A Multidisciplinary Behavioral Agency - Wiley Online Library

Decision Sciences Volume 40 Number 4 November 2009

C 2009, The Author C 2009, Decision Sciences Institute Journal compilation

The Decision of the Supply Chain Executive to Support or Impede Supply Chain Integration: A Multidisciplinary Behavioral Agency Perspective∗ Verónica H. Villena† Department of Operations and Technology Management, Instituto de Empresa Business School, Maria de Molina 12 Bajo 28006 Madrid, Spain, e-mail: [email protected]

Luis R. Gomez-Mejia Management Department, Mays College of Business, Texas A&M University, 4221 TAMU, College Station, TX 77843, e-mail: [email protected]

Elena Revilla Department of Operations and Technology Management, Instituto de Empresa Business School, Maria de Molina 12 Bajo 28006 Madrid, Spain, e-mail: [email protected]

ABSTRACT Applying the behavioral agency model developed by Wiseman and Gomez-Mejia (1998), this article analyzes human resource factors that induce supply chain executives (SCEs) to make decisions that foster or hinder supply chain integration. We examine two internal sources (compensation and employment risk) and one external source (environmental volatility) of risk bearing that can make SCEs more reluctant to make the decision to promote supply chain integration. We argue and empirically confirm the notion that an employment and compensation system that increases SCE risk bearing reduces the SCE’s willingness to make risky decisions and thus discourages supply chain integration. We also reveal that this negative relationship becomes stronger under conditions of high environmental volatility. In addressing the “so what?” question, we found empirical support for the hypothesis that supply chain integration positively influences operational performance. Even though this decision has a positive value for the firm, we showed that SCEs discourage supply chain integration when they face higher risk bearing. Hypotheses are tested using a combination of primary survey data and archival measures in a sample of 133 Spanish firms.

Subject Areas: Behavioral Agency Model, Decision Making, Executive Risk Taking, Multidisciplinary Research, Performance, Risk Bearing, Supply Chain Executive, and Supply Chain Integration.

∗ We are grateful to Manuel Becerra and Mark Errington for their helpful and constructive comments on earlier drafts of this article and to Angel Diaz and Björn Claes for their help with data-collection processes. † Corresponding

author.

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A Multidisciplinary Behavioral Agency Perspective

INTRODUCTION Research on strategic supply chain management has viewed supply chains not only as production and distribution mechanisms but also as important competitive weapons (Hult, Ketchen, & Nichols, 2002; Hult, Ketchen, & Slater, 2004). Firms such as Toyota, Inditex, and Dell have used the collective capabilities of their supply chains rather than solely their individual capabilities to succeed in an increasingly competitive market. Firms recognize that they do not possess the full range of capabilities to design, produce, and deliver the products and services that demanding markets require or to compete globally (Gomes-Casseres, 1994). They thus need to develop strategic partnerships with other supply chain members (Powell, Koput, & Smith-Doerr, 1996; Dyer & Nobeoka, 2000; Mentzer, Min, & Zacharia, 2000; Kotabe, Martin, & Domoto, 2003; Wisner, 2003) with the objective of accessing new resources and achieving an integrated coordination that allows them to respond more efficiently and more quickly to current market needs. However, supply chain partnerships may be inherently risky. Knowledge may spill over to potential competitors (Bleeke & Ernst, 1995; Dyer & Singh, 1998; Gulati & Gargiulo, 1999; Kale, Singh, & Perlmutter, 2000; Myers & Cheung, 2008), and partnership-specific investments may be lost through poor managerial decisions (Williamson, 1985; Osborn & Hagedoorn, 1997; Jap, 1999). Opportunism by a partner may be difficult to detect because of information asymmetries and this self-serving behavior could erode the firm’s competitive position (Eisenhardt, 1989). Supply chain executives (SCEs) aware of the potentially high, but uncertain benefits derived from supply chain partnerships might be less willing to engage in collaborative initiatives with supply chain partners if they believe that by doing so their compensation and employment security would be at risk. There is a large body of literature suggesting that when the executive feels vulnerable in terms of potential losses to personal income and/or the possibility of dismissal as a result of managerial decisions, they take a more conservative path and avoid perilous choices that they believe augment personal risk bearing (e.g., Gray & Cannella, 1997; Bloom & Milkovich, 1998). Environmental uncertainty also magnifies executives’ vulnerability as it severely limits how much control they can exert over performance outcomes resulting from a particular choice (Miller, Wiseman, & Gomez-Mejia, 2002). This literature has almost exclusively focused on the Chief Executive Officer (CEO) with the firm as the unit of analysis. In this article, we take a different approach and focus on how SCEs respond to these internal and external risks in promoting or hindering supply chain integration, which we argue is a risky endeavour despite its potential benefits. This study makes three main contributions to the decision-making literature and supply chain research in particular. First, previous research on executive risk bearing and risk taking has focused almost exclusively on the CEO (e.g., Tosi & Gomez-Mejia, 1989; Wiseman & Bromiley, 1996; Gray & Cannella, 1997; Tosi, Werner, Katz, & Gomez-Mejia, 2000; Larraza-Kintana, Wiseman, Gomez-Mejia, & Welbourne, 2007; Devers, McNamara, Wiseman, & Arrfelt, 2008) and more recently on the Chief Information Office (CIO) (Preston, Chen, & Leidner, 2008). However, the SCE also plays a pivotal role in how much risk is taken by the firm to ensure its continued competitiveness and success (Mangan & Christopher, 2005).

Villena, Gomez-Mejia, and Revilla

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We develop a multidisciplinary research framework that analyzes how a firm’s employment and compensation system influences its SCE’s decision to foster or limit supply chain integration. This is a unique study that combines research streams from behavioral decision making and supply chain management, and uses matched-pair survey data as well as archival measures to test hypotheses. Second, we extend the behavioral agency model (BAM) (Wiseman & Gomez-Mejia, 1998) by examining the moderating role of environmental risk in the relationship between risk bearing and risk taking, which has not been analyzed in prior research. Our model combines elements of agency theory with behavioral views of decision making under uncertainty to examine how compensation and employment risk influences SCE choices when the outcomes of these choices may be at least partially beyond the executive’s control (when environmental uncertainty is high). Lastly, previous research on supply chain integration has mainly stressed the importance of trust, commitment, information technologies, and information sharing. Within this list of factors that enable supply chain integration, we examine the distinct and additive impact of managerial incentives as the key element in the SCE’s decision to foster this risky choice. In summary, this study attempts to develop guidelines for designing an employment and compensation system for the SCE in order to encourage supply chain integration through the development of supply chain partnerships. We argue and find empirical support for the notion that greater compensation and employment risk for the SCE is negatively associated with supply chain integration, and that this negative relation becomes stronger under conditions of high environmental uncertainty. Furthermore, we hypothesize and our results confirm that as supply chain integration declines so does performance. We organize the article into five sections. In the next section, we discuss the conceptual framework and formulate the hypotheses. First, we present the behavioral agency framework, leading to the hypotheses concerning how SCE compensation (Hypothesis 1) and employment (Hypothesis 2) risk negatively influencing supply chain integration and the moderating role played by environmental risk (Hypothesis 3a and b) in this process. Second, we deal with the “so what” question by discussing how supply chain integration affects operational performance (Hypothesis 4). In the third section, we describe the methodology used to test the hypotheses, which combines both archival and primary survey measures in a sample of 133 Spanish firms. In the fourth section, we present the results confirming the hypotheses. In the fifth and last section, we conclude with a discussion of theoretical and practical implications of the findings and an agenda for future research.

