The Relationship between Distance and Risk Perception in Multi-Tier Supply Chain: The Psychological Typhoon Eye Effect

Abstract

Previous research has shown that an individual’s proximity to the epicenter can influence their perception and response to risk. However, this aspect has been largely overlooked in the supply chain risk literature. This paper aims to fill this gap by investigating the impact of distance on the perception and response to supply chain disruption risk. An online survey was conducted with 1055 managers working within the supply chain of ZTE, a Chinese multinational company providing integrated communications and information solutions. The survey aimed to examine how their distance from the disruption epicenter (i.e., ZTE) affected their risk perception and subsequent managerial responses. The findings indicate that those closer to the epicenter perceive a lower risk of disruption compared to those farther away, resulting in a reduced likelihood of taking management action. This phenomenon is referred to as the “psychological typhoon eye” (PTE) effect in supply chain disruption risk. Further analysis revealed that risk information quality mediated the relationship between distance and risk perception, while an individual’s job position level moderated the relationship between risk information quality and disruption risk perception. To mitigate the PTE effect in the multi-tier supply chain, the focal firm must prioritize high-quality information synchronization, extending beyond single-company initiatives.

1. Introduction

Today’s supply chains are increasingly vulnerable to disruption risk due to operational shutdowns directly or indirectly caused by a wide array of events, including climatological disasters, epidemics, political conflicts, terrorism, and financial scams [1,2]. To minimize the high costs of supply chain disruptions, firms need to actively access and manage the direct or indirect disruption risks from upstream or downstream of the supply chain [3]. A significant number of research studies on supply chain disruption risk management have been carried out to date [4], and most of the literature has assumed that supply chain managers can make optimal decisions based on objective risk assessment [5]. However, research has found that intuitive or emotional responses play a key role in human decision-making, leading people to make biased decisions that systematically deviate from rational judgment [6,7]. In addition, people usually rely on heuristic strategies (such as availability heuristics, anchoring heuristics, and representativeness heuristics) rather than a rational model to make judgments and decisions under uncertainty because risks cannot be accurately assessed [8]. Managerial responses to supply chain disruptions are triggered by managers’ risk perceptions rather than by the disruptions themselves [9,10]. Moreover, in decision-making, managerial risk perceptions are more influential than purely objective risk assessments alone [11]. Disruption risk perception refers to an individual manager’s subjective assessment of the risk inherent in a disruption [12]. In recent years, there has been a surge in academic research on supply chain disruption risk perception [10], with these studies underscoring the importance of risk perception in supply chain risk decision-making. However, a thorough review reveals that most of the existing literature focuses on buyers’ perceptions of supply disruption risk within two-tier supply chains (i.e., a dyadic relationship between supplier and buyer), while a supply chain typically consists of a focal firm and numerous upstream and downstream members in a multi-tiered structure.

When the focal firm of a supply chain experiences a disruption event or faces disruption risks (as illustrated in Figure 1), its upstream and downstream members may be directly (tier-1 suppliers/customers) or indirectly (suppliers/customers at tier-2 and beyond) impacted [13]. For instance, when the Hynix memory maker in China experienced a fire, computer manufacturers and parts suppliers perceived the risk and quickly purchased as much inventory as possible to secure better prices, which pushed up prices and created shortages [13]. Consequently, it is essential to understand not only how managers of the focal firm perceive its disruption risk but also how managers of upstream and downstream firms in the supply chain perceive the disruption risk.

Subjective risk perception is not a direct reflection of objective danger or threat (such as supply chain disruption), and individuals perceive the same danger differently [14,15]. Intuitively, closer proximity to the epicenter correlates with a higher perceived risk of the threat. However, research has demonstrated that this is not always the case; field evidence indicates that individuals closer to the epicenter are often less worried or fearful than those farther away. This phenomenon is known as the “psychological typhoon eye” (PTE) effect, as the epicenter of a typhoon is relatively calm. The phenomenon of the PTE effect highlights the intricate interplay between risk perception and distance, which encompasses various aspects such as the geographical distance from the epicenter [16,17], interpersonal relationship distance from the affected party [18], and the level of involvement in the threat of danger [19]. Hence, the primary objective of the present study is to gain insights into the impact of distance on managers’ perception of supply chain disruption risk. We build on the literature to investigate whether another version of the PTE effect (i.e., “supply chain distance” version) exists in the context of supply chain disruption risk perception. In this study, supply chain distance is conceptualized as the number of tiers between the disrupted firm and its upstream or downstream members. For instance, the supply chain distance between the disrupted firm and its first-tier suppliers/customers is 1, the distance between the disrupted firm and its second-tier suppliers/customers is 2, and so on (as depicted in Figure 1).

Numerous studies have demonstrated that risk information significantly influences risk perception (e.g., [20,21]). People’s perceptions of risks are shaped by the information available to them [22]. Firms facing greater environmental uncertainty must gather and process more information. Managerial perceptions of disruption risk depend on comprehensive and high-quality information about the disruption, and managers utilize relevant information to assess disruption risk and make decisions [23]. As a result, disseminating relevant information about the disruption to supply chain partners has become a critical risk mitigation strategy [24]. Accordingly, research on the role of available risk-related information in supply chain managers’ disruption risk perception is anticipated to raise intriguing questions about risk perception mechanisms that warrant further investigation. Therefore, the second aim of the present study is to empirically test whether the risk information can account for the supply chain distance version of the PTE effect.

Job position level can be another important factor affecting risk perception in the context of supply chain disruptions. Higher-level managers may possess a more expansive view of the supply chain and greater experience with potential disruptions, enabling them to better identify risks and threats [25]. In contrast, lower-level managers may have a more limited perspective and less experience with supply chain disruptions, leading to a decreased sensitivity to disruption risks. Additionally, higher-level managers may be responsible for making strategic decisions that have a greater impact on the organization, leading to more cautious decision-making in the face of disruption risks [26]. However, it is also possible that top managers perceive the risks as less threatening because they have more resources and support. Based on the above analysis, it is worth studying whether job position level will affect the relationship between risk information and managers’ risk perception, which is also the third research objective of the present study.

Given the above, this study will investigate the following research questions:

RQ1:If a focal firm in a multi-tier supply chain is at risk of disruption, does a PTE effect exist in the perception of this risk among managers in different tiers of the supply chain?

RQ2:What role does risk information play in the perception of disruption risk among supply chain managers?

RQ3:Does job position level moderate the relationship between risk information and disruption risk perception?

The answers to these questions will not only extend the existing literature by facilitating the understanding of managers’ risk perception mechanisms in the context of multi-tier supply chains but also help broaden the application of psychological theory in operations and supply chain management.

The remainder of this study is structured as follows: We first review the extant research and develop our theoretical model and hypotheses. We then overview the methodology, statistical analyses, and findings. Finally, we highlight the academic and managerial implications of our finds, the limitations of the study, and opportunities for future research.

2. Theoretical Constructs and Hypotheses

2.1. Effect of Distance upon Risk Perception

Risk refers to the uncertainty and potential severity of consequences associated with an activity that is valued by humans. Risk perception refers to the subjective judgment of individuals regarding such risks. Traditionally, risk perception is measured by multiplying the probabilities of risk occurrence by the magnitude of the risk impact [27]. This method is considered rational, yet it has limitations. Sociologists and psychologists have shown that laypeople tend to perceive risk based on emotions, intuition, and direct judgment, whereas a rational risk assessment is typically processed by experts [28]. These emotional and intuitive perceptions of risk can be irrational and influenced by factors such as risk descriptions, previous experiences, effect, imagination, trust, values, and worldviews [29].

