two supply base complexity decisions (i.e. differentiation and lead time), the trade-off is similarly presented to participants.

4.2 Sample

The scenario-based experiment is distributed through the crowdsourcing service MTurk (Amazon Mechanical Turk). Researchers use MTurk across different disciplines including supply chain management studies (e.g., Cantor et al., 2014; Pulles & Loohuis, 2020). This instrument utilizes human participants to complete distinct tasks (Paolacci et al., 2010). Participants are paid a fixed amount of $0.50 dollar for completion of the experiment. In addition, MTurk workers (US-based) are selected that have an approval rate above 95%. This approval rate suggests the proportion of successful tasks completed.

The questionnaire collected data regarding supply base complexity decisions of these participants following a supply chain disruption. A total number of 107 MTurk workers participated in this questionnaire of which 100 participants completed the survey. The remaining 7 participants are labelled as non-finishers and are deleted from the experiment. Additionally, 10 participants are removed since they failed the attention check at the end of the questionnaire. Lastly, 10 participants are excluded due to speeding2. Hence, the final sample size entails 80 individual participants. The response time to fill in the questionnaire took these participants 450 seconds on average, as roughly expected. All data is collected on 30th November 2020. Table 4.2 presents the number of participants in each scenario.

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TABLE 4.2 Experimental Design

Scenario type Frequency Percentage

No disruption 23 29%

Minor disruption 30 38%

Major disruption 27 33%

Total 80 100%

There is a wide range in both age and work experience. The youngest participant in the experiment is 23 years old, while the oldest participant is 74 years (M = 38.28; SD = 10.59). The participants work experience is likewise widely spread (M = 10.73; SD = 9.34). A slight majority of participants are male (54%). Overall, most participants work in the machinery industry (19%) and the services industry (25%). A table with participant characteristics (age and work experience) is shown in Table 4.3. More detailed descriptive statistics are presented in Appendix D.

TABLE 4.3 Demographic Statistics

Characteristics Mean SD Min Max Range

Age 38.28 10.59 23 74 51

Work experience 10.73 9.34 0 45 45

4.3 Measurement

The operationalisation of the variables is presented in Table 4.4. Each item in this survey is measured on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Cronbach’s Alpha is used for measuring internal consistency. According to Karlsson (2016), the desired threshold for items is  > 0.7. In addition, the Keiser-Meyer-Olkin measure of sampling adequacy indicates an acceptable correlation among variables (KMO = .70).

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20 TABLE 4.4

Measurement Variables

Variable (items)  M SD

Scenario realism (Dabholkar, 1994) 0.516 4.25 0.60

The situation described in the scenario was realistic 4.18 0.73

I can imagine myself in the described situation 4.33 0.73

Size of the supply base 0.861 3.50 1.00

I would decrease the total number of suppliers 3.49 1.15

I would start sourcing from less suppliers 3.53 1.23

I would prefer a lower number of suppliers in my supply base 3.40 1.06

Differentiation of the supply base 0.707 3.80 0.67

I would reduce the geographical dispersion of suppliers 3.64 0.95 I would prefer suppliers that are in close geographically proximity 3.81 0.96 I would prefer suppliers that have a similar organizational culture 3.94 0.83 I would prefer suppliers that share common work norms 3.80 0.92

Supplier lead time 0.226 3.91 0.50

I prefer suppliers that offer short lead times in the delivery process 3.79 0.76 I would prefer suppliers that offer a high reliable delivery process 4.04 0.86 I would prefer suppliers that can offer a competitive price 3.90 0.76

Personal risk-taking propensity (Meertens & Lion, 2018) 0.819 3.78 0.50

I prefer to avoid risks 3.83 0.95

I really dislike not knowing what is going to happen 3.69 1.09

I view myself as a risk avoider 3.83 1.07

Manipulation check (i.e. perceived disruption impact) 0.874 3.53 1.32 A major disruption occurred in my organization’s supply chain 3.54 1.30 Because of a disruption in our supply base, my organization is

possibly in danger

3.51 1.50

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21 characteristics are used to measure this specific variable. The first dependent variable in this study is the size of the supply base ( = ) measured by three items Subsequently, the dependent variable differentiation of the supply base ( = ) is measured by four items. The last dependent variable is lead time of suppliers in the supply base ( = ) and is measured by three items. This last variable does not meet the general threshold ( => .7). However, deleting a single item would not result in attaining this desired Cronbach’s Alpha level. Therefore, no items are deleted. More specific statistics (on item-level) of these variables can be found in Table 4.4. A factor analysis of the supply base complexity variables can be found in Appendix A.