THEORETICAL FRAMEWORK AND HYPOTHESES To support our arguments, we developed a theoretical framework that uses the BAM of managerial risk taking (Wiseman & Gomez-Mejia, 1998). As discussed below, this theory suggests that decision makers are loss-averse when facing uncertain outcomes that threaten their personal wealth resulting from particular choices (such as entering supply chain partnerships).

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Behavioral Agency Model (BAM) The BAM developed by Wiseman and Gomez-Mejia (1998) integrates elements of agency theory and behavioral views of corporate governance decision making under conditions of uncertainty in order to develop a model of managerial risk taking. Behavioral models of decisions (Sitkin & Pablo, 1992) explain how risk preference changes with the framing of problems. The BAM predicts that decision makers will avoid risk when selecting among positively framed problems and seek risk when selecting among identical but negatively framed prospects (Kahneman & Tversky, 1979). Risk bearing under the BAM is defined as the potential reduction in wealth (either in the form of income and/or employment loss) for the decision maker when confronted with a particular choice. The potential loss of compensation implies a threat to the person’s basic standard of living. Furthermore, “since termination results in the complete loss of all current income and puts in serious jeopardy future income, employment risk represents the ultimate threat to an agent’s wealth” (Larraza-Kintana et al., 2007). Risk bearing makes the executive more vulnerable to the negative consequences (such as foregone pay or dismissal) of taking risks (such as pursuing supply chain integration) if the results of such choices are poor or disappointing. This risk bearing is compounded when the ability of the decision maker to influence or control outcomes resulting from a decision decreases, as is the case in highly volatile environments (Miller et al., 2002). Simply put, according to the BAM any action that threatens base pay or adjustments to it or reduces employment security (e.g., receiving a poor performance evaluation, unmet targets) greatly increases risk bearing and thus the reluctance of the decision maker to make risky choices. This tendency is likely to be present no matter how potentially beneficial the risky decision might be. In our particular context, the BAM suggests that the SCE is loss averse and that risk bearing occurs when there is a threat to his or her compensation and employment security if he or she were to pursue high-risk/high-return alternatives (such as choices related to supply chain integration). In other words, the SCE may perceive the integration choice as beneficial to the firm; yet, avoid that choice if it implies a private threat to his/her wealth. Next, we explain why supply chain integration represents a risky choice followed by a discussion of the factors that would dissuade the SCE from making this choice. Integrated supply chains as a risky choice Integrated supply chains are those whose members synchronize their processes and share relevant, updated information hoping to improve their performance. Herein, integrated supply chain partners combine resources in a unique way and may gain an advantage over competing firms that are unable or unwilling to do so. Together they generate and own relational profits that cannot be created by either firm independently (Dyer & Singh, 1998). On the other hand, there are some implicit risks involved in a supply chain integration decision. According to the relational view, firms benefit from systematically sharing valuable knowledge within the supply chain (Dyer & Singh, 1998); but, of


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course, there is a risk of disclosure to potential competitors. The relational view also suggests that greater partnership-specific asset investments will lead to higher collective profits (Asanuma, 1989; Clark & Fujimoto, 1991; Dyer, 1996; Dyer & Singh, 1998); however, these investments may create a high degree of interdependence and information asymmetries between supply chain partners and, hence, risk for the participating parties who must rely on the purported good faith of the other parties involved in the relationship. That is, supply chain partnerships create potential for a breach of trust among partners, which may be difficult to detect by SCEs (Gulati & Singh, 1998; Gulati & Gargiulo, 1999). These threats are discussed in greater detail below. The first inherent hazard in supply chain partnerships is knowledge transfer to external parties. In supply chain partnerships, there is a high likelihood that a firm participates in several supply chain partnerships simultaneously (Rice & Hoppe, 2001). Thus, knowledge shared with a supplier or knowledge jointly created between a buyer and its supplier may not enjoy proprietary protection and be raided by potential competitors (Bleeke & Ernst, 1995; Dyer & Singh, 1998; Gulati & Gargiulo, 1999; Kale, Singh, & Perlmutter, 2000; Myers & Cheung, 2008). This situation is intensified when there is no formal contractual agreement, as is the case in many supply chain partnerships (Lambert, Emmelhainz, & Gardner, 1996). Unlike other types of interfirm partnerships, a supply chain relationship is built upon repeated transactions and reliance on the other party’s capabilities to complete the value chain process, rather than shared equity or contracted objectives (He, Ghobadian, Gallear, Race, & Spinks, 2007). In such a situation, the firm faces considerable moral hazard because its supply chain partners may “freeride” by limiting their contributions to the partnership or might simply behave opportunistically (Gulati, 1998). The second implicit threat in fostering supply chain integration is the high level of investments in specific assets. For instance, the use of purchased inputs (e.g., customized new technology) may only have value within the unique context of the buyer–supplier relationship and hence cannot be easily transposed to other uses (Williamson, 1985; Anderson & Weitz, 1992; Kumar, Scheer, & Steenkamp, 1995; Dyer & Singh, 1998). At the same time, “tailor made” training or development programs for employees involved in the buyer–supplier partnership are indispensable in order to achieve higher levels of knowledge sharing routines between partners (Monczka, Petersen, Handfield, & Ragatz, 1998; Krause, Scannell, & Calantone, 2000; Modi & Mabert, 2007). Such investments in valuable tangible assets and human capital are costly and may produce asymmetric gains favoring the partner or, conversely, produce largely unrecoverable losses if the partnership fails or produces disappointing results (Williamson, 1985; Jap, 1999). In short, despite the potential advantages to participating firms, the uncertainty of disclosure of key knowledge and the important specific investments in supply chain partnerships makes this a risky decision to the extent that the SCE is made accountable for the outcomes of such a decision. The risk is privatized by the SCE even though he or she may not be fully or even partially responsible for the observed results. As discussed next, the more vulnerable the SCE is to losses (in terms of compensation and employment, plus the compounding effect

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Figure 1: Conceptual model. Executive Risk Bearing Compensation Risk

(-) (-)

Managerial Risk Taking

Outcome

Supply Chain Integration

Operational Performance

(+)

Employment Risk (-)

Environmental Risk

of environmental uncertainty), the less likely the SCE is to support an integration decision (Hypotheses 1–3), even if the SCE sees it as “the right thing to do” (Hypothesis 4).

Vulnerability of the SCE: Sources of executive risk bearing Figure 1 provides an overview of the conceptual model. Three kinds of risk— compensation risk, employment risk, and environmental risk—augment the vulnerability of the SCE and thus reduce his or her willingness to foster supply chain integration (which, as argued above, represents a risky choice). Each of these elements is discussed next, followed by a discussion of the consequences of supply chain integration or the lack thereof. Compensation risk: Developers of traditional agency models (Beatty & Zajac, 1994; Gray & Cannella, 1997) assume that the executive is risk averse and thus prefers higher proportions of certain compensation over uncertain (variable) compensation. In contrast, under the BAM, variable pay is assumed to have little effect on agent risk bearing when it is simply added to a compensation scheme as an “extra layer” (e.g., from 10% to 20% of variable pay).This is because (i) variable compensation is normally used for discretionary consumption expenses and savings, which can be deferred into the future or, perhaps, forgone altogether (Strahilevitz, 1992; Arkes, Joyner, & Stone, 1994) and (ii) it is not usually counted in calculations of perceived current wealth due to the fact that it is far less certain (Wiseman & Gomez-Mejia, 1998; Larraza-Kintana et al., 2007). Under BAM, base pay is anticipated and viewed as essential to the executive’s standard of living. It is usually linked primarily to indispensable consumption expenses (i.e., rent, food, utilities, and so forth) (Strahilevitz, 1992) and, consequently, threats to this type of compensation would be more prominent than threats to variable pay. In the words of Wiseman and Gomez-Mejia (1998, p. 140) “threats to future base pay and its value (e.g., losing cost of living or market adjustments) would seem more salient than threats to variable pay, since losses associated with future base pay pose a significant threat to an executive’s perceived wealth, future


