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Goals, Conflict, Politics, and Performance of Cross-Functional Sourcing Teams—Results from a Social Team Experiment Henrik Franke and Kai Foerstl German Graduate School of Management and Law (GGS)

S trategic sourcing is carried out in cross-functional teams to account for the complexity and multidimensionality of modern procurementdecisions. However, such teams not only enable the integration of distinct interdependent skill sets and viewpoints, they are also character- ized by functional goal misalignment. We focus on the resulting behavioral challenges, namely conflict and politics, and their effects on team satisfaction and rationality, which ultimately leads to observed outcomes. We test our hypotheses in a structural equation model based on data gathered from 468 participants in a social team experiment. We find a mediated effect of goal misalignment on political behavior via two types of team conflict. Political behavior, in turn, obstructs rational team sourcing decisions and reduces team members’ satisfaction with the process. Our study indicates that behavioral challenges in the framework of Organizational Buying Behavior not only co-occur but affect each other via mediation. Hence, managers need to closely monitor the escalation chains’ origin, task conflict, which constitutes a necessary condition for fur- ther emotional dissent and political biasing. We contribute to the understanding of the challenges in cross-functional sourcing teams, thereby providing advice to executives in their pursuit to rationalize and improve sourcing team decisions and their outcomes.

Keywords: Global sourcing; Team decision-making; Experimental design; Organizational Buying Behavior

INTRODUCTION

As a result of the outsourcing waves of recent decades, organiza- tional buying has become an integral element of firms’ strategies and operations (Carter and Narasimhan 1996; Trent and Mon- czka 2003). At the same time, deciding where to buy from has become more and more complex (Riedl et al. 2013). In order to make effective buying (or sourcing) decisions, various functions, such as purchasing, R&D, and marketing, have to align and inte- grate (Trent and Monczka 1994; Mentzer et al. 2008). Thus, members of sourcing teams typically possess unique, nonredun- dant skills, which create interdependence in supply chain man- agement (SCM) decision-making processes, requiring functional representatives to exchange and jointly process information to leverage their combined expertise (Thompson 1967; Moses and �Ahlstr€om 2008).

It is well established that cross-functional integration con- tributes to firm performance and supply chain effectiveness (Flynn et al. 2010), yet diverse sourcing teams also face chal- lenges from functional misalignment and conflicting motives that potentially interfere with rational decision-making processes (Moses and �Ahlstr€om 2008; Kaufmann et al. 2012). Thus, cross- functional sourcing teams face a coopetition situation where cooperation is required (i.e., from executives), but functional managers still compete for influence in political fights (Rajala and Tidstr€om 2017). For instance, technical functions politically defend their interests by cropping available supply options to R&D’s preferred supplier via the design of sourcing requirements (Stanczyk et al. 2015). In a prestudy with a German multina- tional automotive supplier, a top-level supply chain manager sta- ted that, while the trade-offs between costs and technical

capabilities can theoretically be resolved throughout the decision- making process, personal animosities, and departmental silo- thinking prevent the required level of collaboration and conces- sion making in sourcing and other SCM committees.

Despite the acknowledgment of misaligned incentives, ineffec- tiveness, and conflict in cross-functional sourcing teams (Sheth 1973), empirical insights on the particular problems and their potential detrimental effects are still limited. Although metathe- ory in the field prominently discusses the team level (Schorsch et al. 2017), it remains empirically underrepresented given the traditional focus on organizational-level buyer–supplier relations (e.g., Zimmermann and Foerstl 2014). Today, especially conflict, self-serving politics, and their mutual relationship in SCM team, decision making remains underrepresented yet impactful phe- nomena (Bai et al. 2016; Thornton et al. 2016). Given initial case study evidence on conflict (e.g., Oliva and Watson 2011) and politics in SCM teams (e.g., Stanczyk et al. 2015), we can suspect that both have the potential to obstruct modern sourcing organizations from reaping the benefits of cross-functional inte- gration (Turkulainen and Ketokivi 2012). Considering these research gaps, we pose the following research questions: (1) How do (mis)aligned goals affect conflict and politics in cross- functional sourcing teams and how are both related to each other? (2) What are the implications of conflict and politics in cross-functional sourcing teams?

Figure 1 shows the framework that derives from our research questions. By further specifying and testing the model we derive from the framework above, we contribute generally by answering the call for more “people-focused” SCM research, which is the primary underestimated SCM research theme today (Wieland et al. 2016; Schorsch et al. 2017). More specifically, this paper advances our conceptual and empirical understanding of Organi- zational Buying Behavior (OBB) as it begins to unravel the hith- erto ignored internal relationships among OBB concepts on conflict, politicking, and negotiation (Sheth 1973). Specifically, we jointly consider conflict, politics, and their relationship in SCM decision-making processes. Thus, our study deepens and

Corresponding author: Kai Foerstl, German Graduate School of Management & Law (GGS), Bildungscampus 2, 74076 Heilbronn, Germany; E-mail: [email protected]

Journal of Business Logistics, 2020, 41(1): 6–30 doi: 10.1111/jbl.12225 © 2019 Council of Supply Chain Management Professionals

extends the original OBB theory and taps into the relatively uncovered area of team politics (Vigoda-Gadot and Vashdi 2012). Accordingly, our contribution can be understood as middle-range theory elaboration of OBB that enlightens a deeply rooted practi- cal problem in firms’ sourcing and SCM teams (Garver 2019).

Moreover, this is the first study to examine team-level politics with large-N empirical methods in SCM and continues the yet emerging literature on conflict and politics in sourcing teams (e.g., Marshall et al. 2015; Stanczyk et al. 2015). Hence, we pro- vide empirical evidence on political decision-making processes at the team level and add detail to the benefits and drawbacks of the cross-functional organizational buying centers. Considering the study context, our results allow for careful extrapolation to other cross-functional, temporary, and interdependent team set- tings in SCM research such as outsourcing or location decisions.

The remainder of the paper is structured as follows: First, we highlight how exactly our study relates to the sourcing and OBB literature as our conceptual foundation, review the emerging SCM team research, and give an overview of today’s ambiguous research on the “conflict–politics link.” Thereby, we define our concepts of interest before deriving our hypotheses based on extant SCM and general management insight. We continue to present our methodology and channel our results into a discus- sion of findings based on various robustness checks and comple- mentary analyses. We conclude with the implications of our research and future research opportunities that may fill the limita- tions that we accepted in this study.

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT

Context and theoretical grounding

Strategic decision about the upstream supply chain is usually car- ried out in cross-functional sourcing teams (Driedonks et al. 2010; Flynn et al. 2010). Functional representatives come from diverse backgrounds such as purchasing, marketing, or R&D and

usually have divergent goals, expertise, or decision-making styles (Moses and �Ahlstr€om 2008; Kaufmann and Wagner 2017). In addition, cross-functional sourcing teams are examples of task- based functional integration that serves to solve a particular sour- cing task through temporal rather than permanent integration (Miller and Dr€oge 1986; Foerstl et al. 2015). Hence, such a team can be defined as a collection of individuals with distinct nonre- dundant skills from different units with a common purpose of enhancing the performance of a particular supplier selection (adapted from Mohsen and Eng 2016). Conversely, collaborative planning, forecasting, and replenishment would rather be imple- mented in permanent “embedded” (i.e., permanent) teams (Foer- stl et al. 2015). Our study focuses on temporal “task-based” and interdependent sourcing teams and the vital impact of their deci- sions on modern business organizations (Barney 2012; Riedl et al. 2013). Our idea of a sourcing team thus reflects the notion of the buying center in Organizational Buying Behavior (OBB).

Organizational Buying Behavior literature has established that corporate buying is a multidimension, multiperson, and thus mul- tiperspective process, laying grounds for behavioral research in sourcing teams (Webster and Wind 1972; Sheth 1973). Interest- ingly, original OBB models do account for conflict, negotiation, bargaining, and politicking as irrational sources of inefficiency in buying centers (Sheth 1973; Johnston and Lewin 1996). The general model in Webster and Wind (1972) speaks explicitly of conflict and so-called “personal–political tactics” that can mani- fest in “reliance on informal relationships and friendships to get decision made and an exchange of favors with other members of the buying center” (p. 18). Despite their acknowledgment, we lack empirical evidence on those challenges and how they may interact in buying processes. Consistently, Sheth (1996) con- cluded that the OBB research stream has not been fully exploited, particularly where it relates to global sourcing teams. Studies have suspected that conflict and political dynamics affect purchasing and SCM decisions (Stank et al. 2001; Moses and �Ahlstr€om 2008), yet hitherto not focused much on them but often focused on the individual decision maker or the organiza- tion as a whole (Schorsch et al. 2017).