Several control variables are included in this study. The personal risk-taking propensity is used as a measure for quantifying participants risk-taking behaviour. This measure is adapted from a study of Meertens and Lion (2008) and also used in a similar and recent behavioural study on sourcing decisions (e.g. Chae et al., 2019). In addition, I controlled for effects of gender, age, and work experience. Work experience is included since behaviour towards risk-taking is changing when individuals obtain experience in certain situations (Sitkin & Weingart, 1995). Furthermore, age and gender have found to be important control variables since scholars identified differences regarding risk-taking (Arch, 1993; MacCrimmon & Wehrung, 1990). Lastly, a manipulation check is conducted to assess the effectiveness of the proposed scenarios. This variable is measure by two items ( = ) This manipulation check variable assessed to what degree the participants perceive the impact of a disruption (i.e. perceived disruption impact).

4.4 Descriptive statistics

Descriptive statistics for each of the separate participants' group is presented in Table 4.5.

TABLE 4.5

Frequency Distribution (Scenario Groups)

Scenario N M SD

No disruption 23 3.04 1.45

Minor disruption 30 3.06 1.28

Major disruption 27 4.44 0.60

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22 A one-way analysis of variance (ANOVA) is performed to compare the means of each three scenario groups. A statistically significant difference is found for the scenario ‘major disruption’ compared to the other scenarios, F (2,77) = 12,76, p < 0.01. The Post Hoc Test Games-Howell is used to explore the three groups more in-depth. This test indicates that the mean of the group ‘major disruption’ is significantly different compared to the mean of the group ‘no disruption’ and ‘minor disruption’. However, no significant difference in means is observed between the groups ‘no disruption’ and ‘minor disruption’, see Table 4.6.

TABLE 4.6

Scenario Comparison (ANOVA – Games Howell Post Hoc analysis)

Manipulation Check (DV)

M diff. SDE Sig.

No disruption Minor disruption -0.02 0.38 0.998

Major disruption -1.40 0.32 0.001

Minor disruption No disruption 0.02 0.38 0.998

Major disruption -1.38 0.26 0.000

The scenario groups ‘no disruption’ and ‘minor disruption’ are not statistically different (p = .998). Additionally, the descriptive statistics for the ‘no disruption’ scenario indicates a relatively high mean and standard deviation (Mean = 3.04; SD = 1.45). Further analysis revealed that out of 23 participants a total of 12 participants in the ‘no disruption’ group indicated that they ‘somewhat’ or ‘strongly’ agreed that a major disruption occurred. This is the majority of this group which contradicts with the intention of this scenario. A minority of 9 (out of 23) participants indicated that they ‘somewhat or ‘strongly’ disagreed that a major disruption occurred at their organisation Voltage. This implies that participants in the ‘no disruption’ scenario perceived (to some extent) that their organization Voltage is in major disruption and that the organization is in danger considering business continuity. Thus, in the scenario group ‘no disruption’ it remains unclear for participants what the exact situation is. This may explain the high standard deviation of the manipulation check for this specific scenario (SD = 1.45). Finally, I conclude that the manipulations failed, and subsequently, no conclusions will be drawn based on the different scenarios. Instead of using the three distinctive scenarios, a single independent variable will be used: “perceived disruption impact”.

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23 impact and supply base size correlate (p = .035). In addition, there is a strong correlation between the perceived disruption impact and lead time of suppliers (p < .001). Alternatively, no correlation between perceived disruption impact and differentiation of the supply base is found (p = 0.53). Lastly, the control variables do not significantly correlate to the independent variable perceived disruption impact.

TABLE 4.7

Correlation Table (Pearson)

1 2 3 4 5 6 7

1. Perceived disruption impact

2. Size of supply base *.24

3. Differentiation supply base .22 **.52

4. Lead time of suppliers **.50 *.23 **.51

5. Risk taking propensity .01 *-.25 .09 .05

6. Age .15 -.01 .17 .20 .11

7. Work experience -.02 -.10 -.05 .10 .07 **.77

8. Gender .09 -.03 .09 .07 .20 **.30 .16

Note: *p < .05, **p < .01, two-tailed, N = 80. For gender, 1 = male and 2 = female.