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earnings potential and, ultimately, to his or her standard of living.” For instance, the base pay of the SCE may be frozen, fall behind market, lose ground relative to other managers or new hires, and, in the extreme case, may be cut. In contrast to CEOs (the central focus of most of this research), who normally receive 40% of their pay in a variable form (Devers et al., 2008), for SCEs, base pay or salary on average represents 94% of total pay (salary.com’s survey, 2008). These figures are derived from the U.S. surveys but the percentage of base pay for SCEs in Spain (where the study was conducted) is likely to be much higher, probably closer to 98% on average. This means that to the extent that supply chain integration constitutes what Larraza-Kintana et al. (2007) identify as “downside risk” to SCE’s base pay (e.g., by receiving a weak performance appraisal if supply chain integration targets are not met and thereby foregoing merit pay adjustments), the SCE will tend to avoid that risk even if risk avoidance implies surrendering the potential benefits to the firm of such integration. Employment risk: A related but different source of risk bearing is employment risk. Herein, the SCE may face a greater probability of (i) termination and (ii) negative reputation in the market if the investment to create supply chain partnerships does not produce the desired results. In the first case, top executives have a tendency to “scapegoat” those below them when results are disappointing (Gomez-Mejia, Nuñez-Nickel, & Gutierrez, 2001). In the second case, it is difficult for outsiders to distinguish unfortunate circumstances from poor decisions (Wiseman & GomezMejia, 1998). As termination plus negative reputation equals loss of all current income and seriously jeopardizes all future income, employment risk represents a more severe threat to the SCE’s wealth than does compensation risk (LarrazaKintana et al., 2007). This is consistent with Tversky and Wakker’s (1995) research on choice behavior related to risk, which suggests that a loss-averse SCE will tend to avoid supply chain integration if this further jeopardizes the SCE’s job security, which is associated with the loss of all of his or her current income and negatively impacts further income stream. The employment risk perceived by the SCE is also intimately related to the firm’s evaluation and reward system. Failure to meet performance targets usually translates into being perceived by superiors as a substandard contributor and, hence, more amenable to being replaced (Wiseman & Gomez-Mejia, 1998). At the very least, this is likely to be perceived by the SCE as damaging to his or her career prospects both within the firm and in the labor market at large. In short, SCEs who do not meet quantifiable performance targets are likely to receive (or perceive that they will have) low performances marks and consequently face greater employment risk.

Hypothesis on internal sources of risk To summarize our discussion so far, SCEs experiencing high levels of compensation and employment risk are less likely to foster supply chain integration. Such an integration is risky in terms of uncertainty of eventual outcomes. Conditions of high compensation and employment risk should dissuade SCEs from undertaking this additional risk (as falling below performance targets again in the near future

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could mean a poor evaluation or immediate dismissal). To this, we would like to add three other related reasons. First, supply chains nowadays are viewed as important competitive weapons for most firms (Hult et al., 2002, 2004) and, thus, should be considered as part of the strategic planning process. However, few firms include this facet (or ambiguously implement it) as part of the evaluation criteria for SCEs. Most SCEs are appraised in the traditional way, mainly by measuring operative outcomes relative to targets such as price reduction (Rossetti & Choi, 2005) or short-term profits (Ro, Liker, & Fixson, 2007). To the extent that efforts or processes to develop (long-term oriented) supply chain partnerships are not given sufficient weight in the SCE’s annual review, these executives are unlikely to perceive that they are being evaluated or rewarded for this risky endeavor and, thus, discourage supply chain integration. Second, SCEs might suspect that they could increase the probability of an unfavorable evaluation by top managers when pursuing supply chain partnerships even if upper echelons pay lip service to it. Because of asymmetrical power, SCEs might become an easy blame target by superiors under lackluster results while superiors may claim credit if the outcomes are positive (Boeker, 1992; Gomez-Mejia et al., 2001). Lastly, the focal SCE knows that the result of supply chain partnerships does not exclusively depend on his or her own effort; instead, it depends on the collaborative effort of other SCEs who could become members of the supply chain. These three situations intensify the vulnerability of SCEs and, thus, they might be more reluctant to allocate resources to pursue collaborative supply chain partnerships. Thus, H1: The higher the level of base compensation risk, the less likely the SCE is to promote supply chain integration. H2: The higher the level of perceived employment risk, the less likely the SCE is to promote supply chain integration. The moderator role of environmental risk: The third kind of risk bearing analyzed in this research is related to external risk (i.e., environmental volatility), which is likely to compound the effects of employment and compensation risk. Environmental risk is typically defined as greater variability in organizational returns and increased chances for corporate ruin (Baird & Thomas, 1985; Miller & Bromiley, 1990). Industry-wide forces and other external factors (e.g., global financial crisis) expose the firm to performance uncertainty over which it may exert very little control (Gray & Cannella, 1997; Bloom & Milkovich, 1998; Cruz, Gomez-Mejia & Becerra, in press). This risk also has implications for agents. For instance, a greater external risk may itself threaten executives’ income as uncertain cash flows or greater chances of organizational failure may make it more difficult for a firm to meet its present and future compensation obligations (Bloom & Milkovich, 1998). The downside of the cycle also means that upward adjustments to base pay are minimized (Milkovich & Newman, 2008). When a decision could lead to failure (e.g., because of high market variability), despite the best efforts of the SCE, he or she may be much more willing to withhold effort or to take evasive actions designed to reduce risk exposure. This is because environmental risk will magnify the fear that compensation would suffer for reasons that are totally beyond the control of the SCE. We therefore expect that:


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H3a: The negative effect of compensation risk on promoting supply chain integration will be greater for a higher degree of environmental risk. Similarly external risk as captured by high firm performance variability threatens employment stability as firms attempt to reduce costs during down cycles; a threat to which the SCE is not immune. Under the BAM perspective, we would expect that a highly volatile environment would make the SCE less willing to take the additional risk of promoting supply chain integration as this might exacerbate an anticipated loss of employment. Not only do the risks of supply chain integration increase in volatile environments (Liker & Choi, 2004; Chopra & Meindl, 2007), but also the SCE may rightfully fear that, cost considerations aside, it is easier for superiors to rationalize or make negative performance attributions to the SCE if he or she takes additional risk during the down cycle and use that negative bias to replace the executive, hoping to get a better substitute (Boeker, 1992). Thus, H3b: The negative effect of employment risk on promoting supply chain integration will be greater for a higher level of environmental risk.