Figure 1: Research framework.

Cross-Functional Sourcing Teams 7

Our study builds on the understanding of sourcing as stepwise incremental and partly rough process whose actors are highly bounded in their efforts to decide rationally (Makkonen et al. 2012). We acknowledge the group or team focus necessary to build knowledge on firm buying, consistent with earlier OBB studies viewing the buying center as a collection of individuals (Spekman and Stern 1979; Wilson et al. 1991). Furthermore, our study includes the notion that sourcing is executed by a group or team individuals that are set out to cooperate, yet may still feel cross- functional competition manifested in political game playing (Rajala and Tidstr€om 2017). Our “coopetition” perspective on organiza- tional buying including procedural rationality as process-focused outcome (see Figure 1) also relates our study to earlier OBB works that emphasized the need for analytical scrutiny and diligence in buying processes (Bunn 1994). With this process-focused approach, we continue developing operations and SCM scholarship toward considering processes more intensively as called for by Sil- ver (2004). Building on the OBB tradition, we extend the research on behavioral sourcing, which is presented next.

Literature review of relevant behavioral research in sourcing teams

As a starting point, it is important to acknowledge that sourcing teams are cross-functional, highly interdependent, and temporary in nature (Trent and Monczka 2003; Moses and �Ahlstr€om 2008; Stanczyk et al. 2015). Building on these tenets, exploratory inquiry into problems during cross-functional sourcing decisions detects that behavioral problems are significant challenges to sourcing teams (Moses and �Ahlstr€om 2008) and that conflict and politics, specifically, are possible impediments to effective cross- functional team or project work (Marshall et al. 2015; Stanczyk et al. 2015). Further studies show that the two major established types of conflict (i.e., task and relationship conflict) have differ- ent effects on sourcing teams and SCM teams and that heated emotions are usually unproductive in conflict situations (Andre 1995; Ehie 2010). Such relationship conflicts are “interpersonal incompatibilities among group members, which typically includes tension, animosity, and annoyance among members within a group” (Jehn 1995, p. 258). Additionally, rational task conflict can be defined as “disagreements among group members about the content of the tasks being performed, including differ- ences in viewpoints, ideas, and opinions.” Emotional conflicts in SCM teams can best be resolved by open and collaborative con- flict resolution strategies (Oliva and Watson 2011). Yet, qualita- tive observations indicate that collaboration can be obstructed, for instance by power imbalance among sourcing team members, functional goal misalignment, or political agendas of individual members (Stanczyk et al. 2015). So-called political behavior, being “intentional acts of influence to enhance or protect the self-interest of individuals or groups” (Allen et al. 1979, p. 77), is subjected to interact with formal power and to ultimately diminish decision outcomes. Consistent with the theorizing in Eisenhardt and Bourgeois (1988), Stanczyk et al. (2015) argue that a powerful actor is naturally able to influence other sourcing team members. Moreover, recent observational evidence suggests that managers’ political agendas vary in outsourcing projects depending on their underlying motivation, which remains inde- pendent of the formal power of actors (Marshall et al. 2015). In

particular, long-term oriented motivation, such as promoting one’s individual reputation, is suspected to contribute to decision outcomes, while, for example, short-term financial attainment goals ameliorate project outcomes.

Based on the review of the SCM team literature, we agree that “the impacts of political environments, behaviors, strategies, and skills on SCM remain empirically untested and poorly understood” (Thornton et al. 2016, p. 44). Additionally, organizational scholars have noted recently that “there is relatively little knowledge about the politics in and around teams” even outside the scope of SCM literature (Vigoda-Gadot and Vashdi 2012, p. 287). In an attempt to contribute to the knowledge on politics in sourcing teams but also on politics in teams in general, this study reports on a deci- sion-making environment free of hierarchies within the team, which have been put forward as natural driver of influence in buy- ing centers (Webster and Wind 1972). Instead, we focus on politi- cal behavior following extant research on sourcing team processes (Stanczyk et al. 2015) and its relation to team conflict, which is an under-researched area of inquiry. Notably, our results on the hypothesized sequence from conflict to politics are robust to alter- native measures of team politics based on an adapted perception of politics scale (Ferris and Kacmar 1992) as similarly applied at the team level by Maslyn and Fedor (1998).

The unexplored “Conflict–Politics Link”

Different to the underrepresentation of team politics research, scholars have added diverse studies on the impact of politics on individual employees and team conflict has traditionally received strong attention.1 Both streams of literature have brought for- ward a set of informative meta-analyses (De Dreu and Weingart 2003; Higgins et al. 2003; Miller et al. 2008; Bing et al. 2011; De Wit et al. 2012) and semantic reviews (Cohen and Bailey 1997; Doldor 2007; Mathieu et al. 2008; Weissenberger-Eibl and Teufel 2011). However, despite the intensive research, “conflict and politics have traditionally been treated as separate literatures, and the link to connect both is understudied” (Bai et al. 2016, p. 96; Franke and Foerstl 2018). Thus, besides our studies’ motiva- tion rooted in SCM literature and OBB concepts, it shall con- tribute to enhancing a unified discussion of both concepts bridging across disciplines. Thereby, we caution readers to con- sider the cross-functional, interdependent, and strategic context of our study and point to the idiosyncratic character of context in SCM studies (Durach et al. 2017).

Consistent with the assessment in Bai et al. (2016), we find few studies that consider both politics and conflict today. On the one hand, several studies observe conflicts and the resulting resource constraints resulting in politics as means to overcome obstacles and still achieve individual goals (Eisenhardt and Bour- geois 1988; Gargiulo 1993; Stanczyk et al. 2015). They consis- tently describe how disagreements create incentives for

1This study in informed by a previous systematic review of 166 publications seeking to bridge between the streams on con- flict and politics outside the scope of SCM journals. We omit a detailed review of this literature due to space limitations yet draw from that “general management” literature base in the following and throughout our theoretical argumentations.

8 H. Franke and K. Foerstl

individuals within social networks (i.e., a group) to use influenc- ing tactics on the constraining party itself or even other parties that can control the constraint. Consistently, Pfeffer and Salancik (1978) argue that politics are a result of the conflict for resources in an organization. Also, OBB models indicate that politics are applied to resolve or maneuver around conflicts in buying centers (Sheth 1973). On the other hand, we are unaware of studies that have directly observed the inverse namely politics causing con- flict. Instead, studies have highlighted that group-level politics lead to citizenship behavior as defense mechanism or means to gain legitimacy in the political environment (Maslyn and Fedor 1998). This tendency is also supported at the individual level along with the suggestion that politics in the organizational envi- ronment evoke employees to join the political game (Hsiung et al. 2012) or withdraw from the political environment once and for all (Chang et al. 2009).

To our knowledge, only Bai et al. (2016) have tested a poli- tics–conflict relation using cross-sectional survey methods. How- ever, the authors partly blend the concepts of functions’ goals (a rather static trait of the organization and the grown incentive structure) and politics (a dynamic behavioral phenomena), which our study seeks to disentangle: “political climate describes the team context in which members share the perception that individ- uals in their organization have opposing interests” (Bai et al. 2016, p. 97). Considering original definitions in Kacmar and Baron (1999) or the one used in this study (Allen et al. 1979), formal goals (a state) are not part of politics (a behavior or per- ception) per se unless individuals actively pursue diverse goals (a behavior) in political ways (Dean and Sharfman 1996). In order to substantiate and continue recent inquiry in the field of SCM (Stanczyk et al. 2015), we separate goals from politics and focus on a conflict–politics relation, yet deem an inverse link theoretically possible but conceptually less plausible for the tem- porary sourcing team context (also see Appendix E).

Finally, a branch of research on the conflict–politics link is concerned with political skill, essentially the ability to influence others effectively (Mintzberg 1983, 1985), yet seldom takes the group or team perspective. Pioneering team-level studies find that teams’ political skill increases agreement and performance of the team (Lvina et al. 2018). The larger individual-level research has highlighted that political skill can serve as employee’s coping resource for conflict in organizations (Perrew�e et al. 2004; Meurs et al. 2010) and increase popularity at work, which leads to less workplace conflict and ostracism (Cullen et al. 2014). Table 1 enables an easier access to the key pieces of our literature base.