Recent behavioral supply chain research has only touched on the linkages between various factors and how they impact people’s perception of and response to disruption risk. For example, Sarafan et al. demonstrate how individualism–collectivism negatively affects how individuals perceive risk and supplier-switching intention in the face of a supply disruption [12]. Other researchers have emphasized the importance of attributions and emotions in explaining differences in managerial decisions following the occurrence of a disruption [30]. Vanpoucke and Ellis examine the relationship between buyers’ perceptions of disruption risk and their adoption of buffer- and process-oriented risk mitigation tactics [31]. These limited studies have contributed to our understanding of the psychological and social factors affecting people’s perception of disruption risk from the perspective of a one-tier supply chain, but little attention has been paid to the effect of distance on disruption risk perception. Therefore, our present research aims to explore how distance affects an individual’s perception of disruption risk in the context of a multi-tier supply chain.

There is an extensive body of literature demonstrating how distance affects individuals’ risk perception and behavior [32]. According to the PTE effect theory, people who are closer to the center of an adverse event are less concerned or fearful about the event. For example, Maderthaner et al. found that in a local attitude survey about a nuclear reactor in Vienna, residents living farther from the reactor perceived it to be riskier than those living closer [33]. Tilt discovered that industrial workers who labored under highly polluted conditions provided lower risk ratings than farmers and commercial/service sector workers who were farther from the polluting sources [34]. In a study by Li et al., a convenience sample of 2262 adults was surveyed about their post-earthquake concerns regarding safety and health after the Wenchuan earthquake, and their findings suggested that people who were farther from the earthquake area (i.e., more remote) were more likely to have a higher estimation of their post-earthquake concern [16]. During the SARS epidemic, it was reported that the level of exposure to SARS was not a primary determinant of experienced anxiety, and nearness to the center of the epidemic was negatively related to anxiety levels [35]. Similarly, studies conducted during the COVID-19 pandemic have come to similar conclusions (e.g., [17,36]).

Within the context of the supply chain disruption risk, the disrupted firm is the epicenter of the risk (as the manufacturer in Figure 1), and the upstream and downstream supply chain members’ reactions and responses to the disruption risk are enhanced with the increment of supply chain distance, which increases the negative effect of the disruption risk [13]. Previous studies have shown that individual managers’ disruption risk perception has a positive correlation with their reactions and responses to the risk [12,37,38]. This implies that the supply chain members’ level of risk perception may also increase with the increment of supply chain distance.

Therefore, we hypothesize that:

Hypothesis 1 (H1):

The distance between managers’ firms from the disrupted firm and their levels of disruption risk perception are positively related.

H1 posits that as the upstream and downstream firms in the supply chain move closer to the disrupted firm, their concern regarding the risk associated with the disruption of the focal firm decreases. In other words, when the focal enterprise in the supply chain faces a disruption risk, the perception of managers at different tiers of the supply chain towards this risk is influenced by the proximity of their firms to the focal firm, demonstrating the presence of the PTE effect.

2.2. The Mediating Role of Available Risk Information

Previous studies have shown that people’s perceptions about any risk are shaped by the information available to them [22]. In most cases, individuals tend to perceive a greater risk when they have more knowledge about the adverse event, as they are aware that its consequences could be severe [39]. However, in many cases, people perceived less risk when they had sufficient information about the adverse event, which was likely driven by familiarity bias [40]. That is, people who are more familiar with the risks are likely to perceive them as less frightening. As commonly acknowledged, individuals who are located at a considerable distance from the epicenter typically receive second-hand information regarding risks, while those who are in close proximity not only have access to first-hand risk-related information but also have their own direct experiences to draw upon. Not surprisingly, this leads to a difference in their perception of risk. Based on this, it is reasonable to speculate that the available risk-related information may be the underlying mechanism for the PTE effect.

Information quantity and information quality are two important aspects of available risk-related information [41]. More recently, Yang and colleagues discovered that the proportion of risk information (RIP) played a significant role in explaining the PTE effect concerning COVID-19 risk perception in Wuhan. Specifically, the RIP acts as a mediator between the respondents’ distance from Wuhan and their level of concern and perception of risk regarding the epidemic that took place in the city [42]. In their study, RIP was defined as the ratio of “the amount of information related to the occurrence of risk events in a certain area” and “the total amount of information about all events in a certain area”. Yang et al.’s research [42] can be seen as an explanation of the PTE effect mechanism in terms of the quantity dimension of the available information, while the present study attempts to examine whether another dimension of the available information, i.e., information quality, can explain the PTE effect in the supply chain disruption risk.

In the initial stages of a supply chain disruption risk, the available information is often uncertain and ambiguous. As individuals move closer to the epicenter of the risk, the information becomes more certain and less ambiguous [12,24]. This means that the disrupted firm’s tier-1 suppliers/customers can relatively easily obtain disruption risk-related information that is of high quality, but their distant (tier-2 and above) suppliers/customers cannot [43]; they only have indirect access to the disrupted firm’s second-hand information that is filtered, altered, and likely to be inaccurate via their partners, mass media, social media, etc. Additionally, firms typically postpone public announcements of disruptions [44], underreport, or hide disruption information [45], which reduces the level of quality of disruption information available to managers. Low information quality is characterized as delayed, incomplete, and ambiguous, which prevents supply chain managers from having a clear picture of what is actually happening in the disrupted firm [46], leading to supply chain managers far from the epicenter overestimating the disruption risk [47]. In other words, the PTE effect in the supply chain disruption risk may be caused by differences in the quality of risk information. That is, with the increment of managers’ distance to the disrupted firm, the quality of disruption risk information declines, which leads managers to overestimate the disruption risk.

Therefore, we formulate the following research hypothesis:

Hypothesis 2 (H2):

Risk information quality played a mediating role between distance and disruption risk perception.

2.3. The Moderating Role of Job Position Level

When people make judgments or decisions, they may be influenced by their prior beliefs, attitudes, and values [48]. Many studies have shown that people are susceptible to the “belief bias” effect and tend to accept or reject conclusions based on their consistency with everyday knowledge, regardless of whether these conclusions validly deviate from their premises [49,50,51]. Pre-existing beliefs can cause bias for people’s perception of risk, leading them to over- or underestimate the likelihood or severity of a risk based on their existing beliefs [52,53]. For example, a person who strongly believes in the safety of a particular technology may underestimate the risks associated with that technology, while a person who strongly opposes that technology may overestimate the risks.

Most non-experts lack professional expertise and enough experience to assess risk [29]. As a result, they often rely on various cues available to them to aid in their judgment and decision-making [21]. Experts, because of their training and experience, are more likely to have knowledge (i.e., expertise and experience) about a certain hazard or adverse event unavailable to the average citizen. Therefore, experts do not need too much information about the adverse event in order to make their risk assessments [54]. The role of a supply chain manager is highly analytical and typically involves tasks such as planning, scheduling, and coordinating supplies [38]. High-level supply chain managers have a large amount of knowledge and experience in the field of supply chain risk management [55]. The more knowledge and expertise they have about disruption risks, the more they feel certain about their risk assessments, and the less disruption risk information from external sources they use in the assessment process [23]. Based on this, it is reasonable to speculate that top managers’ perception of disruption risk is less affected by risk information quality than lower-level managers. Therefore, we hypothesize that:

Hypothesis 3 (H3):

Job position level moderates the relationship between risk information quality and disruption risk perception. Specifically, the effect of risk information quality on disruption risk perception gets stronger among low-level managers, but it is attenuated among high-level managers.

2.4. Perceived Risk Influences Individuals’ Response

The risky decision-making theory provides an explanation for the relationship between managerial risk perceptions and their subsequent behavioral response in the face of a supply chain disruption [56]. This response includes the actions taken by managers to minimize the impact of disruption. Zsidisin and Wagner demonstrated that managers who perceive the extended supply chain as a potential risk source are more likely to take action to mitigate such risks [57]. Meanwhile, Ellis et al. found that buyers who perceive high levels of overall supply disruption risk tend to seek alternative sources of supply to mitigate such risks [37]. In addition, Kull et al. revealed that cognitive and behavioral factors, inducing risk perceptions in uncertain supplier selection situations, can lead to a higher preference for suppliers with more certain outcomes [38]. Sarafan et al. conducted a scenario-based experiment to investigate the effect of cultural value orientations on individuals’ perception of risk and supplier-switching intention in the face of a supply disruption. They found that higher levels of disruption risk perception led to significantly higher supplier switching intention [12].