Supply Chain Integration and Performance So far, we have discussed the antecedents of supply chain integration from the perspective of SCEs as decision makers and risk bearers. The last hypothesis deals with the consequences of promoting supply chain integration on firm performance. Firms are increasingly discovering the advantages that can be gained by seeking mutually beneficial partnerships with other supply chain members (Powell et al., 1996; Dyer & Singh, 1998; Dyer & Nobeoka, 2000; Mentzer et al., 2000; Kotabe et al., 2003; Wisner, 2003; Liker & Choi, 2004; Myers & Cheung, 2008). The access and use of knowledge and resources of their supply chain partners allows them to better exploit and improve their capacities. To achieve such advantages, integrated partners should seek to synchronize processes, establish high levels of coordination, and share updated, relevant information (Handfield & Nichols, 1999; Frohlich & Westbrook, 2001; Wisner, 2003; Chen & Paulraj, 2004; Malhotra, Gosain, & El Sawy, 2005). They should systematically seek to create a value that could otherwise not be created by either partner independently (Zajac & Olsen, 1993). Conversely, the lack of supply chain integration may lead to increased inventories, poorer product availability, higher manufacturing cost, increased replenishment lead times, and a drop in profits (Lee, Padmanabhan, & Whang, 1997; Chopra & Meindl, 2007). Previous research (Lee et al., 1997; Frohlich & Westbrook, 2001; Sahin & Robinson, 2002; Vickery, Jayaram, Droge, & Calantone, 2003; Wisner, 2003; Chen & Paulraj, 2004; Liker & Choi, 2004; Modi & Mabert, 2007; Wang & Wei, 2007) has consistently shown that high levels of supply chain integration improve the operational performance of the committed partners. This effect is due to the intensive use of communication channels that permit partners to better synchronize their processes and better coordinate their activities. This directly translates into reduced variability that, in turn, leads to greater efficiency along with a cost reduction and improved lead time. The teamwork between supply chain partners

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also makes them more willing to help each other to cope with uncertainty from supply, manufacturing, and demand. They gain a better understanding about how the supply chain they belong to works and what role they play to strengthen supply chain capabilities. Partners therefore become more responsive to markets, having, for instance, shorter lead time, better service levels, greater product variety and so forth. Finally, fostering an environment of mutual support, integrated partners aim to become quality-oriented organizations that seek (i) the fulfillment of the quality standard of products, (ii) the reduction of shipments rejected and defect rates, and (iii) the increment of order entry accuracy. Effective supply chain integration thus seeks to improve participant performance with the main objective of offering better levels of service owing to the better use of capabilities, expertise, and technologies of committed partners within the partnership. H4: Supply chain integration should be positively related to improved operational performance.

RESEARCH METHODOLOGY Sample Selection and Data Collection It is difficult to obtain data on compensation systems, evaluation systems, decision making, and performance from SCEs because these matters are usually considered confidential, the information is sensitive, and unlike the case of many CEOs, the data are not publicly available. This study thus used multiple methods that combine surveys and interviews with SCEs and human resources management (HRM) executives as well as secondary data in order to reduce source bias and obtain reliable and valid data. A pilot survey was designed and developed from a thorough literature review. The survey was next validated through a pre-test. Comments from three academics, seven SCEs, and two senior consultants in the field of supply chain management (SCM) help to refine the research constructs. These individuals are members of the Supply Chain Management Interest Group (SCMIG); a discussion forum composed of executives from Spanish and multinational firms sponsored by IE Business School in Madrid. They closely reviewed and critiqued the pilot survey and offered several suggestions for improving its wording, design, and administration. Finally, we sent the definitive survey to the sample members, along with a cover letter explaining the purpose of the study and the assurance of anonymity for respondents. In some cases, SCEs in different industries were visited to collect data and interviewed to have in-depth information. Our population was primarily composed of manufacturing Spanish firms and subsidiaries of multinational companies operating in Spain. We selected these companies from the list of 5,000 large companies published by the respected Spanish business periodical Actualidad Economica. A three-member panel of experts with extensive experience in SCM and cognizant of the Spanish market selected 932 companies from this list using as a criteria the extent to which SCM is an important part of their operations. As noted later, most of these firms fell into the food & beverage, chemical & pharmaceutical, and automotive sectors. Most service


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organizations were deliberately excluded because they generally lack a tangible resource or because their SCM operations are menial, which could create differences in the nature of their interfirm collaboration requirements (Chase, Jacobs, & Aquilano, 2006). The data-collection process yielded 133 usable responses, for a response rate of 14.3%. This rate compares favorably with those reported by other studies conducted in Spain (Cruz et al., in press; Gallo & Villaseca, 1996) and experienced by other surveys in SCM issues (Wisner, 2003; Modi & Mabert, 2007; Sanders, 2007). This response rate is higher than “the 10–12% rate typical for studies that target executives in upper echelons” (McDougall & Robinson, 1990; Hambrick, Geletkanycz, & Fredrickson, 1993; Judge & Dobbins, 1995; Koch & McGrath, 1996; Geletkanycz, 1997, p. 622; Larraza-Kintana et al., 2007). Table 1 shows the profile of the sample, which reflects the diversity that exists among the participating firms, based on the number of employees, industry sectors, and annual sales. The targeted respondents consisted of SCEs at decision-making levels and in strategically oriented positions. The most common titles for these SCEs included corporate directors of purchasing, supply chain directors, and supply chain managers. In addition to the SCE survey, we also secured independent sources of data following guidelines from previous research (McDougall, 1989; Hitt, Hoskisson, Johnson & Moesel, 1996). First, we sent a questionnaire about SCE compensation

Table 1: Profile of the sample . Frequency (%) No. of employees 0–50 51–100 101–500 501–1000 >1000 Total

13 (9.8) 13 (9.8) 72 (54.1) 19 (14.3) 16 (12.0) 133 (100.0)

Industry sector Food and beverage Chemical and pharmaceutical Automotive Textile Beauty and hygiene Other industries Total

31 (23.3) 30 (22.6) 27 (20.3) 15 (11.3) 6 (4.5) 24 (18.0) 133 (100.0)

Annual sales 0–20 Million 20–50 Million 50–99 Million 100–500 Million >500 Million Total

5 (3.8) 33 (24.8) 49 (36.8) 39 (29.3) 7 (5.3) 133 (100.0)

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policies to the HRM directors of the responding firms. Completed responses from HRM managers were received from 26 of the 133 responding companies. These data were used to determine interrater agreement regarding the responses to survey items concerning compensation and employment risk between supply chain managers and human resources managers. Second, we accessed archival data for all of the responding firms. The secondary data came from the “Sistemas de Análisis de Balances Ibéricos” (SABI) database that compiles data from the annual reports filed by 949,246 Spanish firms in the year 2008. This database includes firm size, firm age, industry sector, financial information, operational rates, and other miscellaneous data for each company. Survey-based measures were cross-checked and validated with information from the SABI database in order to ensure reliable and valid data. This database was also used to check for a nonresponse bias and calculate several of the control variables, the environmental risk variable, and the objective operational performance measure used in the analysis.