Hypotheses development

We develop hypotheses around functional goal misalignment, con- flict, politics, and team outcomes. Thereby, we concentrate on the two main types of conflict, namely task and relationship conflict, as they have been popular subject to general management research (De Dreu and Weingart 2003; De Wit et al. 2012). Moreover, we focus on procedural rationality as “task-oriented” outcome and team mem- ber satisfaction as “non-task-oriented” outcome following the dis- tinction in Webster and Wind (1972). Procedural rationality is a verified team performance proxy (Riedl et al. 2013) and furthermore correlates significantly with observed performance outcomes of our

study (see results section). Our approach can be considered middle- range theorizing as we generally explain and test relationships between abstract variables but within the souring team domain guided by OBB models, which include both group and individual behavioral elements (Garver 2019). Please find the research model depicted in Figure 2 and the definitions summarized in Table 2.

The effect of goal misalignment on conflict in cross-functional sourcing teams Dissimilar goals lie at the heart of task conflict although conflict neither in its conceptualization nor in its measurement instrument encompasses misaligned goals explicitly (Jehn 1995, 1997). Webster and Wind (1972) noted that players in buying centers can have conflicting needs and criteria. Consistently, studies argue that members of sourcing teams frequently have different goals and values, which lead to individuals interpreting the same information in different ways (Sheth 1973). Based on this notion of different perception (Dearborn and Simon 1958), conflict is a usual outcome of the joint decision-making process since indi- vidual representatives make independent evaluations based on their functional background and incentive scheme. For instance, functional representatives may disagree upon the prioritization of suppliers based on technical, cost, logistical, or quality indicators, leading to a task-focused conflict. Thus, we expect that cross- functional members of goal misaligned and interdependent sour- cing teams will realize their different objectives and naturally begin a task-focused discussion in the sense of Jehn (1997) on which parameters of the supplier quotes should be prioritized to derive a final decision. Thus, we posit:

H1 : Goal misalignment positively affects task conflict in cross-functional sourcing teams.

Extant research has indicated that misaligned goals create sev- eral problems in sourcing teams (e.g., Moses and �Ahlstr€om 2008), yet SCM and organizational research remain unclear on whether misalignment alone can create political behavior among teams. We propose that politics may emerge under misaligned goals in a sourcing team based on a dual process: One driver may be an unjustified anticipation of obstacles to one’s own goal achievement, while the other may be politics in response to actual constraints that reduce the chances of achieving one’s individual goals. We elaborate on the reactive response to resource constraints in later hypotheses while focusing on the proactive anticipating approach in the next paragraph.

Since individual goals and functional preferences are central to buying centers’ processes (Sheth 1996), it is likely that these aspects are discussed first or at least become implicitly clear in early stages of the negotiation. Hence, team members feel pres- sured to apply political tactics based on the anticipation of self- serving or opportunistic intentions of their fellow sourcing team members solely due to competing or mutually exclusive goals. Functional managers expect politics since they are aware that others are incentivized to reach their functional targets as they are themselves. Therefore, they are more likely to apply political tactics proactively in order to avoid a competitive disadvantage and to hedge for possible negative effects of goal misalignment on their own target achievement. Thus, we posit:

Cross-Functional Sourcing Teams 9

Table 1: Key studies from the literature review across disciplines

Author(s) Year Key Finding(s) SCM Team Politics Conflict Journal

Stanczyk et al. 2015 Power imbalance and intuition create politics in cross-functional sourcing teams, which negatively affects rationality.

X X X JBL

Marshall et al. 2015 Individual goals of political actors shape the success or failure of outsourcing project teams.

X X X IJOPM

Moses and �Ahlstr€om 2008 “Functional misalignment” manifests in conflict and selfish (i.e., political) behavior among sourcing team members.

X X X X JPSM

Andre 1995 Solution-oriented conflict resolution in logistics teams outperforms nonconfrontational or control styles.

X X X JBL

Ehie 2010 Rational conflict in manufacturing teams has positive outcomes (e.g., firm performance), while emotional conflict has negative outcomes.

X X X IJPE

Oliva and Watson 2011 Open and constructive processes during supply chain planning can integrate diverse goals and manage conflicts.

X X X JOM

Kaufmann et al. 2012 More rational sourcing teams make better performing decisions regardless of market dynamism and stability.

X X JPSM

Riedl et al. 2013 Accountability and effective incentives among sourcing teams drive procedural rationality and final sourcing decision outcomes.

X X JOM

Thornton et al. 2016 Organization-level politics interact with top SCM executives’ political skill to improve the overall supply chain orientation of the firm.

X X JSCM

Sheth 1973 Buying centers’ choices are affected by conflicts that root from goal misalignment and that can be resolved by applying political tactics.

X X X JOMar.

Webster Jr and Wind 1972 Buying centers processes include “personal–political tactics” that enhance actors’ power and influence during buying decision making.

X X X JOMar.

Allen et al. 1979 Politics can be distinct tactics that are different in nature yet share the goal of selfish support of own goals irrespective of others’ interests.

X CMR

Ferris and Kacmar 1992 Tests the left side of the original perceptions of politics conceptual model including a negative effect of work group cohesion on perceived politics.

X JO Mgt.

Pfeffer and Salancik 1978 Politics are the result of conflict over the resources in organizations.

X X -

Jehn 1995 Task and relationship conflicts’ effect on outcomes of teams depends on the task type, task interdependence, and group norms.

X X ASQ

Gargiulo 1993 Managers use political influence tactics to obtain control over individuals that are sources of resource- constraining conflicts.

X X ASQ

Eisenhardt and Bourgeois 1988 Conflict leads to politics when few actors within the top management team hold superior power compared to others.

X X X AMJ

Bai et al. 2016 Perceived politics entail that individuals hold divergent goals, which lead to conflicts in management teams.

X X X JBE

Continued.

10 H. Franke and K. Foerstl

H2 : Goal misalignment positively affects political behavior in cross-functional sourcing teams.

Additional to the hedging and proactive politics, the alterna- tive effect may be due to justified evidence that requires man- agers to apply political tactics as soon as conflicts emerge. Consequently, team members react to tangible constraints with politics that seek to overcome obstacles to goal achievement

(Sheth 1973). For this path, however, we first need to establish a connection between the two conflict types under study, namely task and relationship conflict.

Studies in the field of team conflict suggest that task conflict, being rational discussions around the task at hand, may subse- quently escalate into relationship conflict, when the task-focused problems cannot be resolved (e.g., Peterson and Behfar 2003; De Wit et al. 2012). For instance, Camelo-Ordaz et al. (2015) find that management teams disagree about the task (i.e., strategic firm

Figure 2: Conceptual research model.

Table 2: Construct definitions

Name Construct Definitions Based on

Goal misalignment Differences in goals, interests, or priorities such as price, quality, security of supply, etc.

Stanczyk et al. (2015)

Task conflict Disagreements among group members about the content of the tasks beingPerformed

Jehn (1995)

Relationship conflict Interpersonal incompatibilities among group members, which typically includes tension, animosity, and annoyance

Jehn (1995)

Political behavior Intentional acts of influence to enhance or protect the self-interest of individuals or groups

Allen et al. (1979)

Procedural rationality The extent to which the decision-making process reflects a desire to make the best decision possible under the circumstances

Dean and Sharfman (1993a)

Team member satisfaction The general state of joy, happiness, and satisfaction during and after the teamwork

Jehn et al. (2010)

Table 1: (Continued)

Author(s) Year Key Finding(s) SCM Team Politics Conflict Journal

Maslyn and Fedor 1998 Perceived politics at the organizational and team level have different effects, which justifies a team perspective on politics.

X X JAP

Dean and Sharfman 1996 Procedural rationality increases teams’ decision- making effectiveness while political behavior reduces effectiveness.

X X AMJ

A wide range of publications on team-level conflict and organizational-level politics is available outside the scope of SCM. We include early influential publications and refer to Franke and Foerstl (2018) for a comprehensive report on the literature.

Cross-Functional Sourcing Teams 11

decisions) and relationship conflict serves as a mediating factor between task conflict and firm innovativeness. Also, OBB models support that conflicts among buying centers may shift away from task-focused topics, such as supplier requirements, to emotional and personal matters (Sheth 1973). In line with the early OBB model and given that sourcing team members still need to exchange information and coordinate regardless of task conflict, we argue that the difficult but ongoing integration processes may trigger personal annoyance and animosity that lead to explicit rela- tionship conflict. Particularly, the interdependence of knowledge and skills among cross-functional sourcing team members even requires them to integrate and exchange rather than avoiding the apparent task conflicts. As a result, the repeated experience of task conflicts without proper resolution likely leads to heated emotional fights that disregard facts and figures of the selection process but target personal problems. Hence, we hypothesize:

H3 : Task conflict positively affects relationship conflict in cross-functional sourcing teams.