Therefore, we follow previous studies by offering the following hypothesis:

Hypothesis 4 (H4):

Higher perceived disruption risk is associated with a higher propensity to take action in the face of supply chain disruption.

Based on the above analysis, we depict the conceptual research model in Figure 2.

3. Methodology

3.1. Empirical Study Setting

To test the above hypothesis, we selected an information and communication technology supply chain with ZTE as the focal firm and conducted a scenario-based empirical investigation according to the similarity study [37,58]. Specifically, we focused on managers of ZTE and its upstream and downstream firms in the supply chain and how to perceive ZTE’s disruption risk. This study setting is suitable for the hypotheses test for three reasons. First, more and more companies, such as telecom equipment makers, are at a high level of disruption risk caused by adverse events such as geostrategic conflict or COVID-19. Second, the company had been barred by the U.S. Commerce Department from purchasing components from American companies in April 2018; therefore, professionals from the ZTE supply chain have a better understanding of ZTE’s disruption risk and risky decision-making. Third, ZTE is a prominent global telecom equipment manufacturer that strives to deliver cutting-edge technologies and comprehensive solutions to a diverse clientele comprising governments, enterprises, and consumers in over 160 countries. Due to ZTE’s vast network of upstream and downstream companies, it is convenient for us to recruit professionals affiliated with ZTE and its partners as research respondents.

3.2. Respondents and Data Collection

The survey was conducted on a computerized response system to facilitate the completion and collection of data throughout China from March to November 2020. We invited professionals from upstream and downstream firms of the ZTE supply chain through the authors’ personal social networks, CFLP (China Federation of Logistics & Purchasing), and a web-based surveys platform (www.wjx.cn, accessed on 4 March 2020). A total of 1735 respondents engaged in our survey. Respondents were first asked to confirm their network position in the ZTE supply chain (i.e., ZTE’s first-tier supplier/customer is 1, second-tier supplier/customer is 2) and then answer a 24-item questionnaire on their perceived psychological distances of ZTE’s disruption, subjective perception of ZTE’s disruption risk, disruption risk information quality, managerial response, risk propensity, and demographic characteristics.

Finally, we received 1055 usable responses, and all questionnaires were valid because we set the online survey system so it did not allow missing data. Among the sampled firms, there were 295 from ZTE Corporation, 359 from ZTE’s upstream suppliers (tier-1 and tier-2 suppliers), and 401 from ZTE’s downstream customers (tier-1 wholesalers and tier-2 wholesalers/retailers). More than half of sampled firms are medium and large enterprises. Characteristics of the sampled firms are presented in

Table 1. Characteristics of sampled firms (N = 1055).

		Frequency	Percentage (%)
Supply chain position	ZTE corporation	295	28.0
	Tier-1 suppliers	193	18.3
	Tier-2 suppliers	166	15.7
	Tier-1 wholesalers	207	19.6
	Tier-2 wholesalers/retailers	194	18.4
Number of employees	≤10	38	3.6
	11–50	166	15.7
	51–100	250	23.7
	101–500	335	31.8
	501–1000	129	12.2
	≥1001	137	13.0
Annual sales revenue (CNY)	<1 million	137	13.0
	1–4.99 million	246	23.3
	5–9.99 million	233	22.1
	10–49.99 million	188	17.8
	≥50 million	251	23.8

.

It is noteworthy that the participants from ZTE Corporation not only serve as its employees but also function as organizers and managers of the supply chain. As a result, they hold a central position in relation to disruption risk and experience it directly, thereby being the most directly impacted stakeholders. ZTE employees acquire pertinent information regarding disruption directly, and function as disseminators of disruption risk information. They communicate information pertaining to disruption risk through various channels, such as news media, partners, and social networks.

To ensure the external validity of results [38], the overwhelming majority of participants had more than three years of experience in related operations or supply chain management areas, approximately 52.5% of the respondents were men, and over 80% of respondents held a bachelor’s degree or above. The detailed demographic information on the respondents is presented in

Table 2. Demographic data in the surveys (N = 1055).

		Frequency	Percentage (%)
Gender	Male	554	52.5
	Female	501	47.5
Age	≤30	404	38.3
	31–40	423	40.1
	41–50	160	15.2
	51–60	64	6.1
	>60	4	0.4
Education level	Secondary education certificate	5	0.5
	Senior school diploma	49	4.6
	Three-year college diploma	178	16.9
	Bachelor’s degree	691	65.5
	Graduate degree	132	12.5
Work experience	<3 years	155	14.7
	3–5 years	222	21.0
	6–10 years	295	28.0
	11–20 years	219	20.8
	>20 years	164	15.5
Job Function	Planning and purchasing	313	29.7
	Operations and production	233	22.1
	Warehousing and logistics	179	17.0
	Research and development	25	2.4
	Sales and marketing	305	28.9
Job position level	Executive-level manager	148	14.0
	Middle-level manager	247	23.4
	Low-level manager	326	30.9
	Ordinary employee	334	31.7

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It was found that the average age of ordinary employees was 32.14 years old, with more than 80% having over 3 years of work experience. Junior managers had an average age of 34.35 years, with 51.3% having more than 5 years of work experience. Middle managers had an average age of 38.6 years, with 22.3% having more than 10 years of work experience. Top managers had an average age of 47.26 years, with 29.1% having more than 20 years of work experience.

3.3. Measure Development

We conducted a comprehensive review of the literature on supply chain disruption, risk perception, and behavioral decision-making to establish operational definitions and survey measurement items. To ensure content validity, we adapted items from previous studies to our research setting when applicable. The participants were asked to rate their level of agreement on a 10-point scale (1 = Strongly disagree, to 10 = Strongly agree) in response to a series of statements concerning their work experience and principles.

3.3.1. Dependent Variable

The dependent variable includes the focal dependent variable and the ultimate dependent variable. The focal dependent variable is disruption risk perception, and the ultimate dependent variable is the managerial response to ZTE’s supply chain disruption.

Disruption risk perception (DRP). We employed the psychometric paradigm as a research framework to measure the risk perception of ZTE’s supply chain disruption. We adopted three items from Xie et al. [35] and Zheng et al. [19] to measure the disruption risk. The participants were asked if they thought the negative performance impact caused by ZTE’s supply chain disruption was serious, dreadful, and uncontrollable. The statements of the three items are as follows: serious, “We will face a severe threat caused by ZTE’s supply chain disruption” (DRP1); dreadful, “We extremely concern about the threat to my company caused by ZTE’s supply chain disruption” (DRP2); uncontrollable, “We are unable to avoid the threat caused by ZTE’s supply chain disruption” (DRP3). The higher the values, the higher level of risk they perceived from ZTE’s supply chain disruption.

Managerial response to ZTE’s supply chain disruption (MR). We developed a three-item scale to measure respondents’ managerial response regarding ZTE’s supply chain disruption based on previous studies [37,59]. The items are: “We will take action immediately to ZTE’s supply chain disruption” (MR1); “We will take effective measures to ZTE’s supply chain disruption” (MR2); “We will undertake an adequate response to ZTE’s supply chain disruption” (MR3). Respondents were asked to rate each item based on the degree to which they agreed with the statement. The higher the values, the more propensity to take action regarding ZTE’s supply chain disruption.

3.3.2. Independent Variable

Supply chain distance. Supply chain distance was determined by the network position of the respondent’s company in ZTE’s supply chain and was an objective distance. At the beginning of the questionnaire, respondents were asked to confirm their company’s role in the ZTE supply chain: the supply chain distance of ZTE’s first-tier supplier/customer is 1, the second-tier supplier/customer’s supply chain distance is 2, and so on.