Nonresponse Bias A nonresponse bias was tested by examining the difference between the answers of respondents and nonrespondents (Lambert & Harrington, 1990). A nonresponse bias was assessed using a t test, which showed no significant difference for firm size (p = .192), firm age (p = .292), and return on assets (ROA) (p = .092) between responding and no responding companies. A chi-square test, moreover, showed no significant differences for industry sector (χ 2 = 8.09, p > .05). These results collectively suggest that a nonresponse bias is not present in the data and that participating firms represented the population from which they were drawn. Measurement Scale Items The different objective and perceptual measures used in the study are described below. Compensation risk indicates the degree to which compensation is at risk of loss. To capture this measure, we asked the SCE to indicate the extent to which he/she assumes any type of risk to his/her base pay. Responses were made on a five-point scale (1 = no risk to my pay; 5 = extremely high risk to my pay). Responses to these questions were independently validated by calculating interrater agreement with responses from HRM managers in terms of risks to annual base salary (r = .79; p < .001), which as noted earlier is the predominant pay form for SCEs. It is important to note that previous research on CEO pay has operationalized compensation risk for various forms of retribution (e.g., salary, short term bonus and stock options). Because SCEs’ salary on average represents the largest percentage of their total pay (94% in U.S. according to salary.com’s survey 2008, and possibly higher in Spain), we decided to focus our attention on annual base salary. This is also entirely consistent with the BAM, which treats base salary as “essential compensation” and variable forms of pay (e.g., long-term bonuses) as “nonessential compensation.” Employment risk values the probability of termination for SCEs. To measure this risk, we asked the SCE to indicate the degree to which he/she has


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failed to satisfy performance targets during the most recent review periods. The response format for this item ranged from 1 (strongly disagree) to 5 (strongly agree). Higher failure in achieving the performance target as part of the appraisal process is a warning sign that superiors are unhappy with the subordinate’s progress and hence places this person’s job at risk (i.e., it increases the probability of termination or replacement; Gomez-Mejia, Balkin, & Cardy, 2009). Hence, below-target performance as reported by the SCE should be an indicator of higher employment risk as perceived by the respondent. Answers to this question were recorded into a dummy variable, where “one” indicates SCEs who responded with “strongly agree” to the statement and “zero” represents those who selected answers 1 through 4 on this scale. Our focus on the extreme case (those who chose “strongly agree”) was to ensure that we had captured those SCEs for whom termination was most likely, as previous research had suggested (Larraza-Kintana et al., 2007). The probability of termination for the SCE is firm specific and depends on a variety of factors, including, for instance, evaluation systems. In general, quantitative goals transfer more risk to executives as they are made accountable for observed performance outcomes even if these are not entirely under their control (Gomez-Mejia, Berrone, & Franco, 2010). At the other end, so-called strategic goals tend to be more process oriented, subjective, and flexible. This allows for the possibility of receiving a good ex post evaluation if the original plan was reasonable, given the information available at the time, even if observed results or final outcomes were disappointing and suggest a reconsideration of the strategic plan (Baysinger & Hoskisson, 1990). Consistent with this and supporting the construct validity of the employment risk measure, we found that the “below performance target” responses were positively correlated (r = .294; p < .001) with another survey item that measured the extent to which the evaluation system of the SCE is based on quantitative goals rather than qualitative (strategic) goals. As an external validation of this measure, we asked HRM managers to indicate if the SCE’s evaluation system was based on strategic goals or if it was mainly focused on meeting operational performance targets. These responses were validated with those from SCEs, showing a strong and statistically significant correlation (r = .73, p < .001). Environmental risk values the degree of volatility of organizational performance. Following previous studies to measure environmental risk (Bromiley, 1991; Haleblian & Finkelstein, 1993; Miller et al., 2002; Miller & Chen, 2004), we used the coefficient of variation of ROA (obtained from SABI) within each industry sector over the 5 years prior to our survey (2003–2007). We first identified the firm’s industry sector following the classification made by Spain’s National Institute of Statistics and then calculated the average coefficient of variation for all firms within each sector. As many of these firms were not publicly traded, stock market return data could not be used to calculate a coefficient of variation. Supply chain integration measures the degree to which the firm and its collaborative supply chain partner (i.e., its supplier) jointly work in the pursuit of improved performance. We developed a multiitem scale based on a thorough

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literature review (Dyer & Singh, 1998; Handfield & Nichols, 1999; Frohlich & Westbrook, 2001; Wisner, 2003; Chen & Paulraj, 2004; Modi & Mabert, 2007) and refined it based on feedback obtained from a pre-test. This resulted in four items measuring the extent to which (i) partners share relevant information to evaluate and adjust to market needs, (ii) partners exchange information about the technology used, (iii) partners allocate resources in encouraging coordinated activities, and (iv) partners exchange information frequently and informally, which allows a better knowledge of their needs. Each item was rated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). When these items were factor-analyzed (with a varimax orthogonal rotation), only one factor reached an eigenvalue higher than 1.0, explaining 53% of the variance. The reliability of this scale was measured by Cronbach’s alpha, achieving a satisfactory value of .70. Operational performance captures the operative improvements owing to teamwork between the firm and its collaborative supplier. To measure it, we used two different, but related measures of operational performance. The first (subjective) measure came from the survey and has been adopted from previous studies that capture the overall operational performance using multiple items (Scannell, Vickery, & Dröge, 2000; Frohlich & Westbrook, 2001; Wisner, 2003; Malhotra et al., 2005; Modi & Mabert, 2007; Sanders, 2007). Using a five-point scale, we asked SCEs to indicate the extent to which they believe that their firms’ operations improve as they engage in supply chain integration in terms of (i) higher productivity, (ii) shorter lead time, (iii) improved quality, and (iv) better service levels. Only one factor reached an eigenvalue higher than 1, explaining 57% of the variance. The Cronbach’s alpha value of this construct was .72. The second (objective) measure came from the SABI database and has been extensively used as a measure of performance (Swamidass, Nair, & Mistry, 1999; Frohlich & Westbrook, 2001; McKone, Schroeder, & Cua, 2001; Malhotra et al., 2005; Mitra & Singhal, 2008) and considered as a strategic performance ratio (Griffis, Cooper, Goldsby, & Closs, 2004; Griffis, Goldsby, Cooper, & Closs, 2007). Inventory turnover represents the degree to which a firm’s supply chain processes are streamlined and integrated with those of its partners. A high level of stock turnover indicates a greater efficiency in demand management, the use of advanced supply chain technology, and a lower risk of loss through unsaleable stock. However, an inventory turnover that is out of proportion to industry norms may suggest losses due to shortages and poor customer-service. We therefore used stock turnover as an important operative measure that indicates the coordinated effort within supply chain operations. Data about the stock turnover rate of each firm came from the SABI database for the year 2007.

Control Variables This study included several relevant control variables. These were classified into three types: one set concerns firm characteristics, another set captures industry membership, and the last set measures the presence of enablers for supply chain integration as suggested in the literature.


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Organizational-level variables Company size, firm age, and firm performance were controlled in this study. All of these variables may covary with employment and compensation risks and perhaps, supply chain integration (the dependent variable). Information on these variables was gathered through the SABI database. Firm size was measured as the standardized average value of annual sales. Previous research shows that managerial compensation covaries with firm size and that sales volume is the most commonly used proxy for size (Finkelstein & Hambrick, 1989; Tosi & Gomez-Mejia, 1989; Gray & Cannella, 1997; Bloom & Milkovich, 1998; Miller et al., 2002; LarrazaKintana et al., 2007). Employment and compensation risks also tend to be lower in larger, more established companies (Balkin & Gomez-Mejia, 1987). Moreover large firms differ from smaller firms in their supply chain relationships due to larger budgets and differences in the power they exert in these relationships (Subramani & Venkatraman, 2003; Benton & Maloni, 2005) as well as their greater experience in developing strategic partnerships. Firm age was measured as the standardized value of number of years the company has been in business since its founding. Older firms tend to exhibit higher employment stability and lower compensation risk than younger firms (Balkin & Gomez-Mejia, 1990). Lastly, firm performance was measured using average ROA for the 2003–2007 period. More profitable firms may be able to pay more and have a more stable compensation and employment system (Anderson, Banker, & Ravindran, 2000). They can also afford to devote more resources to foster supply chain integration and to absorb losses if the integration does not produce the desired results. Industry membership Industry sector was controlled in order to partial out the influence of industry-level effects on managerial pay and employment risk as previous research indicates (Bloom & Milkovich, 1998; Yanadori & Marler, 2006). Previous research also suggests that firms in some industries, such as the automotive sector, engage in more integration within the chain (Dyer, 1996; Vickery et al., 2003). In the SABI database, each firm reports its industry membership based on the categorization supplied by Spain’s National Institute of Statistics. As the samples in some of the industry sectors were small, we had to group the 62 registered categories into four clusters: food and beverage (23.3%), chemical and pharmaceutical (22.6%), automotive (20.3), and others. Because each sector was treated as a dummy variable, it was necessary to omit one cluster from analysis. The omitted sector was that represented by “others” that included 33.8% of our sample. Enablers of supply chain integration This group of control variables was included with the main purpose of capturing the unique and additive impact of compensation and employment risks and their interactive effects with environmental risk on supply chain integration, after partialling out the influence of enablers such as trust, commitment and information technology, which have been amply studied as key factors of supply chain integration (Morgan & Hunt, 1994; Monczka et al., 1998; Mentzer et al., 2000;