4.5 Sample characteristics

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24

5. RESULTS

This section reports on the quantitative analysis following the scenario-based experiment. In particular, the developed multiple linear regression models, and the inferential statistics derived from these models are presented.

5.1 Multiple linear regression models

Multiple linear regression is performed to evaluate the effect that perceived supply disruption impact has on the supply base complexity decision-making variables. Three different models are constructed to test the hypotheses. Since the hypotheses are directional formulated (i.e. a decrease of supply base complexity is expected), all models use one-tailed tests. Model 1 tests the effect that perceived disruption impact has on the size of the supply base while controlling for risk propensity, age, work experience, and gender. This model resulted in a significant result (F(5,73) = 2.05, p = 0.035. Model 2 tests the effect that perceived supply chain disruption impact has on the degree of differentiation in the supply base while controlling for risk propensity, age, work experience, and gender. Similarly, a significant result is found (F(5,73) = 2.09, p = .038. Lastly, model 3 tests the effect that perceived supply chain disruption impact has on the preferred lead time of suppliers in the supply base while controlling for risk propensity, age, work experience, and gender. Also this last model provides significant results (F(5,73) = 5.62, p < .001.

For each of these three models, the Beta coefficient is derived from the statistical tests which indicate the slope of the relationship between the variables. Additionally, if this coefficient indicates is a positive or negative relationship. Table 5.1 includes the Beta coefficients for each of the three models. Each of the three models indicates a negative relationship between the independent and dependent variable. The negative Beta coefficient of perceived disruption impact in model 1 (p = .03) and model 3 (p < .001) are significant. The Beta coefficient in model 2 is nonsignificant (p = .21).

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TABLE 5.1

Coefficients of Multiple Linear Regression Models

Model 1 Model 2 Model 3

Beta t-value Sig. Beta t-value Sig. Beta t-value Sig.

Variable statistics

Perceived disruption impact -.16* -1.91 .03 -.07 -.21 .21 -.18** -4.64 .00

Age -.01 -.65 .26 -.03* -2.30 .02 -.01 -1.03 .16 Work experience .02 .96 .17 .03* 2.16 .02 .00 .12 .46 Risk-taking propensity .28* 2.23 .02 .02 .55 .29 .04 .25 .40 Gender -.01 .05 .48 -.05 -.15 .44 -.01 -.34 .37 Models statistics R .36 .35 .53 R² .128 .125 .278 F 2.05 2.09 5.61 p-value .035* .038* .000**

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Since there is one independent variable and there are four control variables in each of the regression models, it remains unclear what the contribution of the independent variable is to each multiple regression model. The variance explained from the independent variable could simply be calculated by detracting the R² of the control variables model from the R² of the complete/overall model. Therefore, three additional models are created that only include the control variables in the regression analysis, see the results for the R² statistics in Appendix E. Detracting the variance that is contributed by the control variables from the overall regression models result in Table 5.2. These results imply the total amount of variance that can be explained by the independent variable perceived disruption impact.

TABLE 5.2

Explained Variance Contributed by Perceived Disruption Impact

Model summary Model 1 Model 2 Model 3

R .067 .028 .273

R² .043 .019 .214

Sig. (p-value) .018* .027* .000**

Note: *p < .05, **p < .01, One-tailed, N = 80

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27

6. DISCUSSION

This section provides the interpretation of the results. Subsequently, the theoretical and managerial implications of these results are discussed. Finally, the limitations and opportunities for future research will be reviewed.

6.1 Interpretation of results

This research aims to study if there is a negative relationship between perceived disruption impact and risk-taking behaviour. Specifically, risk-taking behaviour is represented by the supply base complexity decisions. This study demonstrates a moderate to a strong correlation between perceived disruption impact and the supply base complexity variables. The multiple regression analysis supports H1 (p < .05) and H3 (p < .01) with statistical significance. H2 is not supported (p > .05), see Table 6.1.

TABLE 6.1 Hypotheses Results

Hypothesis Result

H1. Supply chain managers are more risk-seeking when they perceive

a disruption as high impactful, and therefore are more likely to decrease their total number of suppliers in the supply base.

Supported

H2. Supply chain managers are more risk-seeking when they perceive

a disruption as high impactful, and therefore are more likely to decrease the degree of differentiation in their supply base

Not supported

H3. Despite that supply chain managers are risk-seeking when they

perceive a disruption as high impactful, they will prefer a decrease of delivery lead time in their supply base

Supported

All models indicate a negative relationship between perceived disruption impact and the supply base complexity variables, see Figure 6.1, 6.2 and 6.3. Implying that for every increased ‘unit’ of perceived disruption impact, the supply base complexity variables decrease by the beta coefficients presented.