In addition to the objective distance (supply chain distance), we also measured the subjective distance between the respondents and ZTE’s disruption risk, which was operationalized as psychological distance. In a range of risk domains—from climate change to nuclear energy, from food safety to health—the association of psychological distance on an individual’s perception and response to risk has been proven to be robust [60,61,62].

Psychological distance (PD). We created a set of four items to assess the participants’ perception of the psychological distance of ZTE’s disruption. This includes spatial distance, temporal distance, social distance, and hypotheticality [63]. In this present study, spatial distance is not the geographical distance between the supply chain actor and the location where a disruption triggers but the supply chain distance instead. Supply chain distance was defined as the “distance between actor and the disruptive incidents, or ‘position’ of an actor in a supply chain network” according to Birkie and Trucco [64] and Ozkul and Barut [65]. The distance was estimated subjectively by the participants, so it also can be called subjective supply chain distance. Therefore, spatial distance (i.e., supply chain distance) was measured by asking “My company’s ‘position’ in the ZTE’s supply chain network determines we are far from ZTE’s disruption” (PD1). The items used to measure temporal and social distance were based on Spaccatini et al.’s work [32], and the item used to measure the hypotheticality of ZTE’s supply chain disruption was based on Ellis et al.’s work [37]. Social distance was measured with “ZTE’s supply chain disruption will have a little impact on my company” (PD2). Hypotheticality was measured by asking “There is a low probability that ZTE will experience a supply chain disruption” (PD3). The temporal distance was measured with “If ZTE’s supply chain would be disrupted by adverse events such as COVID-19 and U.S.-China conflict, that will be something for a long time to come” [66] (PD4). Each of these four items measured a distinct dimension of psychological distance, with high values indicating greater psychological distance and low values indicating less psychological distance.

3.3.3. Mediate Variable

Perceived risk information quality (PRIQ). Timeliness, credibility, and being easily understandable are important dimensions of information quality, and they will affect the individual’s judgment and decision [67]. Therefore, higher risk information quality would make participants suffer from less risk information illusion. PRIQ was operationalized from four dimensions using four items, which refers to information that is readily accessible, timely, credible, and understandable [68]. They are: “Information about ZTE’s supply chain disruption is easy access” (PRIQ1); “Information about ZTE’s supply chain disruption is timely” (PRIQ2); “Information about ZTE’s supply chain disruption is credible” (PRIQ3); “Information about ZTE’s supply chain disruption is understandable” (PRIQ4). The higher values represented a lower degree of information illusion.

3.3.4. Moderate Variable

Job position level (JPL) includes four levels: executive-level manager, middle-level manager, low-level manager, and ordinary employee.

3.3.5. Control Variables

In addition to the above-mentioned key variables, respondents’ risk propensity (RPr) was measured for validation and control variables [12]. Four items for measuring participants’ risk propensity were chosen from the risk propensity scale developed by Hung and Tangpong [69]: “I like to take chances, although I may fail” (RPr1); “I like to try new things, knowing well that some of them will disappoint me” (RPr2); “To earn greater rewards, I am willing to take higher risks” (RPr3); “I seek new experiences even if their outcomes may be risky” (RPr4). The higher the values, the more likely individuals have a greater risk propensity. We also controlled a series of factors to maximize internal validity and rule out other explanations, such as age, gender, educational background, and work experience. Other than these individual-level control variables, we also controlled for the firm size (measured by the number of employees and annual sales revenue). All of the above control variables were kept as categorical variables.

3.4. Construct Validity and Reliability

Since some measurement items are used for the first time in the context of operational and supply chain management, we examined the reliability and validity of scales through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), as suggested by Anderson and Gerbing [70]. First, EFA is used to evaluate whether the measurement items are consistent with theoretical expectations, and then the internal structure of the scale is further confirmed by CFA.

We used SPSS 22.0 to perform the EFA. Results indicate the presence of five factors based on the criteria of eigenvalues greater than 1 (72.72% of total variance explained). The criterion we followed to determine whether to keep an item on a factor was that the item should have a loading of at least 0.40 on the primary factor and not have significant dual loadings (i.e., >0.30 on more than one factor) [71]. One item was problematic: PD4 had a loading of 0.542 on factor 2 and 0.559 on factor 4, and was excluded. Five factors and 16 items were prepared for the subsequent CFA, as shown in

Table 3. The items and factor loadings of the five-factor model.

Measurement Items	Factor 1 (PRIQ)	Factor 2 (MR)	Factor 3 (PD)	Factor 4 (DRP)	Factor 5 (RPr)
PRIQ 1	0.854	−0.029	−0.072	−0.252	0.095
PRIQ 2	0.858	0.089	−0.034	−0.202	0.047
PRIQ 3	0.898	0.013	−0.019	−0.118	0.073
PRIQ 4	0.719	−0.256	−0.014	−0.095	0.164
MR 1	−0.056	0.867	0.106	0.276	0.007
MR 2	−0.048	0.897	0.124	0.142	0.006
MR 3	−0.044	0.884	0.103	0.179	0.040
PD 1	−0.039	0.134	0.897	0.075	0.003
PD 2	−0.019	0.082	0.890	0.101	0.002
PD 3	−0.058	0.118	0.864	0.101	−0.012
PD 4	0.035	0.542	0.171	0.559	0.059
DRP 1	−0.260	0.158	0.048	0.731	−0.198
DRP 2	−0.268	0.239	0.131	0.774	−0.090
DRP 3	−0.251	0.229	0.108	0.785	−0.103
RPr 1	0.044	0.079	0.004	−0.212	0.709
RPr 2	0.084	0.038	−0.019	−0.060	0.787
RPr 3	0.068	0.129	0.024	−0.106	0.752
RPr4	0.142	−0.281	−0.019	0.115	0.637
Proportion variance (%)	29.246	16.525	10.832	10.122	5.993
Cumulative (%) of variance explained	29.246	45.771	56.603	66.725	72.718

.

To further assess the reliability and validity of the constructs, we performed CFA and employed four tests to evaluate the convergent validity and internal consistency of the reflective constructs: Cronbach’s alpha, average variance extracted (AVE), composite reliability (CR), and item loading of the measures. The goodness of fit of the measurement model was evaluated through five common indices, including the ratio of Chi-square to the degree of freedom (χ²/df), comparative fit index (CFI), the goodness of fit index (GFI), Tucker–Lewis index (TLI), and root mean square error of approximation (RMSEA). The CFA was conducted using AMOS 22.0 software [72]. The fit statistics indicated a satisfactory fit between the predicted and observed model with χ²/df = 4.80 [χ²(113) = 542.14 and p = 0.00], CFI = 0.96, GFI = 0.94, TLI = 0.95, and RMSEA = 0.06 [73,74]. As shown in

Table 4. Measures used in proposed constructs.

Construct	Item	Cronbach’s α	AVE	CR	Loading	t-Value	SE
Psychological Distance (PD)		0.88	0.71	0.88
	PD1				0.88	-	-
	PD2				0.84	31.35	0.03
	PD3				0.80	29.74	0.03
Perceived Risk Information Quality (PRIQ)		0.88	0.66	0.88
	PRIQ1				0.86	-	-
	PRIQ2				0.85	34.55	0.03
	PRIQ3				0.87	35.93	0.03
	PRIQ4				0.64	22.88	0.03
Risk Propensity (RPr)		0.70	0.40	0.72
	RPr1				0.61	-	-
	RPr2				0.74	15.12	0.06
	RPr3				0.69	15.05	0.06
	RPr4				0.43	10.97	0.06
Disruption Risk Perception (DPR)		0.83	0.63	0.83
	DRP1				0.70	-	-
	DRP2				0.84	23.47	0.05
	DRP3				0.83	23.36	0.05
Managerial Response (MR)		0.92	0.81	0.93
	MR1				0.89	-	-
	MR2				0.90	41.05	0.03
	MR3				0.91	41.76	0.03

, all Cronbach’s alpha and CR statistics exceeded the 0.7 cut-off recognized in the literature, suggesting good construct reliability. The convergent validity of our multi-item scales is adequate since the AVE was larger than 0.5 and most items had factor loading exceeding 0.7 on their construct [37,75]. We used Harman’s one-factor test to assess the presence of common method variance (CMV). This EFA result indicated that CMV was not a potential issue in this study [76]. Moreover, discriminant validity was assessed by comparing the correlation coefficient of each construct with other constructs to the square root of its AVE. Our findings indicated that the square root of AVE for each construct was greater than its correlation coefficient with other constructs, which supported the discriminant validity of our measures [75].