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Sahin & Robinson, 2002; Kotabe et al., 2003; Vickery et al., 2003; Saeed, Malhotra, & Grover, 2005; Klein, Rai, & Straub, 2007; Sanders, 2007; Wang & Wei, 2007). Trust was measured by a seven-item scale previously validated by Zaheer and Venkatraman (1994); Siguaw, Simpson, and Baker (1998); and Morgan and Hunt (1994). The items on this scale measured the level of confidence that the buyer firm has in its supplier’s reliability, integrity, and fairness. Commitment was measured by a three-item scale adapted from Gilliland and Bello (2002) and Richey (2003). The items of this scale measured the extent to which the firm is willing to develop a long-term relationship with its collaborative supplier in the future and the extent to which it recognizes that the loss of its partner means an important loss in financial investments and knowledge developed during the relationship. Lastly, information technology captures the degree to which the firm can easily interact and coordinate efforts with its collaborative supplier through established and cost-efficient information systems. This was assessed with two survey items that measure the availability of electronic links (e.g., electronic data interchange) and the use of specialized technologies to share updated and relevant information within supply chain operations. The response format for each of these items ranged from 1 (strongly disagree) to 5 (strongly agree). The Cronbach’s alpha values were .86, .74, and .65 for trust, commitment, and information technology, respectively.

RESULTS Table 2 presents means, standard deviations, and correlations among all variables. None of the correlations among the independent and the control variables exceeds .70, and the highest correlation is between chemical and pharmaceutical sector and environmental risk (r = .59), indicating that multicollinearity is not a major problem for the regression analyses. All variables were centered prior to conducting a regression analysis (Cohen, Cohen, West, & Aitken, 2003), except the dichotomous measure for employment risk. The hypotheses were tested using a regression analysis. As is customary, control variables and main effects were entered first. Multiplicative terms were added later to examine the hypothesized interactions. Tables 3 and 4 report the results from the regression analysis with supply chain integration and operational performance as the dependent variables. These tables also report the changes in adjusted R2 at each step and the significance of each regression equation. Hypothesis 1 predicts a negative association between compensation risk and the SCE’s decision to foster supply chain integration. Consistent with prediction, this hypothesis was supported (ß = −.13, p < .05) suggesting that the higher the level of perceived compensation risk, the less likely the SCE promotes supply chain integration (see Table 3, model 2). Hypothesis 2 predicts that there is a negative relationship between the SCE’s employment risk and supply chain integration. The regression results support hypothesis 2’s prediction (ß = −.47, p < .05) (see Table 3, model 2). These findings collectively suggest that the higher the risk perceived by SCEs, either to compensation or employment stability, the less likely that they will allocate resources to encourage supply chain integration.

†

a

0.01 −0.24∗∗ 0.09 0.15 0.11 0.13 −0.09 −0.11 0.00 0.00

0.00

0.10 −0.03 −0.06 −0.02 0.17∗ −0.12 −0.01 −0.01 0.12 0.04

−0.02

0.42 1.17 0.51 0.93 1.03 1.1 0.30 0.71 0.78 0.69

0.23 1.82 3.99 2.67 2.89 3.16 0.11 1.58 3.57 3.54

15.22 13.43

−0.16∗ −0.02

2

0.09 −0.02 −0.15

1

3.06 0.93 29.21 21.00 0.23 0.42 0.20 0.39

SD

0.05

−0.29∗∗ 0.26∗∗ −0.06 0.15 −0.13 0.00 −0.02 0.54∗∗ −0.03 0.10

−0.27∗∗

3

−0.08

−0.26∗∗ 0.00 0.01 −0.06 0.17∗∗ −0.07 0.08 −0.59∗∗ 0.09 −0.08

4

−0.12

−0.07 0.06 0.13 −0.03 0.02 −0.01 −0.29∗∗ −0.05 0.11

5

Correlations

0.02

−0.06 0.03 0.05 −0.15† 0.05 0.12 −0.17∗ 0.07

6

Size was recoded: (1) 0–20 million, (2) 20–50 million, (3) 50–99 million, (4) 100–500 million, (5) >500 million. p < .1, ∗ p < .05, ∗∗ p < .01.

1 Size 2 Age 3 Food & beverage sector 4 Chemical & pharmaceutical sector 5 Automotive sector 6 Return on assets (ROA) 7 Trust 8 Commitment 9 Information technology 10 Compensation risk 11 Employment risk 12 Environmental risk 13 Supply chain integration 14 Operational performance (subjective measure) 15 Operational performance (objective measure)

Mean

Table 2: Descriptive statistics and correlations. 8

0.15

0.10

0.00 −0.02 0.00 0.02 0.15 ∗ −0.18 −0.08 0.03 0.00 0.26∗∗ 0.13 0.45∗∗ 0.15

7

0.09

0.06 −0.09 −0.25∗∗ 0.07 0.00

9

−0.05

−0.31∗∗ 0.03 −0.08 −0.14

10

12

−0.05

0.05

0.00 −0.26∗∗ −0.09 0.23∗∗ 0.06

11

0.16†

0.22∗

13

0.12

14

Villena, Gomez-Mejia, and Revilla 651

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Table 3: Regression analysis examining the effects of compensation, employment, and environmental risk on supply chain integration. Supply Chain Integration Variables Control Size Age Food & beverage sector Chemical & pharmaceutical sector Automotive sector Return of assets (ROA) Trust Commitment Information technology Main effects Compensation risk Employment risk Environmental risk Moderator effects Compensation risk × Environmental risk Employment risk × Environmental risk R2 (adjusted) R2 (adjusted) change R2 F p value (change) †

Model 1 B

Model 2 B

Model 3 B

.13∗ −.13∗ .06 .24 −.16 −.14∗ .49∗∗ .15∗ .01

.10† −.12† .09 .10 −.21 −.14∗ .42∗∗ .15∗ .01

.10 −.10 .08 .03 −.23 −.12∗ .45∗∗ .14∗ .03

−.13∗ −.47∗ −.12

−.13∗ −.43∗ −.12

18.20% 3.80% 51.40% 3.21∗∗∗ .001

−.04 −.67∗∗ 22.10% 3.90% 56.00% 3.41∗∗∗ .001

14.40% 45.73% 3.23∗∗∗ .001

p < .1, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

The results regarding the interaction effects of compensation, employment, and environmental risk on supply chain integration (Hypotheses 3a and 3b) are presented in model 3 of Table 3. The equation for the full model was statistically significant at p < .0001. The adjusted R2 increased from 18.2 (model 2) to 22.1 (model 3) when the multiplicative terms are added to the equation. The change in adjusted R2 was statistically significant at p < .001. However, only one of the moderating effects was significant. Hypothesis 3a, which predicts that the negative effect of compensation risk on promoting supply chain integration will be greater for a higher degree of environmental risk, was not statistically supported (ß = −.04, ns). Hypothesis 3b, which predicts that the negative effect of employment risk on promoting supply chain integration will be greater under higher levels of environmental risk, was strongly supported. The results show that this interaction term was significantly negative (ß = −.67, p < .01), suggesting that when environmental risk is high, employment risk has a more negative effect on the pursuit of supply chain integration than when environmental risk is low. The reader should note that these results are immune to method variance as the indicator of employment risk comes from the SCE (which is highly correlated with the independent assessment