Relationship conflict, however, is not naturally driven by the misalignment of goals per se but can depend on several contex- tual variables such as the team task, diverse cultural dimensions, or team familiarity (De Wit et al. 2012). Also, relationship con- flict can be path dependent as past relationship conflict can trig- ger more relationship conflict (Peterson and Behfar 2003). Yet, our study focuses on so-called “task-based” functional integration and sourcing teams being composed specifically for a discrete sourcing task (Miller and Dr€oge 1986; Foerstl et al. 2015). Mem- bers in temporary task-based sourcing teams usually have com- parably little work experience with one another due to their heterogeneous functional backgrounds. Hence, sourcing teams should not suffer from past relationship conflict.

Extant studies have suggested mediation of task conflict’s effect on team outcomes through relationship conflict (Camelo- Ordaz et al. 2015). This rationale is based on the logic of sequential escalation from slow or unproductive task conflict subsequently to dissent on the emotional level (H3). As pointed out in our reasoning leading to H1, goal misalignment between participating functions drives them to engage in task-focused conflicts about the sourcing decision at hand. As a result, we expect that task conflict is an intervening mechanism between the structural precondition of goal misalignment and the behav- ioral phenomenon of relationship conflict. Conversely, a direct link between goal misalignment and heated relationship conflict has no theoretical grounds in temporary sourcing teams. Drawing on the above argumentation and referencing back to OBB’s pro- cess models leading to H3, we posit the following mediation:

H4 : Task conflict mediates the link between goal misalignment and relationship conflict in cross-functional sourcing teams.

The effect of conflict on politics in cross-functional sourcing teams Recall that our study argues for a dual process between goal misalignment and politics in sourcing teams. On the one hand, managers anticipate competition based on goal misalignment and

apply political tactics proactively (H2), while on the other hand representatives may need to manage obstacles to their goal achievement reactively. We delineate the second idea in the fol- lowing based on organizational politics and OBB concepts.

Functional representatives need to exchange information and documents through internal integration processes in interdepen- dent sourcing teams (Moses and �Ahlstr€om 2008). In fact, they are reciprocally interdependent, meaning that one function’s out- put is necessary as other functions’ input and vice versa (Thomp- son 1967; Trent and Monczka 2003). Nevertheless, sourcing teams should ideally make the global best decision for the firm, while functional representatives seek to find local optima to serve possibly misaligned functional interests (Stanczyk et al. 2015). The resulting conflicts restrict individual access to information as functional managers become hesitant to share information that may oppose one’s own interests and strategic goals. Task and relationship conflicts triggered by goal misalignment (H1, H3, H4) hence become constraining problems that require managers to seek ways to work around constraints in order to still achieve their individual functional goals. These manifest conflicts repre- sent justified evidence for functional managers to apply political tactics reactively as means to reduce obstacles to their goal achievement.

We build on the notion that political tactics can ease resource access in relations suffering from conflict (Gargiulo 1993). Tac- tics that may help overcome conflicts are, for instance, ingratia- tion toward important owners of information, coalition formation to overrule constraints, or selective information sharing to signal cooperation but avoiding to disclose critical knowledge via coopetition (Stanczyk et al. 2015; Rajala and Tidstr€om 2017). Moreover, OBB process models of sourcing decisions indicate that resolution of conflicts by politicking and back-stabbing is common in industrial buying decisions (Sheth 1973). Similarly, important gatekeepers of purchasing processes will likely be sub- ject of “personal–political” tactics that aim at goal achievement via personal favors (Webster and Wind 1972). We expect that both task conflict and relationship conflict trigger politics in sour- cing teams since both types of conflict potentially obstruct indi- vidual access to important supplier information and can result in politics as reactive coping strategy. Task conflict obstructs exchange processes based on rational incompatibilities of task-fo- cused goals, while relationship conflict obstructs exchange pri- marily based on irrational personal incompatibilities. Thus, we posit:

H5 : Task conflict positively affects political behavior in cross-functional sourcing teams.

H6 : Relationship conflict positively affects political behavior in cross-functional sourcing teams.

Seminal studies on politics in organizations view politics as result of conflicts for resources (e.g., information, documents; Pfeffer and Salancik 1974, 1978), and conceptual models of strategic decision making and OBB see politics as outcome of conflict (Sheth 1973; Eisenhardt and Bourgeois 1988) consistent with our theorizing above. At the same time, we argued that con- flicts are driven by goal misalignment among the sourcing team

12 H. Franke and K. Foerstl

(H1, H3). Therefore, we expect that conflicts are intervening mechanisms between structural goal misalignment and emerging political behavior in cross-functional sourcing teams. On the one hand, task conflicts emerge based on goal misalignment (H1) and subsequently drive political behavior since managers’ rational discussions may lead to more transparency about the available suppliers and their characteristics; however, the remain- ing orthogonality of functional objectives (partly or entirely) trig- gers politics in the team (H5) (Stanczyk et al. 2015). On the other hand, relationship conflict that has developed based on the unsatisfactory task-focused discussions (H3, H4) subsequently triggers politics (H6). Thus, we expect that both types of conflict serve as mediators and additionally posit a sequential mediation hypothesis based on H4.

H7 : Task conflict mediates the link between goal misalignment and political behavior in cross-functional sourcing teams.

H8 : Task conflict and relationship conflict sequentially mediate the link between goal misalignment and political behavior in cross-functional sourcing teams.

The effect of politics on outcomes of cross-functional sourcing teams In a sourcing team, optimally all team members concentrate their energy and capacity at the multilateral integration processes, which are necessary to sort out interdependent task information on strategic decisions (Mell et al. 2014). Research on politics, however, states that when employees face a politically charged environment, their two dominant response strategies are to avoid negative psychological and emotional consequences by with- drawing (i.e., quitting the job) or to join the political game in order to win (Hsiung et al. 2012; Wiltshire et al. 2014). Political teams, other than broader organizations, do not tend to suffer from employee turnover (Maslyn and Fedor 1998), possibly due to their often temporary nature. Hence, team members are only left with engaging in the political game to win. As a result, poli- tics limit team members’ attention to the task by shifting it toward exerting political influence. Hence, managers focus more attention and energy on managing their conflicts via political tac- tics rather than scrutinizing the provided task information in order to make the best possible decision as a team (Eisenhardt and Bourgeois 1988; Dean and Sharfman 1993a, 1996). Hence, we expect that politics among sourcing teams reduce the proce- dural rationality of the decision process.

H9 : Political behavior negatively affects procedural rationality in cross-functional sourcing teams.

Several studies have supported that politics in organizations reduces satisfaction of employees with their job in general (Miller et al. 2008). However, studies have shown that allegedly straight- forward and well-researched links, such as political environments and withdrawal from the job, can be challenged when lifted into the team context (Maslyn and Fedor 1998) (see H9 development). Thus, we seek to trace the effect of politics among sourcing teams on our second outcome dimension, team member satisfaction.

Political environments reflect the notion that personal relations and social ties rather than individual effort and achievement determine personal rewards in organizational research (Ferris and Kacmar 1992). In essence, not the team member with the best contribution but the one that most effectively applies political tactics tends to achieve functional goals in politically charged sourcing teams. Altering the individual “effort–outcome func- tion” by introducing political exchange standards contradicts the ideology of fair treatment and rewards (so-called “exchange ide- ology”; Andrews et al. 2003). As increasing politics further reduce the effect of effort on personal outcomes, such as individ- ual rewards and appreciation by functional peers, team member satisfaction suffers. Accordingly, we formulate our final hypothe- sis.

H10 : Political behavior negatively affects team member satisfaction in cross-functional sourcing teams.

RESEARCH METHODOLOGY

We conduct a social experiment at the team level in order to effectively control extraneous influences and extract solid infer- ences about our dependent variables. We selected the vignette- based experiment method, which has become well accepted in behavioral operations (Bachrach and Bendoly 2011; Croson et al. 2013) and sourcing research (Ribbink and Grimm 2014; Rotten- burger and Kaufmann 2019). Subsequently, we describe our experimental design, its variables and measures, and our treat- ment of possible biases.