4. Results

Data analysis consisted of three steps. In the first step, we performed descriptive statistics and examined Pearson’s product–moment correlations among sociodemographic characteristics, firm size, and the five factors in Table 4, which include psychological distance, disruption risk information quality, disruption risk perception, risk propensity, and managerial response. The second step was a mediation analysis: to investigate the indirect effect of distance on disruption risk perception via disruption risk information quality. The third step was a multivariate analysis: we conducted hierarchical moderated regression analyses in order to test the moderating effect of risk propensity and job position level.

4.1. Descriptive Analyses

Table 5. Means, SDs, and pairwise correlations of the measures.

	M	SD	1	2	3	4	5	6	7	8	9	10	11
1 Gender	0.47	0.50
2 Age	1.90	0.90	0.12 ***
3 Educational background	3.85	0.71	0.07 *	−0.10 **
4 Work experience	3.01	1.28	0.17 ***	0.82 ***	−0.15 ***
5 Job position level	2.20	1.04	−0.04	0.00	0.03	0.09 **
6 Number of employees	3.72	1.33	−0.02	−0.04	0.25 ***	−0.05	0.01
7 Annual sales revenue	3.16	1.36	0.03	−0.10 **	0.23 ***	−0.07 *	0.02	0.67 **
8 Risk propensity	5.02	1.37	0.05	−0.02	0.04	−0.03	0.04	−0.02	-0.05
9 Psychological distance	5.94	1.92	−0.01	−0.19 ***	0.08 **	−0.19 ***	0.04	0.05	0.09 **	−0.02
10 Perceived risk information quality	4.88	1.68	0.01	0.09 **	0.04	0.11 ***	−0.02	−0.02	−0.07 *	0.24 ***	−0.11***
11 Disruption risk perception	6.39	1.98	0.03	−0.18 ***	0.11 ***	−0.18 ***	−0.04	0.11 ***	0.18 ***	−0.27 ***	0.24 ***	−0.48 ***
12 Managerial response	5.45	1.93	−0.00	−0.18 ***	0.08 **	−0.17 ***	0.04	0.08 *	0.14 ***	−0.02	0.26 ***	−0.14 ***	0.44 ***

Note: M = mean; SD = standard deviation. Variables were coded as follows—Gender: 1 = female, 0 = male; Age: 1 = below 30 years old, 2 = 31–40 years old, 3 = 41–50 years old, 4 = 51–60 years old, 5 = above 61 years old; Education: 1 = secondary education certificate, 2 = senior school diploma, 3 = three-year college diploma, 4 = bachelor’s degree, 5 = graduate degree; Work experience: 1 = less than 3 years, 2 = 3–5 years, 3 = 6–10 years, 4 = 11–20 years, 5 = more than 20 years; Job position level: 1 = ordinary employee, 2 = low-level manager, 3 = middle-level manager, 4 = executive-level manager; Number of employees: 1 = fewer than 10 employees, 2 = 11–50 employees, 3 = 51–100 employees, 4 = 101–500 employees, 5 = 501–1000 employees, 6 = more than 1001 employees; Annual sales revenue: 1 = fewer than CNY 1 million, 2 = CNY 1–4.99 million, 3 = CNY 5–9.99 million, 4 = CNY 10–49.99 million, 5 = more than CNY 50 million. * p < 0.05, ** p < 0.01, *** p < 0.001.

displays the variable means, standard deviations, and correlation coefficients between variables for this study. The demographic profile of the respondents was shown in the first five rows of the table.

4.2. Direct Effect of Distance on Disruption Risk Perception

4.2.1. Supply Chain Distance Affects Disruption Risk Perception

The risk perception ratings of the participants varied significantly depending on their supply chain distance (F (4, 1055) = 41.16, p < 0.001, and η² = 0.04, by ANOVA). The scores for their risk perception from lowest to highest in the ZTE supply chain were: ZTE, ZTE’s tier-1 supplier/customer, and ZTE’s tier-2 supplier/customer (see Figure 3). Fisher’s least significant difference (LSD) post hoc test further revealed that the ZTE group reported the lowest risk perception (M = 5.87, SD = 2.03), significantly lower than the ratings given by tier-1 suppliers (M = 6.54, SD = 1.93) and tier-2 suppliers (M = 7.00, SD = 1.68) (F (2, 693) = 9.45, p <0.001, η² = 0.03), which was also significantly lower than the ratings given by tier-1 customers (M = 6.26, SD = 2.13) and tier-2 customers (M = 6.67, SD = 1.82) (F (2, 651) = 19.68, p <0.01, η² = 0.06). In the upstream, tier-2 suppliers perceived more risk than tier-1 suppliers (p < 0.05). In the downstream, similarly, tier-2 customers perceived more risk than tier-1 customers (p < 0.05). However, the rated risk perception between tier-1 suppliers and tier-1 customers had no significant difference (p > 0.05); likewise, no significant difference was found between tier-2 suppliers and tier-2 customers (p > 0.05).

The above results indicated that the greater the supply chain distance, the higher the risk perception toward ZTE’s disruption risk. That is, in the supply chain with ZTE as the focal firm, the farther away the upstream or downstream members were from ZTE, the higher the risk they perceived from ZTE’s supply chain disruption. The hypothesized “supply chain distance” version of the PTE effect was thus observed in the present study. Therefore, the results supported H1.

4.2.2. Psychological Distance Affects Disruption Risk Perception

We respectively conducted a hierarchical regression analysis to reveal the impact of psychological distance on disruption risk perception upstream and downstream of the ZTE supply chain. Participants’ age, gender, educational background, work experience, and firm size were entered as control variables in this analysis. Upstream supply chain members consist of ZTE, tier-1 suppliers, and tier-2 suppliers; downstream supply chain members consist of ZTE, tier-1 customers, and tier-2 customers.

The results of the hierarchical regression analysis for the upstream of ZTE’s supply chain are presented in

Table 6. Hierarchical regression analysis of demographical variables and psychological distance on disruption risk perception in the upstream of ZTE’s supply chain (N = 654).

Variable	Model 1			Model 2
	B	SE	β	B	SE	β
Step 1
(Constant)	7.257 ***	0.534		5.643 ***	0.576
Gender	0.009	0.145	0.002	−0.008	0.141	−0.002
Age	−0.084	0.142	−0.038	−0.036	0.138	−0.016
Educational background	0.243 *	0.107	0.086	0.240 *	0.104	0.085
Work experience	−0.239 *	0.100	−0.155	−0.200 *	0.097	−0.129
Risk propensity	−0.392 ***	0.051	−0.278	−0.377 ***	0.049	−0.268
Number of employees	0.061	0.075	0.040	0.063	0.073	0.041
Annual sales revenue	0.232 ***	0.072	0.160	0.213 ***	0.070	0.147
Step 2
Psychological distance				0.233 ***	0.036	0.228
F value			18.879			22.659
R2			0.170			0.219
Adj. R2			0.161			0.210
∆Adj R 2			0.170			0.050

Note: * p < 0.05, *** p < 0.001.

. When all variables were included in the model, it accounted for 21.9% of the variance in the perception of a higher risk for ZTE’s disruption. In model 1, demographical variables were entered as controls; the overall model was significant, and R² = 0.170, F (7, 646) = 18.879, p < 0.001. In model 2, the psychological distance was entered as a predictor; the overall model remained significant, and R² = 0.219, F (8, 645) = 22.659, p < 0.001. The psychological distance was found to be a significant predictor of disruption risk perception (B = 0.233, p < 0.001).