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28 work experience impact this overall model substantially, see Figure 6.2. Therefore, this study only finds strong evidence to reject the H1 and H3 null hypothesis in favour of the alternative hypotheses.

FIGURE 6.1 Regression Model 1 (Size)

FIGURE 6.2

Regression Model 2 (Differentiation)

FIGURE 6.3

Regression Model 3 (Lead Time) *p < .05, **p < .01 dashed paths indicate nonsignificant results *p < .05, **p < .01 dashed paths indicate nonsignificant results

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29 6.2 Theoretical contributions

The first theoretical contribution of this study is that the perceived disruption impact is negatively related to supply base size (H1). The results indicate that following a disruption, managers will prefer to downsize the supply base. Despite that this action of downsizing increases the probability of future supply chain disruptions (Sarkar & Mohapatra, 2009). Additionally, a recent study of Chae, Mena, Polyviou, Rogers and Wiedmer (2019) conclude that political disruptive events (e.g. import tariffs) may lead to buyer-supplier relationship dissolution. Suggesting that, following a political disruption, buyers search for other suppliers or use fewer existing suppliers in their supply base. This supports my finding that disruption leads to a decreasing supply base size. However, it contradicts to what existing risk mitigation literature suggests. To be specific, an increasing number of suppliers would make the organisation more robust to disruptions (Kleindorfer & Saad, 2009; Sheffi, 2001). Nevertheless, based on the results of this research the behavioural tendency of individuals is to decrease the supply base size.

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30 The last contribution of this study is establishing a new theoretical perspective by studying the effect of perceived disruption impact on supply base complexity decision making. The prior two contributions both implicate that a supply chain disruption triggers structural changes in the (upstream) supply chain. This perspective is deviating from previous research that uses supply base complexity as the predictor variable of the frequency of disruptions (Bode & Wagner, 2015; Brandon-Jones et al., 2015) and disruption severity (Craighead et al., 2007). This research provides a new theoretical perspective aside from the perspective that disruptions itself have an impact on the structure of a supply base.

6.3 Managerial implications

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31 a disruption. They may design their supply chain in a way by considering that they are prone to this risk-taking bias. In other words, managers may consider to (pro-actively) design their supply chain in the simplest manner to prevent risk-taking behaviour following a disruption regarding the supply base (i.e. reduce supply base size). However, it is important to mention that this design should be within the framework and limits of their business model.

6.4 Limitations and opportunities for future research

This study provides a new perspective on how perceived disruptions impact influences supply base complexity. However, certain limitations and opportunities for future research are addressed.

First, the explained variance of the hypothesized models ranges from 2% up to 21%, see Table 5.2. This indicates that the perceived disruption impact only limitedly explains supply base complexity decision-making following a disruption. Future research could study other variables that affect the supply base complexity.

Secondly, the different scenario’s in the experiment may be perceived as simplistic by participants. The scenario does not entail disruption characteristics such as the length (in time) of the disruptive event (cf. Sarafan et al., 2020). In addition, Svensson (2000) concludes that close relationships between buyer and supplier could diminish supply chain vulnerability to disruptions. However, in this experiment, no information is provided to participants regarding the relationship with existing suppliers. Moreover, Ellis, Henry and Shockley (2010) state that risk perceptions evolve from the likelihood, the magnitude and the overall supply risk of a supply chain disruption. In this research, only the magnitude of the disruptive event in the vignettes is included. The likelihood of a future disruption event is not mentioned. This could be of importance in the decision-making process, and therefore future research could explicitly incorporate this component.

Thirdly, the manipulation test failed and made cross-comparison between the different scenario groups not appropriate. Pilot testing would potentially indicate this limitation in an early phase. Pilot testing is suggested by Rungtusanatham et al. (2011), who incorporated pilot testing as an essential step in the post-design phase (vignette validation) of scenario-based experiments. A possible explanation for the failed manipulation is that there is information in the general introduction part that confused participants. More specifically, this sentence: “This morning

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32

are, but you recognize that the supplier is fully accountable for this supply disruption.” In this

introduction, I clearly refer to a disruptive situation. Yet, in the third scenario, I explicitly mentioned that no disruption occurred after all due to a communication error. However, this mixed information may have influenced the participants undesirably.

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33

7. CONCLUSION

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34

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