Experimental design and data collection

Our experimental design follows the methodological guidelines for vignette experiments in SCM (Bachrach and Bendoly 2011; Eckerd 2016) and a recent example of a complex team experi- ment (Mell et al. 2014). We chose to conduct an on-site experi- ment including real team interaction (event technique) on a sourcing task to foster realism (Koschate-Fischer and Schan- delmeier 2014). The vignettes were generated through iterative discussions with experienced field researchers and SCM profes- sionals following the guidelines by Rungtusanatham et al. (2011). The baseline situation describes a hypothetical manufac- turing firm facing a selection of an important supplier. In order to continue serving the market with motorcycles, the sourcing team needs to successfully select a new engine supplier. We ran- domly assigned participants into teams of three and likewise ran- domly assigned the teams to one of the experimental conditions (see next section). Please see the Appendix for example vignettes and an overview of the process.

All participants received a general briefing about the process, individual rewards, and the study’s rules before general instruc- tion emails were sent out. After completing a preparatory online survey on several control variables, team members received an individualized e-mail that framed each participant into an upcom- ing supplier selection meeting for a strategic sourcing item (mo- torcycle engine) and an individual functional background (purchasing, marketing and sales, or R&D) along with explicit

Cross-Functional Sourcing Teams 13

functional goals (e.g., supplier flexibility). All e-mails contained an individualized document with information items on four pos- sible suppliers and a spreadsheet that would assist participants in their premeeting analysis (see Appendix). After 30 minutes of individual analysis, participants performed a standard manipula- tion check reproducing their assigned goal indicators.

Next, the three individuals formed a cross-functional sourcing committee and were given up to 60 minutes to come up with a supplier selection as the outcome of their self-guided group dis- cussion in a meeting setting. During the discussion, participants had to retrieve relevant information from fellow team members to find their preferred supplier reflecting high functional interde- pendency (Thompson 1967; Moses and �Ahlstr€om 2008). Teams were seated at a table in closed meeting rooms. After a decision had been made, all participants individually completed an online survey on latent variables of our model including their final assessment of each supplier’s profitability according to their assigned goals (later used as performance validation). Finally, we thanked and de-briefed the participants and handed over their rewards. Please find an overview of the processes in the Appen- dix.

Experimental conditions and manipulation Participants were either instructed to maximize benefits and mini- mize costs in their choice according to a set of identical indica- tors (goal alignment) or a function-specific set of indicators (goal misalignment). The instruction of goals was explicitly coupled to the performance-based rewards, while participants were not aware of other functions’ goals initially. Literature is ambiguous whether experiment subjects should be incentivized. We incen- tivized our participant because previous performance-based pay- outs have shown several positive effects such as increased data quality (Hertwig and Ortmann 2001). We sampled professionals enrolled in part-time graduate programs and full-time students. Participants were incentivized with both fixed and performance- based course credit where possible (10%–15% of their final course grade) or monetary incentives where course credit was infeasible (5€ for attendance and up to 5€ performance-based). The rewards are small enough for participants to perceive them as small “thank you” and not compare them to their opportunity costs of participation (Koschate-Fischer and Schandelmeier 2014).

Sample characteristics We recruited full-time employed part-time students and full-time students while distributing our sampling across several subgroups to ensure validity of our findings (Knemeyer and Naylor 2011). We recruited 561 students grouped into 187 cross-functional teams of three. 31 teams (93 individual) had to be excluded due to failed manipulation checks resulting in an effective sample of 468 individuals grouped in 156 sourcing teams. Approximately half of our participants are part-time graduate students and exec- utive MBA students (222 in total) who are all fully employed as commercial or technical supply chain agents or in other depart- ments such as sales and marketing to mirror the cross-functional- ity of sourcing teams. Additionally, we recruited 246 full-time students. We find no difference between the mean engagement of professionals and students (F = 2.33, p = .129). Please find the sample descriptive statistics summarized in Table 3.

Variables and measurement

We vary the independent variable goal misalignment on two levels by giving out aligned or misaligned goals in the sourcing teams. We include a dichotomous independent variable to reflect the manipulation in our estimations (0 = aligned goals; 1 = misaligned goals).2 Dependent variables of this study are task conflict, relationship conflict, political behavior, procedural rationality, and team member satisfaction (see Table 2). We operationalized task conflict and relationship conflict with the widely used scale items in Jehn (1995) and used the instrument provided in Dean and Sharfman (1996) for political behavior. We used the term “committee” instead of “team” in our items to avoid significant bias toward cooperation detected in pretests. Both terms share a very similar meaning and fit our definition adapted from Mohsen and Eng (2016). Finally, we proxy team performance with procedural rationality drawing on Dean and

Table 3: Sample descriptive statistics

Fully employed students

Full-time students

Participants 222 246 Teams 74 82 Age (years)* 27.2 22.8 Engagement*,† 5.5 5.6 Experience (years)* 4.9 2.0 Familiarity team*,† 3.0 3.3 Familiarity teamwork*,†

5.7 5.6

Perceived realism*,† 4.5 4.6 Gender Female 94 103 Male 128 143 Incentive type‡

Cash 60 51 Credits 162 195 Industry (of most experience) Agriculture 0.9% 2.0% Construction 4.5% 0.8% Manufacturing 26.1% 15.9% Transportation 5.4% 6.5% Wholesale Trade 3.2% 2.0% Retail Trade 9.5% 8.1% Finance and Insurance

9.0% 12.6%

Services 14.4% 16.3% Public Admin. 1.8% 0.4% Other 25.2% 35.4%§

*Mean values. †Based on 7-point Likert scale. ‡Incentives never differed within teams. §Mostly students without relevant work experience.

2A latent variable based on a reversed “goal similarity” instru- ment in Jehn (1995) delivers robust results.

14 H. Franke and K. Foerstl

Sharfman (1993a) and additionally measure team member satis- faction using a scale instrument used in Jehn et al. (2010). We chose rationality and satisfaction as team outcomes as suggested by extant studies (Jehn et al. 2010; Riedl et al. 2013).

Regardless of their assigned group, all participants read the same baseline scenario that provided information about the com- pany, industry, firm size, sourcing item, decision importance, etc. Thus, we are controlling for factors that are commonly known to influence strategic decision-making processes (Dean and Sharf- man 1993b). We also captured respondents’ age, gender, work experience, familiarity with teamwork, perceived realism of the setting, engagement (Rich et al. 2010), and familiarity among team members (Kohli 1989). We slightly adapted engagement

items to the focus on the “task” instead of on the “job” (Rich et al. 2010) as presented in Table 4. All control variables are presented in the correlation Table 5.

Validity assessment and measurement model Previously validated scales, a thorough literature review, and a pretest were used to ensure content validity (Anderson and Gerb- ing 1988; Forza 2002). In the first pretest with five teams com- posed of postdoctoral researchers and experienced PhD students, we focused on the comprehensibility of the general task, the vignettes, and the accompanying survey. After refinements of the material’s wording and order, we assessed the effectiveness of the intended manipulation in the second round with 21

Table 4: Measurement model properties

Construct (GLB; CR; AVE)Original Item Item Code Item loadings

Engagement (GLB .95; CR .94; AVE .73) (Rich et al. 2010) I worked with intensity on the task. Eng1 .888 I tried my hardest to perform well on the task. Eng2 .830 I exerted my full effort to the task. Eng3 .884 I exerted a lot of energy on the task. Eng4 .824 I strived as hard as I can to complete the task. Eng5 .857 I devoted a lot of energy to the task. Eng6 .852 Goal misalignment (condition marker; 0 aligned goals, 1 misaligned goals) Political behavior (GLB .70; CR .80; AVE .57) (Dean and Sharfman 1996) Committee members were primarily concerned with their own goals rather than with the goals of the organization.

Pb1 .812

Committee members were open with each other about their interests and preferences in the decision (reversed).

Pb2 .747

The decision was affected by the use of power and influence among committee members. Pb3 .698 Procedural rationality (GLB .80; CR .88; AVE .71) (Dean and Sharfman 1993a) How extensively did the committee look for information in making this decision? ProcRat1 .820 In general how effective was the committee at focusing its attention on crucial information and ignoring irrelevant information?