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Figure 2: Hypothesized employment risk and environmental risk interaction plot.

by the HRM executive) and the environmental risk indicator is obtained from the SABI database. The results thus suggest that when it comes to an assessment of the risk bearing attached to environmental volatility by the SCE, the main concern is losing his/her job rather than losing base pay (of course, the former implies forfeiting all base pay, which is a more severe threat). Figure 2 shows a plot of this interaction effect, using procedures by Aiken and West (1991). Turning our attention to the control variables (see Table 3, model 1), it is interesting to note that, as expected, firm size, trust, and commitment are positively related to supply chain integration. Surprisingly, firm age has a negative impact on supply chain integration, perhaps indicating that more traditional organizations with well-established routines exhibit greater inertia and thus higher reluctance to engage in joint decision making with supply chain partners. Firm performance (ROA) also shows a negative impact on integration, perhaps insinuating that firms that are doing well might see less gain in collaborative efforts within the supply chain. Hypothesis 4, which predicts a positive association between supply chain integration and operational performance, was supported. There was a positive and significant correlation between supply chain integration and our subjective measure of operational performance (r = .22, p < .05) assessed by the SCE and our objective measure of stock turnover (r = .16, p = 0.08) obtained from the SABI database (see correlation matrix, Table 2). We also regressed the effects of supply chain integration on operational performance after first introducing control variables related to firm characteristics and industry (see Table 4). We did not include the group of control variables related to enablers in Table 4 because (i) theory tells us that they are conceptually related to factors that enhance supply chain integration rather than performance outcomes, (ii) they were already introduced in the first regression (Table 3) in which the focus was to analyze the additive impact of

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Table 4: Regression analysis examining the effects of supply chain integration on operational performance. Operational Performance Subjective Measure Variables

Objective Measure

Model 1

Model 2

Model 1

Model 2

Control Size Age Food & beverage sector Chemical & pharmaceutical sector Automotive sector Return of assets (ROA) Main effect Supply chain integration

B .02 .05 .14 −.02 .19† .05

B −.01 .07 .13 −.04 .20† .08

B −.01 .01 −.07 −.16 −.18 .03

B −.03 .01 −.07 −.18 −.17 .05

R2 (adjusted) R2 F p value (change)

.00% 3.70% 1.040 .403

†

.25∗∗ 6.20% 11.34% 2.11∗ .005

.17† 1.00% 3.40% 1.120 .741

2.40% 6.40% 2.01† .080

p < .1, ∗ p < .05, ∗∗ p < .01.

compensation risk and employment risk and their interactions with environmental risk that goes further of the effects of previous enablers studied in the literature, and (iii) this would produce an unnecessary loss of degrees of freedom. The regression results show that supply chain integration was significantly and positively related to our subjective measure (ß = .25, p < .01) and our objective measure (ß = .17, p = .07) of operational performance (see Table 4, model 2). Once again the reader is reminded that “common source” cannot account for any of the latter variance or statistical significance as the objective measure (stock turnover) was gathered independently from the SABI database.

DISCUSSION Increasingly SCEs realize that their organizations’ operations are not run independently from their supply chains. They should allocate resources to synchronize processes and share knowledge with other key supply chain partners in order to improve their firms’ operational performance. Simultaneously, they have to deal with the risks resulting from the intense level of collaborative knowledge sharing and the high level of relationship-specific investments. This uncertain situation limits SCEs’ risk taking because they may see their personal wealth threatened if the expected result of supply chain integration does not occur. In order to promote supply chain integration and ensure continued firm competitiveness and success, firms must therefore develop an employment and compensation system that reduces executive risk bearing and thus promotes risk taking for their SCEs.


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Previous research has consistently suggested that integrated supply chains improve the operational performance (Dyer & Nobeoka, 2000; Frohlich & Westbrook, 2001; Vickery et al., 2003; Wisner, 2003; Liker & Choi, 2004; Modi & Mabert, 2007; Myers & Cheung, 2008). But what are the factors that foster or hinder integration? By examining human resource factors using the BAM, this article attempts to explain how to induce SCEs to foster supply chain integration under the perspective of managerial risk bearing. An extrapolation of our findings suggests that the SCE is more willing to take the risky decision of fostering supply chain integration when a firm’s employment and compensation system assures his/her essential (base) pay and also emphasizes the strategic desirability of his/her decisions to the firm’s interests in the appraisal process rather than meeting quantitative operational targets that augment employment risk. In these cases, the idea is to reduce executive risk exposure (e.g., by decoupling employment risk from the pursuit of supply chain integration) in order to change the SCE’s risk preference. Clearly, our results suggest that SCEs will tend to avoid supply chain integration if this involves a personal risk, no matter how beneficial the SCE believes this integration might be for the firm. Almost 20 years ago, Baysinger and Hoskisson (1990) suggested that boards of directors and upper echelons should rely mainly on strategic rather than on numerical control systems. While they did not use our language nor did they apply our theoretical arguments, these authors’ recommendations are congruent with our logic and results. Under strategic control systems, judgments are made about the quality of decisions made by SCEs before implementation, considering the context facing the firm. Hence, the use of strategic controls by superiors relieves SCEs of achieving short-term operative performance outcomes where their influence may only be partial (e.g., when firms operate in volatile environments), and instead focuses evaluation on means that can sustain long-term competitiveness (e.g., supply chain integration process), which can be subjectively assessed. This article thus suggest that a firm’s evaluation system should rely on strategic control systems with top managers actively giving support to SCEs’ long-term oriented decisions, such as supply chain integration, through continuous advice and encouragement. Based on the BAM, this research suggests that by insulating a basic compensation from the threat of loss, we reduce the capacity of SCEs to protect their perceived current wealth and, as a consequence, they are more confident of pursuing high-return, albeit risky, supply chain partnerships. Doing this, the firm provides incentives for the SCE that are tied to its performance, without the threat of loss that pursuing strategies such as supply chain integration entails. In the same vein, the lack of strategic goal setting for SCM, the ambiguity of evaluation criteria, and the focus to evaluate and reward primarily on meeting (short-term) performance criteria make SCEs less willing to put efforts in developing important supply chain partnerships, which imply surrendering their potential benefits. This article also extends the BAM developed by Wiseman and Gomez-Mejia (1998) by incorporating the moderating role of environmental risk. Firms operating in volatile environments have more difficulty in assuring reliable compensation and employment stability for their executives; a situation that is taken into account in the decision-making process by SCEs. The results showed that loss-averse SCEs exhibit less risk-taking behavior in highly volatile markets than stable markets.