We conducted the same regression analysis to analyze the effect in the downstream of ZTE supply chain, and the results are shown in

Table 7. Hierarchical regression analysis of demographical variables and psychological distance on disruption risk perception in the downstream of ZTE’s supply chain (N = 696).

Variable	Model 1			Model 2
	B	SE	β	B	SE	β
Step 1
(Constant)	8.233 ***	0.542		7.139 ***	0.579
Gender	0.438 **	0.148	0.108	0.440 **	0.146	0.108
Age	−0.223	0.139	−0.099	−0.179	0.137	−0.080
Educational background	0.127	0.105	0.045	0.099	0.103	0.035
Work experience	−0.199 *	0.101	−0.124	−0.172 *	0.099	−0.107
Risk propensity	−0.383 ***	0.054	−0.256	−0.382 ***	0.053	−0.255
Number of employees	−0.080	0.071	−0.054	−0.069	0.070	−0.046
Annual sales revenue	0.184 **	0.069	0.126	0.168 **	0.068	0.115
Step 2
Psychological distance				0.182 ***	0.038	0.174
F value			14.579			16.123
R2			0.129			0.158
Adj. R2			0.120			0.148
∆Adj R 2			0.129			0.029

Note: * p < 0.05, ** p < 0.01, *** p < 0.001.

. Overall, with all variables entered, the model explained 15.8% of the variance in having a higher risk perception of ZTE’s disruption. In model 1, demographical variables were entered as controls; the overall model was significant, and R² = 0.065, F (7, 688) = 14.579, p < 0.001. In model 2, the psychological distance was entered as a predictor; the overall model remained significant, and R² = 0.094, F (8, 687) = 16.123, p < 0.001. The psychological distance was a significant predictor of risk perception (B = 0.182, p < 0.001).

The aforementioned analysis results indicate that psychological distance has a significant positive impact on disruption risk perception in both the upstream and downstream of ZTE’s supply chain. Figure 4 depicts the diagram of the results. To better observe and compare the results, the psychological distance scores of the upstream were converted to negative values, and the left and right half of the diagram represent the results of the upstream and downstream, respectively.

An interaction effect test was conducted in order to further test whether there is a symmetrical relationship between the results of the upstream and downstream. The results indicate that both the overall model (F (9, 1045) = 14.331, p > 0.05) and the interaction coefficient (B = −0.016, p > 0.05) were not significant, which indicates that there is no interaction effect between the upstream and downstream. In other words, the impact of psychological distance on disruption risk perception is symmetric (consistent) between the upstream and downstream. Due to this symmetry, the following analysis no longer distinguishes between the upstream or downstream. Therefore, the tier-1 supplier and tier-1 customer are merged (hereinafter referred to as tier 1), and the tier-2 supplier and tier-2 customer are also merged (hereinafter referred to as tier 2).

4.3. Indirect Effect of Distance on Disruption Risk Perception Via Perceived Risk Information Quality

H2 predicted that objective distance and subjective distance would have a direct impact on the perceived quality of risk information and disruption risk perception, as well as an indirect impact on risk perception through perceived risk information quality. We conducted two separate mediation analyses for objective distance (i.e., supply chain distance) and subjective distance (i.e., psychological distance) on disruption risk perception. In order to investigate the mediated impacts of the perceived risk information quality on the relationship between distance and disruption risk perception, we utilized the PROCESS V3.3 tool to test multiple mediators and analyze the overall effects.

4.3.1. The Mediated Effects of Objective (Tier) Distance on Disruption Risk Perception via Perceived Risk Information Quality

The respondents’ ratings of perceived disruption risk information quality differed significantly depending on their supply chain distance (F (2, 1052) = 89.078, p < 0.001, and η² = 0.145, by ANOVA). That is, their perceived risk information quality from highest to lowest in the supply chain were: ZTE (PRIQ score = 5.876), ZTE’s tier-1 supplier/customer (PRIQ score = 4.678), and ZTE’s tier-2 supplier/customer (PRIQ score = 4.883). Fisher’s Least Significant Difference (LSD) post hoc test indicated that the ZTE group’s perceived risk information quality was significantly higher than those of the tier-1 and tier-2 suppliers/customers. (p < 0.001).

Supply chain distance (multi-categorical variable containing three groups: ZTE, tier 1, and tier 2) was entered as the predictor and was encoded as a dummy variable (ZTE was set as the control group), and perceived risk information quality was considered a mediator, with ZTE’s disruption risk perception serving as the outcome or dependent variable. The results of our analysis were assessed using a bootstrap estimation approach, which involved 5000 samples and is presented in

Table 8. Mediating effects of perceived risk information quality on disruption risk perception (ZTE was set as the control group).

Path of Mediating Effect	Point Estimate	SE	95% CI
			Low	High
Group of Tier 1:
Relative total effect (Tier 1 → disruption risk perception)	0.476	0.146	0.190	0.762
Relative direct effect (Tier 1 → disruption risk perception)	−0.167	0.136	−0.434	0.099
Relative mediating effect (Tier 1 → Perceived risk information quality → disruption risk perception)	0.643 a	0.085	0.482	0.813
Group of Tier 2:
Relative total effect (Tier 2 → disruption risk perception)	0.908	0.150	0.614	1.201
Relative direct effect (Tier 2 → disruption risk perception)	0.057	0.143	−0.224	0.338
Relative mediating effect (Tier 2 → Perceived risk information quality → disruption risk perception)	0.850 a	0.093	0.677	1.033

Note: “a” indicates mediating effect is significant.

. Specifically, we examined the total, direct, and mediated effects of the supply chain distance on disruption risk perception.

Following Hayes [77], we began by examining the total effect of supply chain distance on the risk perception of ZTE’s disruption (i.e., the effect of supply chain distance on disruption risk perception without the presence of any mediating effects), and found a positive and significant effect (the control group (ZTE) was used as the reference, group of tier 1: B = 0.476, SE = 0.146, p < 0.001, 95% CI = [0.190, 0.762]; group of tier 2: B = 0.908, SE = 0.150, p < 0.001, 95% CI = [0.614, 1.201]). Next, we examined the complete model, which includes both the direct and mediated effects of supply chain distance on disruption risk perception. As shown in Table 8, the relative direct effect of supply chain distance on disruption risk perception in the presence of mediators becomes nonsignificant (the control group (ZTE) was used as the reference, group of tier 1: B = −0.167, SE = 0.136, p > 0.05, 95% CI = [−0.434, 0.099]; group of tier 2: B = 0.057, SE = 0.143, p > 0.05, 95% CI = [−0.224, 0.338]), indicating full mediation through perceived risk information quality. In addition, supply chain distance showed a relative indirect effect on disruption risk perception through perceived risk information quality (the control group (ZTE) was used as the reference, group of tier 1: B = 0.643, SE = 0.085, 95% CI = [0.482, 0.813]; group of tier 2: B = 0.850, SE = 0.093, 95% CI = [0.677, 1.033]). The bias-corrected bootstrap confidence intervals for the indirect effects were entirely above zero, indicating significant mediation effects.