ProcRat2 .828

How extensively did the committee analyze relevant information before making a decision? ProcRat3 .874 Relationship conflict (GLB .95, CR .96; AVE .87) (Jehn 1995) How much emotional conflict is there among members in your committee? Rc1 .940 How much are personality conflicts evident in your committee? Rc2 .934 How much interpersonal tension is there among members in your committee? Rc3 .940 How much friction is there among members in your committee? Rc4 .908 Task conflict (GLB .94; CR .96; AVE .85) (Jehn 1995) To what extent are there differences of opinion in your committee? Tc1 .878 How frequently are there conflicts about ideas in your committee? Tc2 .946 How much conflict about the work you do is there in your committee? Tc3 .939 How often do people in your committee disagree about opinions regarding the work? Tc4 .929 Team familiarity (GLB .97; CR .97; AVE .87) (Kohli 1989) The committee members knew each other well. Fam1 .960 The committee members could build upon past experience working together. Fam2 .926 The committee members were familiar with each other's way of working. Fam3 .902 The committee members had known each other for a long time. Fam4 .947 Team member satisfaction (GLB .95; CR .96; AVE .90) (Jehn et al. 2010) I was very satisfied working with this committee. Satis1 .939 I was happy working with this group. Satis2 .956 How much did you enjoy working on this task with your committee members? Satis3 .945

CFA model fit: v²=639.38; v²/df = 1.696; CFI = .934; RMSEA = .067.

Cross-Functional Sourcing Teams 15

T ab

le 5:

C on st ru ct

co rr el at io n m at ri x

1 2

3 4

5 6

7 8

9 10

11 12

13 14

15

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n. a.

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-. 20 1*

.8 56

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.8 01 **

-. 12 5

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-. 00 1

.0 06

.9 34

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.1 83 *

-. 08 1

.1 87 *

.2 68 **

-. 10 2

n. a.

7 In ce nt iv e

.2 89 **

-. 27 3* *

.2 37 **

.2 15 **

-. 13 2

-. 05 5

n. a.

8 R ea li sm

.0 25

.2 10 **

.0 33

-. 03 0

-. 19 8*

.0 23

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n. a.

9 S tu de nt

-. 63 5* *

.1 10

-. 46 4* *

.1 00

-. 04 0

.0 08

-. 07 4

.0 58

n. a.

10 G oa l m is al ig n.

.0 03

-. 39 1* *

-. 13 0

-. 00 6

.0 12

.0 40

.1 59 *

-. 11 8

-. 03 9

n. a.

11 P ol . be ha vi or

.0 36

-. 17 8*

.0 17

.1 24

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.7 54

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-. 19 9*

.5 51 **

-. 16 9*

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.0 19

-. 19 9*

.1 42

.1 35

-. 36 5* *

-. 46 6* *

.8 41

13 R el . co nfl

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.1 77 *

-. 24 2* *

.1 61 *

.0 94

-. 07 0

.1 00

.2 22 **

.0 01

-. 19 5*

.4 23 **

.6 58 **

-. 43 2* *

.9 31

14 T as k co nfl

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.1 10

-. 24 0* *

.0 79

-. 01 8

-. 03 3

-. 07 5

.2 22 **

.0 88

-. 13 7

.5 38 **

.6 79 **

-. 43 1* *

.7 99 **

.9 23

15 T ea m

m . sa ti sf .

-. 16 7*

.5 38 **

-. 13 7

.0 45

.0 84

-. 02 4

-. 18 0*

.1 32

.0 77

-. 46 2* *

-. 53 5* *

.5 96 **

-. 59 9* *

-. 54 6* *

.9 46

S am

pl e m ea n

24 .9 10

5. 50 5

3. 33 3

3. 13 4

5. 62 7

n. a.

n. a.

4. 57 3

n. a.

n. a.

2. 38 4

5. 51 5

1. 97 7

2. 98 1

5. 92 0

S td . de vi at io n

3. 43 6

.8 79

2. 87 8

1. 47 1

.6 74

n. a.

n. a.

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n. a.

n. a.

1. 22 8

.8 25

1. 04 0

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*p < 0. 05

; ** p < .0 1;

4: F am

il ia ri ty

w it h te am

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F am

il ia ri ty

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ge ne ra l; di ag on

al sh ow

s sq ua

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of A V E w he re

ap pl ic ab le .

16 H. Franke and K. Foerstl

participants from part-time graduate classes and SCM employees of a major food retailer.

Confirmatory factor analysis (CFA) in AMOS was used to assess convergent validity. The results indicated to narrow down items for political behavior and procedural rationality. We only accepted items above the recommended cutoff value (Nunnally 2010). The CFA presented in Table 4 provides solid model fit indices (v² = 743.78; v²/df = 1.71; CFI = .92 7; RMSEA = .067). Scale reliability was assessed based on the Greatest Lower Bound (GLB) of construct reliability and con- generic reliability (CR) for each scale. Cronbach’s alpha is not applicable in our case since our instruments show unequal fac- tor loadings (Ten Berge and So�can 2004). We compute the GLB using the R package referenced in Peters (2014). Further- more, the comparison of the square root of the AVE for each construct and the corresponding correlation coefficients (see Table 5) supported discriminant validity (Fornell and Larcker 1981). The psychometric properties of our measurement model are presented in Tables 4 and 5. We aggregated several of our individual-response variables to the team level to account for their team-level conceptualization following advice on SCM multilevel studies (Carter et al. 2015). To verify that aggrega- tion was possible, we assessed each teams’ inter-rater agree- ment Rwg(J) for each latent construct based on the recommendations in Boyer and Verma (2000) and LeBreton and Senter (2008). Even the minimum detected within-team agreement (.67) exceeds the recommended .5 cutoff value (e.g., Chun and Choi 2014).

Manipulation checks and bias treatment

Several steps were taken to reduce concerns on manipulation failure, common method bias, and measurement model invari- ance. To check the effectiveness of the manipulation, we include a qualitative manipulation check that entails a simple reproduc- tion of the assigned goal indicators. 31 teams were excluded from the data since the responses to the manipulation check were incorrect. Furthermore, common method bias (CMB) was assessed by applying several CFA model comparisons as described in Williams et al. (2010). First, we find that modeling marker variable loadings onto the substantive (theory-based) indi- cators does not improve model fit significantly, indicating no CMB (“Baseline” vs. “Method-C” Model) (Dv² = .03; Ddf = 1; v²critical = 3.84; no difference). In addition, we find that the non- significant CMB is uniform in our model (tau-equivalence) and that the substantive factor correlations remain unaffected by CMB. Therefore, we conclude that CMB did not significantly impact our structural model estimations. We also tested for mea- surement equivalence between the subgroups in our data, stu- dents versus young professionals, and cash versus credit incentive using the procedure recommended in Knoppen et al. (2015). We find that all indicators loaded significantly on their respective constructs in all subgroups (configural equivalence) and that constraining the item loadings across groups (i.e., enforcing equivalence) does not significantly reduce the model fit for students versus young professionals (Dv² = 26.6; Ddf = 22; v²critical = 33.9; no difference) and cash versus credit incentive (Dv² = 22.9; Ddf = 22; v²critical = 33.9; no difference).

RESULTS

We tested our hypotheses in a path analytical structural equa- tion model. Consistent with the recommendations in Preacher and Hayes (2008) and Malhotra et al. (2014), we use bootstrap- ping to test our mediation hypotheses. Please see Figure 3 and Table 6 for a summary of the estimated structural model and the hypotheses tests, which are subsequently summarized per hypothesis. Our results show that goal misalignment creates task conflict among cross-functional sourcing teams (H1; b = .538, p < .01; see Table 6) and that misaligned goals also drive politi- cal behavior (H2; b = .188, p < .01). Thus, we accept H1 and H2. Furthermore, the model substantiates the direct link between task and relationship conflict (H3; b = .817, p < .01) for the sourcing context. Moreover, we find support for H4 stating that task conflict mediates the link between goal misalignment and relationship conflict since the specific indirect effect is significant (b = .440, p < .01; Table 6 Mediation).

We find support for H5 and H6 as both task conflict (b = .319, p < .01) and relationship conflict (b = .323, p < .01) show the hypothesized positive effect on political behavior. Additionally, the specific indirect effects of goal misalignment through task conflict (b = .172, p < .01) and through both types of conflict sequentially (b = .142, p < .01) were found significant, supporting H7 and H8 (see Table 6 Mediation). Given that goal misalignment’s direct effect on political behavior prevails (support for H2), we conclude a partial mediation sequence for the goals–conflict–politics medi- ation. Finally, we tested whether political behavior affects pro- cedural rationality and satisfaction and found support for its detrimental effect on rationality (b = �.414, p < .01) and on team members satisfaction (b = �.247, p < .01). Hence, we accept H9 and H10.