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This suggests that firms operating in dynamic markets that seek to promote supply chain integration (as a way to gain control over supply uncertainty and share risks with partners) should attempt to assure essential compensation and, more importantly, focus on the quality of the decision-making processes of SCEs as they may be blamed for poor results even if performance outcomes are largely beyond their control. This article also uses a new context to examine managerial risk bearing. Past research on compensation design has been almost exclusively concerned with CEOs. A major novelty of this study is its focus on the SCE, who plays an increasingly pivotal role in ensuring continued firm competitiveness and success (Mangan & Christopher, 2005). We have analyzed how a firm’s appropriate employment and compensation system induces the SCE to foster supply chain partnerships, which improve operational performance. This is important because increasingly competition actually does take place between supply chains and, therefore, to have richer insights about which human resource factors facilitate supply chain integration (despite the influence of enablers such as trust and commitment) becomes vital for researchers and practitioners. Finally, this article confirms previous empirical findings that supply chain integration improves operational performance of committed partners. Lastly, it is important to remind the reader that even when the SCE believes that supply chain integration has a salutary effect on operational performance (as captured by the four Likert type items) this individual is willing to decide against it when compensation or employment security is at risk. This is consistent with BAM predictions that excessive agent risk bearing increases agency costs (in our case foregoing the purported advantages of supply chain integration).

Limitations and Future Research The limitations of this research provide fertile areas for further research. First, this article emphasizes the new tendency toward greater collaboration between supply chain partners, but such collaborations are difficult where competing supply chains have common suppliers or where suppliers compete with customers (Rice & Hoppe, 2001). Our conclusions should therefore be interpreted with caution in those situations. Second, although we used multiple respondents and secondary data to strengthen the veridicality of the results, we examined the viewpoint of the SCE of a buying firm in relation to its main supplier to evaluate supply chain integration. Future studies should also collect data from both enterprises involved in the partnership to ensure a balanced view. Third, we analyzed a specific situation of SCEs’ risk-taking behavior regarding supply chain integration. Although we have no reason to think that our findings can change for other types of risky decisions, it might be interesting to confirm the strength of our findings with other similar decisions involved in SCM. Fourth, this article does not address advantages of supply chain integration that go beyond operations management to areas such as innovation and marketing. Executives of these areas could be interested in developing joint strategies with supply chain members to achieve their firms’ goals. It would be interesting to consider how to design a compensation plan for teams of executives, in addition


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to individual compensation studied here. Relatedly, we have focused our compensation analysis on base pay but it would be interesting to examine the role of other managerial incentives that Wiseman and Gomez-Mejia (1998) refer to as “nonessential pay.” We have argued that a firm’s employment and compensation system should assure reliable pay that is perceived as essential to the SCE’s standard of living. But layering variable-pay incentives on top of essential (base) pay may persuade the SCE to seek higher return, albeit risky projects such as supply chain integration. Fifth, the variables of compensation and employment risk in this research are measured by single items, and as such may be subject to a measurement error (Churchill, 1979). Future research might develop multiple-item scales that can better specify the constructs’ domain and improve their validity and reliability. Finally, this study does not analyze the influence of the decisions of SCEs on the bottom line, for example on their firms’ stock price. Future research could analyze the extent to which SCEs, unlike other senior executives, can influence their firms’ stock prices. There are many questions that arise from this study that are amenable to future multidisciplinary research. These include examining the conditions when bilateral dependence, power imbalance, and information asymmetries among supply chain partners increase the SCE’s risk bearing; how various types of trust (e.g., ability, benevolence, and integrity; Cruz et al., in press) influence the perceived vulnerability (and hence risk bearing) of the SCE; how the SCE sets the reference points for gains or losses (Wiseman & Gomez-Mejia, 1998) in supply chain partnerships; and the extent to which the organizational culture is tolerant of failure and hence allows the SCE to take more risks within the integration process.

Conclusions In this article, we state that the propensity of a firm to promote supply chain integration is related to the attitudes of the SCE toward risk and that these attitudes will change systematically according to the three kinds of risk-bearing analyzed. This research also suggests that an appropriate employment and compensation system can help to induce SCEs to exhibit risk-taking behavior as exemplified in supply chain integration. Such risk-taking behavior is also likely to have positive consequences for the firm (such as salutary impact of supply chain integration on operational performance). Our theoretical arguments and findings indicate that external context facing the firm is a critical element to consider when designing the compensation and employment contract for SCEs. High levels of firm-specific risk augment executive risk bearing and, thus, reduce risk-taking behavior. This suggests that firms operating in highly uncertain markets that seek to develop strategic partnerships within the supply chain should put more emphasis on evaluating the decision-making process of the SCE, rather than on the extent to which performance outcomes have been met (because uncertain environments allow SCEs little control over the operational performance). [Received: January 2009. Accepted: July 2009.]

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Tosi, H. L., & Gomez-Mejia, L. R. (1989). The decoupling of CEO pay and performance: An agency theory perspective. Administrative Science Quarterly, 34(2), 169–190. Tosi, H. L., Werner, S., Katz, J. P., & Gomez-Mejia, L. R. (2000). How much does performance matter? A meta-analysis of CEO pay studies. Journal of Management, 26(2), 301–339. Vickery, S. K., Jayaram, J., Droge, C., & Calantone, R. (2003).The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships. Journal of Operations Management, 21(5), 523–540. Wang, E. T. G., & Wei, H.-L. (2007). Interorganizational governance value creation: Coordinating for information visibility and flexibility in supply chains. Decision Sciences, 38(4), 647–674. Williamson, O. E. (1985). The economic institutions of capitalism. New York: The Free Press. Wisner, J. D. (2003). A structural equation model of supply chain management strategies and firm performance. Journal of Business Logistics, 24(1), 1–26. Wiseman, R. M., & Bromiley, P. (1996). Toward a model of risk in declining organizations: An empirical examination of risk, performance and decline. Organization Science, 7(5), 525–543. Wiseman, R. M., & Gomez-Mejia, L. R. (1998). A behavioral agency model of managerial risk taking. Academy of Management Review, 23(1), 133–153. Yanadori, Y., & Marler, J. H. (2006). Compensation strategy: Does business strategy influence compensation in high-technology firms? Strategic Management Journal, 27(6), 559–570. Zaheer, A., & Venkatraman, N. (1994). Determinants of electronic integration in the insurance industry: An empirical test. Management Science, 40(5), 449–566. Zajac, E. J., & Olsen, C. P. (1993). From transaction cost to transactional value analysis: Implications for the study of interorganizational strategies. Journal of Management Studies, 30(1), 131–145. Verónica H. Villena is a PhD candidate at IE Business School in Spain. Her current research interests include social capital in buyer–supplier relationships, supply chain integration, and supply networks. She received her MBA from IE Business School and her ISO 9000 leader auditor certification from IRCA/SGS. She has participated in various conferences, including those organized by the Academy of Management, Production and Operations Management Society, Decision Sciences Institute, and European Operations Management Association. Luis R. Gomez-Mejia holds the Benton Cocanougher Chair in Business at the Mays Business School, Texas A&M University. Prior to that he held faculty positions at Arizona State University, University of Colorado-Boulder, and University of Florida. He is a fellow of the Academy of Management and member of the “Hall of Fame” of the Academy of Management (which includes 33 out of approximately


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10,000 members in the Academy of Management). He has published more than 200 articles and 12 books in the area of macro human resources and corporate governance. His research has been cited more than 6,000 times in the literature, placing him as one of the most cited management scholars. Elena Revilla is professor of operations management at IE Business School in Spain. She received her PhD from the University of Valladolid and an MA in Science and Technology Management from Carlos III University, Madrid. She was a postdoctoral fellow in the Kenan Flager Business School, University of North Carolina in Chapel-Hill. She received the 1996 award for the best doctoral dissertation granted by the Club Gestión de Calidad. She is the author of the book Factores determinantes del aprendizaje organizativo (Club Gestión de la Calidad, 1996). Her work has appeared in several leading journals.