4.3.2. The Mediated Effects of Subjective (Psychological) Distance on Disruption Risk Perception via Perceived Risk Information Quality

We conducted the same analysis of the subjective (psychological) distance. In this model, objective (tier) distance was entered as control. We first examined the total effect of psychological distance on the risk perception of ZTE’s disruption (i.e., the effect of psychological distance on disruption risk perception without the mediated effects) and found a positive and significant effect (B = 0.208, SE = 0.031, p < 0.001, 95% CI = [0.147, 0.269]). We then examined the direct and mediated effects. As shown in Figure 5, psychological distance had a positive effect on perceived risk information quality (B = −0.080, SE = 0.027, p < 0 0.01, 95% CI = [0.112, 0.219]), which was positively related to willingness to use (B = −0.535, SE = 0.031, p < 0.001, 95% CI = [−0.595, −0.474]; indirect effect of psychological distance: B = 0.043, SE = 0.017, 95% CI = [0.012, 0.077]), indicating a mediation effect. The direct effect of psychological distance on disruption risk perception in the presence of mediators was significant (B = 0.165, SE = 0.027, p < 0.001, 95% CI = [0.112, 0.219]). As shown in Figure 5, the results suggest that perceived risk information quality partially mediates the effect of psychological distance on disruption risk perception.

Altogether, mediation analyses confirmed our H2, i.e., the effect of distance on disruption risk perception is mediated through perceived risk information quality.

4.4. The Moderating Effect of Job Position Level

We used the hierarchical linear regression model to examine the moderating effect of job position level. Before performing the regression analysis, we centered all non-nominal variables to alleviate the threats of multi-collinearity between the component measures [78]. Models 1–3 and 4–5, respectively, employed supply chain disruption risk perception to take management response as dependent variables. For models 1–3, sociodemographic and firm size were entered into the first layer as control variables (i.e., model 1), and then the perceived risk information quality and job position level were entered as the second layer (i.e., model 2). It should be noted that job position level is a categorical variable and was encoded as a dichotomous variable (0 = low-level positions, 1= high-level positions). In the final layer of the regression model, the interaction term between job position level and perceived risk information quality was included (i.e., model 3). For models 4–5, the first layer is the control variables (i.e., model 4), and the second layer is the supply chain disruption risk perception (i.e., model 5).

Model 1: Supply chain disruption risk perception = B₀ + Controls + e

Model 2: Supply chain disruption risk perception = B₀ + B₁(Perceived risk information quality) + B₂(Job position level) + Controls + e

Model 3: Supply chain disruption risk perception = B₀ + B₁(Perceived risk information quality) + B₂(Job position level) + B₃(Job position level × Perceived risk information quality) + Controls + e

Model 4: Managerial response = B₀ + Controls + e

Model 5: Managerial response = B₀ + B₁(Supply chain disruption risk perception) + Controls + e

When assessing the moderating effect, it is common practice to use the regression coefficient and significance of interaction terms to determine the presence of such an effect. According to the findings presented in

Table 9. Hierarchical linear regressions (for “Disruption risk perception” and “Managerial response”).

	Dependent Variable: DRP						Dependent Variable: MR
	Model 1		Model 2		Model 3		Model 4		Model 5
	Estimate	SE	Estimate	SE	Estimate	SE	Estimate	SE	Estimate	SE
Layer 1: Control Variables
(Constant)	−0.543	0.373	−0.887 **	0.447	−0.847 **	0.336	−0.097	0.381	0.131	0.348
Gender	0.265 *	0.116	0.197 †	0.105	0.197 †	0.104	0.060	0.119	−0.051	0.109
Age	−0.183	0.112	−0.235 *	0.101	−0.243 *	0.101	−0.286 **	0.114	−0.209 *	0.104
Education	0.184 *	0.085	0.262 ***	0.076	0.259 ***	0.076	0.100	0.086	0.022	0.079
Experience	−0.168 *	0.080	−0.055	0.072	−0.049	0.072	−0.069	0.081	0.002	0.074
Rpr	−0.393 ***	0.042	−0.240 ***	0.039	−0.234 ***	0.039	−0.033	0.043	0.132 ***	0.040
Number of employees	−0.030	0.058	−0.009	0.052	−0.015	0.052	−0.034	0.059	−0.021	0.054
Annual sales revenue	0.213 ***	0.056	0.161 **	0.051	0.160 **	0.051	0.179 **	0.058	0.089	0.053
Layer 2: Main effect
PRIQ			−0.506 ***	0.032	−0.558 ***	0.038
JPL			−0.211 *	0.107	−0.207 *	0.106
DRP									0.421 ***	0.029
Layer 3: Interaction effect
JPL × PRIQ					0.158 *	0.065
R2	0.140		0.311		0.315		0.051		0.212
Adjusted R2	0.134		0.305		0.308		0.044		0.206
R2 change	0.140		0.171		0.004		0.051		0.161
F-statistic	24.376		52.356		47.926		8.006		35.230

Note: ^†p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.

, job position level serves as a significant moderator in the association between perceived risk information quality and disruption risk perception (B₃ = 0.153, p < 0.05).

To simplify the interpretation of the interaction terms, we utilized the “pick-a-point” technique to identify the conditional impact of perceived risk information quality on disruption risk perception, based on low and high levels of job position level (where 0 = low level, 1 = high level), in accordance with the findings of the moderating effect analysis. Subsequent simple slope tests revealed that the association between perceived risk information quality and disruption risk perception remains significant in both low and high levels of job position level (slope _{low job position level} = −0.558, p < 0.001; slope _{high job position level} = −0.400, p < 0.001). As Figure 6 presents, in the case of a high job position level, perceived risk information quality has a weaker negative effect on disruption risk perception. Thus, H3 was supported.

Finally, with regard to the influence of disruption risk perception on managerial response, the outcomes displayed in Table 9 provide evidence in favor of Hypothesis 4, indicating that heightened disruption risk perception results in a markedly stronger managerial response (B = 0.421, p < 0.001).

5. Discussion

5.1. Theoretical Contributions

The PTE effect has been observed in different risk areas such as earthquakes (e.g., [16]), terrorist attacks (e.g., [79]), epidemic outbreaks (e.g., [35,36]), environmental pollution (e.g., [19]), etc. Unlike these studies, where the risk perception was a function of geographical distance, the PTE effect in the present study is a function of a new type of distance, i.e., supply chain distance. As far as we are aware, this is the initial empirical investigation that examines the PTE effect in the realm of operations and supply chain management. Our study addresses the need for research that incorporates psychological risk theories into operations and supply chain management by extending upon previous research on supply chain disruption risk [12,80].

This study investigates the risk perception of supply chain upstream and downstream toward the focal firm’s disruption. We moved beyond the single-tier buyer–seller relationships towards a multi-tier supply chain context to provide empirical insights into managers’ perception of disruption risk. Although there has been considerable research on supply chain disruption risk perception, there is predominantly a focus on managers’ perception of supply disruption risk within the purchasing domain [10,12,37]. This dyadic buyer–supplier perspective considers only the existence of direct relationships within a supply chain where most studies have drawn their research boundaries. However, such investigations fail to fully encompass the numerous, distinct, and interdependent interactions that coexist throughout the supply chain [58,81,82]. A supply chain network is vulnerable to disruptions not only because of the direct impacts of those disruptions but also because of the risk propagation [1,83]. Therefore, if the lead firm is at risk of supply chain disruption, the company itself and its first-tier and low-tier suppliers/customers would assess and respond to the direct or indirect risk [84,85]. This requires us to understand not only the focal firm’s disruption risk perception but also how upstream and downstream firms perceive the disruption risk. The present study makes a preliminary exploration and provides some inspiration for future studies in this area.

This study also contributes to our understanding of the mechanism of the PTE effect. Risk perception is all about thoughts, beliefs, and constructs [86], and people’s perception of risk is based on experience and available information [87]. Available information has a significant impact on the assessments and judgments that are made, thereby influencing the emotions and actions of supply chain participants towards the disrupted firm [88]. Yang et al.’s research [42] has explored the PTE effect mechanism in terms of the quantity dimension of the available information in the context of the COVID-19 pandemic, while the present study validated the PTE effect through the quality dimension of the available information in the context of supply chain disruption risk. Specifically, supply chain members who are close to the disrupted echelon have easier and more timely access to first-hand disruption risk information, i.e., they have access to high-quality information about the disruption risk. High-quality available information helps managers reasonably assess the actual level of disruption risk so that they do not over- or underestimate the risk. Supply chain members who are far away from the disrupted echelon can only receive disruption risk information from media reports or their partners. However, the disruption risk information has problems with distortion, delay, and untrustworthiness, i.e., supply chain managers receive lower quality information about the disruption risk. Low-quality information has the potential to lead to confusion and limit an individual’s capacity to adequately process and react to the information presented [89], which makes it impossible for supply chain managers to learn the truth of the disrupted firm, ultimately leading them to overestimate the disruption risk [46,47].