Post hoc robustness checks

As mentioned in the methods section, we captured the individual assessments of each supplier’s profitability after the sourcing team discussion. Ideally, all team members would have reached their individual correct analytical solution to the sourcing prob- lem according to the (mis)aligned goals. We average (adding is equivalent) individual scores to form a team-level error measure. The error metric significantly correlates with our latent measures for political behavior (.239, p < .01) and procedural rationality (�.210, p < .01), which provides predictive validation for the validity of procedural rationality (cf. Figure 1) in line with earlier findings of Riedl et al. (2013).

In addition, we performed multiple v² difference tests to verify the structure of the model and avoid model overspecification. We ran a model excluding links between task conflict, relation- ship conflict, and political behavior (Ddf = 3; Dv² = 200.27; Dv²critical = 7.82) and two other models that each omit a single link between the two conflict constructs and political behavior (Ddf = 1; Dv² = 5.44 and 6.89, respectively; Dv²critical = 3.84). According to the three significant v² tests, the larger (fewer df) hypothesized model (see Figure 2) always fit the data better. This result provides further support for conflict driving politics in a stepwise mediated process.

Cross-Functional Sourcing Teams 17

Finally, we repositioned task conflict, relationship conflict, and political behavior in two different ways in the SEM to observe changes in effect sizes and R². Appendix E.1 shows an alternative model with political behavior as intervening mechanism between task conflict and relationship conflict. This model is based on the possible notion that politics can trigger emotional responses (i.e., relationship conflict) to fend off political influencing. While effect sizes largely match our expectations, R² drops significantly for political behavior (DR² = �.340) and the model does not mirror the behavioral process that finally leads to teams’ performance in practice (insignificant path and indirect effects toward procedural rationality). Similarly, E.2, assuming a “politics-conflict link,” shows that R² for political behavior again drops (DR² = �.277) and indirect effects on procedural rationality are insignificant. These results substantiate both conflict types as predictors of political behavior, as theorized in our model (Figure 2), that explain the team processes and its outcomes more effectively.

Post hoc examination of the “Conflict–Politics Link” using NCA

To further dive into the relation of conflict and politics and derive more insightful implications, we complement the SEM with Necessary Condition Analysis (NCA) (Dul 2016). NCA looks for necessary but not sufficient drivers of an outcome vari- able instead of the often assumed linear and continuous relation- ship in regular estimation techniques such as SEM or regressions. In short, NCA informs us on how the variables of our model may constrain each other and assists in deriving more interesting and insightful implications on the “conflict–politics link” (Cachon 2012). In other words, “a necessary cause is a constraint, a barrier, an obstacle, a bottleneck that must be man- aged to allow a desired outcome to exist” (Dul 2016, p. 11). Please find detailed results of our analyses in Appendix F and note that we reversed factor scores of our two outcome variables for NCA since it only operates in the upper-right quadrant of the Cartesian coordinate system in the current version 3.0.1. Appendix G provides a concise overview of NCA.

We find that multiple necessary conditions exist among the variables of our model, but they differ significantly in size. The strongest constraint is posed by task conflict on relationship con- flict (d = .425, p < .01), which also shows to constrain political behavior with d = .201 (p < .01), an effect sized considered mod- erately strong for NCA (Dul 2016). These patterns can be more easily observed in the bottleneck table in Appendix F. We find that relatively high levels of political behavior and procedural irra- tionality are possible without contributions of the structural goal misalignment or any type of behavioral process we proposed (con- flict and politics). In fact, 60% of political behavior’s and 50% of (ir)rationality’s magnitude are unexplained by its antecedents in our model. The bottleneck tables also allow inferring when and how quickly our antecedent variables allow for increases in politi- cal behavior and procedural rationality. For instance, we can observe that 73.2% of task conflict’s range is necessary to allow for the highest political behavior, but only 34.5% of relationship conflict will suffice to reach that high level. Thus, when relation- ship conflict occurs, even low conflict levels allow for maximum political behavior. For procedural (ir)rationality, we find a simi- larly steep incline based on relationship conflict albeit the variable contributes late to irrationality (starting at >70% of Y). The NCA revealed that relatively low levels of relationship conflict can trig- ger strong politics and irrationality, whereas task conflict seems strongly coupled with relationship conflict. These findings are in line with our robustness checks concerning the model structure.

DISCUSSION

Theoretical implications

Our work makes several important contributions to the literature. First, our study contributes to OBB literature by providing empirical substance for its conceptual framing of sourcing/buying as a constrained process among irrational and misaligned profes- sional actors besides the proven benefits of integration (Swink and Schoenherr 2015). According to the theory, the heterogeneity

Goal misalignment

Task conflict

Political behavior

Procedural rationality

Team member

satisfaction Relationship

conflict

.538**

H5/H7

H6/H8

H9

-.247**

.188** H2

R² = .290

R² = .657

R² = .525

R² = .524

R² = .598 H10

-.414** .319**

H3/H4/H8 .817**

H1/H4/H7/H8

.323**

* p < .05; ** p < .01

Estimated but nonhypothesized link. Please see Table 6 for all estimated effect sizes.

Figure 3: Summary of results.

18 H. Franke and K. Foerstl

of skill sets and function-inherent goals lead to emotional and social barriers to rational choice. This study is among the first to study these barriers in a large-N empirical setting. Particularly, we begin filling the empirical void that persisted in the OBB lit- erature concerning “politicking” and conflicts (Sheth 1973) or “interpersonal influence” (Webster and Wind 1972). While the original frameworks have recognized the challenges inherent to conflict and politics, studies regret that they have hardly been conceptually disentangled and empirically observed (Bai et al. 2016; Thornton et al. 2016). With the additions in our study, we not only initiate closing this gap but follow the general call for more people-focused logistics and SCM research (Wieland et al. 2016).

Second, we speak to work that has recognized the tension between necessary cooperation among functional areas and the arising competition (“coopetition”) that roots from functional diversity in buying processes (Rajala and Tidstr€om 2017). Our study emphasizes political behavior as one possible manifesta- tion or outcome of coopetition in OBB where actors are sup- posed to work together in a joint task (supplier selection) yet also choose to exert undisclosed influence to serve unidi- mensional functional needs. Thus, we provide further substance to studies emphasizing that complex decisions cannot be decom- posed into clearly defined and structured steps but that the pro- cess can be rugged, intuitive, and spontaneous (Makkonen et al. 2012). Also, we add to the ongoing discussion around politics

Table 6: Estimation results

H bpath p 95% CI btotal p 95% CI

Substantive relations (all) Goal misalignment (GM) ? Task conflict H1 .538 .000** [.425; .648] .538 .000** [.425; .648] GM ? Relationship conflict – �.011 .855 [�.130; .113] .428 .000** [.297; .546] GM ? Political behavior H2 .188 .003** [.055; .306] .498 .000** [.387; .602] GM ? Procedural rationality – .039 .606 [�.090; .194] �.192 .006** [�.320; �.049] GM ? Team member satisfaction – �.061 .345 [�.183; .069] �.322 .000** [�.444; �.182] Task conflict (TC) ? Relationship conflict H3 .817 .000** [.717; .907] .817 .000** [.717; .907] TC ? Political behavior H5 .319 .001** [.129; .502] .583 .000** [.472; .693] TC ? Procedural rationality – .010 .924 [�.216; .209] �.289 .000** [�.447; �.134] TC ? Team member satisf. – .040 .751 [�.220; .294] �.407 .000** [�.552; �.255] Rel. conflict (RC) ? Political behavior H6 .323 .000** [.148; .500] .323 .000** [.148; .500] RC ? Procedural rationality – �.071 .485 [�.272; .130] �.205 .036* [�.406; �.015] RC ? Team member satisf. – �.370 .004** [�.604; �.090] �.450 .000** [�.673; �.182] Political behavior ? Proc. rationality H9 �.414 .000** [�.581; �.249] �.414 .000** [�.581; �.249] Political behavior ? Team member satisf. H10 �.247 .003** [�.413; �.096] �.247 .003 [�.413; �.096] Mediation (hypothesized only) GM ? TC ? RC H4 .440 .000** [.331; .548] – – – GM ? TC ? Political behavior H7 .172 .002* [.068; .288] – – – GM ? TC ? RC ? Political behavior H8 .142 .000** [.061; .235] – – – Control variables Age ? Proc. rationality – �.113 .409 [�.376; .160] �.113 .409 [�.376; .160] Age ? Team member satisfaction – �.114 .296 [�.327; .100] �.114 .296 [�.327; .100] Engagement ? Procedural rationality – .421 .000** [.284; .558] .421 .000** [.284; .558] Engagement ? Team member satisf. – .383 .000** [.276; .503] .383 .000** [.276; .503] Experience ? Procedural rationality – �.023 .852 [�.263; .229] �.023 .852 [�.263; .229] Experience ? Team member satisfaction – �.041 .638 [�.220; .133] �.041 .638 [�.220; .133] Fam. team ? Procedural rationality – .173 .018 [.033; .314] .173 .018 [.033; .314] Fam. team ? Team member satisfaction – .109 .075 [�.014; .224] .109 .075 [�.014; .224] Fam. work ? Procedural rationality – .020 .725 [�.088; .136] .02 .725 [�.088; .136] Fam. work ? Team member satisfaction – �.008 .901 [�.125; .119] �.008 .901 [�.125; .119] Gender ? Procedural rationality – .054 .395 [�.075; .177] .054 .395 [�.075; .177] Gender ? Team member satisfaction – .059 .354 [�.057; .192] .059 .354 [�.057; .192] Incentive ? Procedural rationality – �.037 .584 [�.167; .089] �.037 .584 [�.167; .089] Incentive ? Team member satisfaction – .040 .562 [�.092; .179] .040 .562 [�.092; .179] Realism ? Procedural rationality – .062 .313 [�.050; .189] .062 .313 [�.050; .189] Realism ? Team member satisfaction – .041 .508 [�.090; .154] .041 .508 [�.090; .154] Student dummy ? Procedural rationality – �.041 .643 [�.215; .135] �.041 .643 [�.215; .135] Student dummy ? Team member satisf. – �.144 .080 [�.297; .030] �.144 .080 [�.297; .030]