We also found that job position level moderated the relationship between risk information quality and risk perception. Senior supply chain managers, who are experts with extensive risk management experience and expertise, are more inclined to use intuition to assess risk and require less risk-related information [23,90,91], so they are less influenced by external information and, accordingly, the level of risk information quality has less impact on their disruption risk perception. As with the general public, supply chain managers at lower levels conduct risk assessments based primarily on the risk information obtained, or rather on the basis of information or evidence-based assessments [86,92], therefore, the level of information quality has a greater impact on their perception of supply chain disruption risk.

In uncertain environments, people rely on heuristic strategies to make judgments and decisions [93]. According to the theory, supply chain managers are more likely to believe information that they are exposed to. Managers of focal companies and their direct trading partners observe or personally experience the disruption risk, and their assessment of disruption risk will be closer to reality. However, upstream and downstream members far from the focal company primarily learn about its disruption risk information through media or other channels. They tend to use this distorted and amplified information to assess risk, leading to a significant increase in their perception of disruption risk. Additionally, senior managers tend to assess risk based on their experience rather than the information they receive. In a word, supply chain managers often use heuristic strategies to assess disruption risk. However, differences in information quality, experience, etc., result in a perception bias of disruption risk known as the PTE effect.

5.2. Managerial Implications

The theory of risky decision-making offers an explanation for the association between managerial risk perceptions and their utilization of mitigation strategies [37]. A higher perception of disruption risk would lead supply chain managers to take action on behalf of their home organization in order to minimize the likelihood of being affected by a supply-side or demand-side disruption. For instance, when the focal firm faces supply chain disruption risk, its upstream partners will cut the exclusive supply capacity, and downstream partners will implement alternative sourcing [57] or switch suppliers [12]. This may exacerbate the focal firm’s overall operational risk. Therefore, how to alleviate the PTE effect upstream and downstream is the key to supply chain disruption risk management for the focal firm.

Supply chain disruption risk is characterized by information uncertainty and the gap between the information available and the information needed to estimate and respond to the risk [94]. The present study shows that the high-quality risk information perceived by supply chain managers can reduce the proportion of disruption risk information so as to reduce their focus illusion and would finally reduce the level of perception of the focal firm’s disruption risk. As such, effective management of disruption risk within and between firms necessitates a collective commitment to high-quality information synchronization, which ensures that disruption risk information is readily accessible, timely, credible, and comprehensible. This effort must extend beyond a single company initiative and involve all firms in the supply chain [68,82,95].

Specifically, first, the focal firm must ensure that upstream and downstream enterprises have easy access to real-time information related to disruption risk. It is widely acknowledged that many companies lack adequate information about their lower-tier partners in multi-tier supply chains [96]. This is compounded by narrow information sharing and communication channels, which restrict the efficiency of supply chain risk management efforts [82]. Therefore, the focal firm must take proactive measures to improve supply chain risk visibility and communication, empowering lower-tier suppliers to easily obtain reliable disruption risk information [97,98], making it difficult for them to gain misinformation or illusory information about the disruption risk. Secondly, the focal firm should timely release disruption risk-related information, disallowing a window of time for disruption risk misinformation to spread. The timely release and updating of disruption risk-related information is an effective measure to stop the spread of misinformation and avoid partners being overly concerned about disruptions [99]. The focal firm can release information and announcements through the official website, email, social media, and other channels so that supply chain members can obtain timely disruption risk information. Thirdly, the focal firm should enhance the credibility of the disruption risk information. Accurate and reliable information helps to eliminate supply chain professionals’ information illusions and reduce their concerns and misevaluation of the disruption risk [100,101]. Lastly, the disruption risk information provided by the disrupted firm needs to be easy to understand. Compared with unclear and ambiguous information, specific and easy-to-understand information can reduce an individual’s risk perception [102]. This requires the disrupted firm to release information relevant to the disruption risk in an easy-to-understand manner (e.g., text combined with pictures or videos) to facilitate supply chain members to assess the disruption risk properly.

Furthermore, it is important to note that definitions and interpretations of disruption risk terms may vary across organizations due to differences in business contexts and cultures [11]. This variation can lead to misunderstandings in shared disruption risk information and can ultimately impede effective supply chain risk management efforts within and between firms [98]. To address this challenge, it is recommended that a unified risk information language be established within and between supply chain firms to ensure consistent and clear communication about disruption risk. This approach will support objective disruption risk assessment and effective disruption risk communication along the entire supply chain of the focal firm.

Compared to high-level managers, lower-level supply chain managers’ disruption risk perception towards the focal firm is more likely to be affected by their perceived risk information quality. So, it is more necessary to provide them with more timely, credible, and understandable disruption risk information and make it easier for them to acquire this information.

It is worth noting that different representations of the same information can lead to different judgments and decisions [103]. Therefore, in addition to the disruption risk information quality, the representation of disruption risk information can also affect managers’ risk perception and decision-making. This inspires us that when focal companies release disruption risk information, they can flexibly design the framework to reasonably weaken the threat of disruption information, strengthen the information of disruption mitigation and control, mitigate excessive concerns of supply chain members far from the focal company about disruption risk, and reduce their irrational operational decisions.

6. Limitations and Future Work

Risk perception among managers in a multi-tier supply chain has received limited attention. This current study provides initial insights into this area, but it still has several potential limitations that must be highlighted to motivate future research. First, the supply chain management literature identifies several factors that may also impact perceptions of supply chain disruption risk [38]. The present study empirically examines the influence of distance on risk perception within the context of a multi-tier supply chain. Other contingency factors such as cultural value orientations [12,104], uncertainty [10,38], or trust [54] would affect an individual’s disruption risk perception; future research could examine whether there is a PTE effect, which may contribute to providing a richer understanding of risk perception in the multi-tier supply chain. Second, the understanding of the underlying mechanism of the PTE effect in the multi-tier supply chain raises a challenging question: how can a disrupted focal firm enable its supply chain partners to receive high-quality disruption risk information to mitigate the PTE effect? While the present study does not focus on specific tactics, it does provide a rich avenue for future scientific research to design and examine the PTE effect mitigation strategies. Third, it is worth noting that our data was gathered through a cross-sectional survey, which is susceptible to respondents’ subjective judgment and may involve some level of arbitrariness or variability. To further support and validate our results, future research could utilize alternative data collection methods and research designs, such as longitudinal studies (e.g., [14]) or laboratory experiments (e.g., [38,105]). Additionally, it is important to acknowledge that this study was conducted in mainland China, with its unique social norms and economic system. Thus, in order to enhance the external validity of our findings, it is recommended that further research be conducted in other countries/regions with different social and cultural norms.

7. Conclusions

The perception of disruption risks by supply chain managers can significantly impact their subsequent management responses, particularly in an era where supply chain disruption risks have become increasingly common due to events such as the 2011 Great Tohoku Earthquake, the COVID-19 pandemic, and the Russia–Ukraine conflict. This study investigates the risk perception of supply chain disruption in ZTE and its upstream and downstream members. The results indicate that as supply chain members are farther from the epicenter (i.e., ZTE), their risk perception of the disruption at the epicenter increases, a phenomenon we refer to as the PTE effect in supply chain disruption risk. Further research reveals that both supply chain distance and psychological distance influence disruption risk perception through risk information quality, and job position level moderates the relationship between risk information quality and disruption risk perception. These findings suggest that the focal firm must go beyond single-company initiatives and prioritize high-quality information synchronization to mitigate the PTE effect within the supply chain.