p-values and confidence intervals (CI) calculated using bootstrapping; *p < 0.05; **p < .01.

Cross-Functional Sourcing Teams 19

and influencing in sourcing processes (Stanczyk et al. 2015; Brewer et al. 2019).

Third, we add substance to the mediation commencing with conflict via politicking on organizational buying team outcomes based on alternative path model estimations (see Appendix E) and NCAs (see Appendix F and G). Consequently, this finding speaks to work that has emphasized emotional processes shaping buying center decisions and behavior at several organizational levels (Kemp et al. 2018). We add to these recent developments as we disentangle the relationship of rational task conflict (i.e., topic-focused discussions) with their emotional escalation (i.e., relationship conflict) and the resulting political response strate- gies. Hence, these findings suggest that problems in cross-func- tional sourcing teams not only co-occur but may affect each other via mediation and also constrain one another as necessary conditions, thereby empirically extending conceptual and anecdo- tal insights of former studies (Trent and Monczka 1994; Moses and �Ahlstr€om 2008). Specifically, we add to the closing of the conceptual and empirical void between conflict and politics (Bai et al. 2016) but also generally to the research that emphasizes emotional or irrational processes in OBB and in the wider OSCM domain (Urda and Loch 2013; Stanczyk et al. 2015; Kemp et al. 2018). Notably, our findings on the “goal misalign- ment–political behavior link” can even be extended toward our final outcomes. The extended specific indirect effects account for 30.7% and 10.5% of the total effects on procedural rationality and team member satisfaction, respectively, which outlines the significance of the observed mediation chain. More subtle drivers of team-level politics, outside the scope of this study, are yet to be uncovered as also indicated by our NCA (Appendix F).

Fourth, we find that task conflict does not show its rationaliz- ing effect but serves as necessary condition to the detrimental relationship conflict. This finding partially contradicts conclu- sions that task conflict can have positive effects as it brings all information to the table and helps teams to better understand complex tasks (De Wit et al. 2012). The absence of the catalyz- ing role of task conflict may be due to participants anyhow high attention to discussing the data provided in the experimental design. Thus, future studies could substantiate the merits or dan- gers of task conflict in sourcing teams. Additionally, our negative implications of politics’ effect on team outcomes are consistent with the extant SCM team politics evidence (Stanczyk et al. 2015), yet future SCM research should also consider the recently emerging positive perspective of politics (Hochwarter 2012; Hsi- ung et al. 2012; Kapoutsis and Thanos 2016), which we did not detect in this study.>

Managerial implications

Our study is relevant to executives supervising cross-functional sourcing teams and managers involved in cross-functional sour- cing teamwork. This study shows that sourcing teams (or buying centers) are not always rational actors in the best interest of the firm but that individuals may bring in misaligned functional incentive structures and goals based on their diverse functional background. We highlight that the resulting social and emotional problems between functional areas not only co-occur but also affect each other in a mediation sequence. Goal misalignment drives task-focused conflicts, which in turn constitute a necessary

condition for further emotionally laden conflicts to emerge. We find that both types of conflict are positively associated with political response strategies to defend uni-lateral interests and goals. According to our study, managers should avert personal interpretations of task conflict and the resulting relational (emo- tional) conflict. This is particularly important as task conflicts need to be very intensive (73% necessary of a 1%–100% scale) to allow strong politics (100%—the maximum) to emerge and negatively affect rationality and decision outcomes, while even low levels of relationship conflict (35% on a 1–100% scale) result in strong team politics, which significantly hamper rationality in deriving decision outcomes. Moreover, managers should not counteract low to medium levels of task-focused con- flicts, also because they can be functional (De Wit et al. 2012), but closely monitor and manage relationship conflict.

While we argue for politics to be a coping strategy for man- agers seeking to overcome conflict and still achieve individual functional goals, we caution decision makers that political influ- encing behavior ultimately leads to reduced scrutiny in sourcing teams and ultimately causes lower performance. Our analysis reveals that goal misalignment, conflict, and politics all drive irrationality, yet their influence mostly explains medium to high levels of irrationality according to the post hoc analyses. Roughly 50% of irrational influence at low or medium levels is likely due to hitherto unexplored behavioral phenomena in sour- cing teams. Thus, we advise sourcing team managers not to solely look for conflicts or political fights to resolve, but also for extraneous and subtle factors (i.e., past experience, personal needs, or prejudices) whose influence future research still has to trace. Generally, we can advise practice to reduce functional goal misalignment as far as possible as it causes proactive politics (hedging against other’s manipulating) and to closely monitor escalation processes from rational discussions to heated emo- tional debates.

LIMITATIONS AND FUTURE RESEARCH

The limitations of our research also open the field to further research. First, we do not consider diversity in teams beyond functional backgrounds, expertise, and goals. Recently, behav- ioral SCM research has begun observing team diversity and included emotions as varying coping strategy among team mem- bers (Kaufmann and Wagner 2017). Consistently, reviews of OBB literature state that team diversity, also of ethnic or cultural origin, remains largely unexplored in the literature (Sheth 1996). Second, we did not capture influences of personality traits on our results, but opted for a random assignment of subjects. Building on personal differences in sourcing teams, personality may play a larger role than extant research has assumed. Although studies have supported the impact of personality and emotions on SCM in general (Urda and Loch 2013), tangible influences of consci- entiousness or political skill (i.e., the ability to exert political influence) in sourcing teams remain unexplored also in our study. Third, despite assessing the team’s divergence from the ideal rational choice (cost–utility-based solution), our study and large portions of SCM research are unable to assess long-term perfor- mance of a recently selected supplier. In this study, we cannot predict the selected supplier’s performance beyond the stylized

20 H. Franke and K. Foerstl

experimental context. Hence, future studies should pursue a lon- gitudinal design possibly also including qualitative observations of sourcing teams in a real-world context. Fourth, our methods entail pooling respondents from diverse origin such as students and young professionals as well as across different incentive schemes. Although our equivalence testing was satisfactory, we advise future research to avoid using few heterogeneous data sources where possible. Fifth, our study benefits from the experi- mental prediction power, yet only for goal misalignment (manip- ulated). We acknowledge that the “conflict–politics link” is possibly not one-directional but bi-directional or even circular as functional representatives can build expectations and gather evi- dence on their colleagues behavior in recurring team settings. In line with ad hoc sourcing decisions, our design focuses on tem- porary task-based integration (Miller and Dr€oge 1986; Foerstl et al. 2015). Such situations are typical for large companies with constantly changing sourcing team configurations, whereas in small and medium enterprises specific teams may interact more regularly. For the latter, the shadows of the past and the future may have a stronger impact on team and functional relationships. In line with the social exchange perspective, we advise future research to consider that previous social interaction can impact future encounters and assess medium- to long-term political behavior in under such varying circumstances. Finally, our experiment placed participants in a particular functional context, which may or may not have reflected their own work in practice. Despite the satisfactory engagement and realism ratings, we acknowledge that additional research such as field experiments will be necessary to test our findings also in different contexts, such as permanently embedded teams. While none of the above limitations fundamentally question the validity of this study, we hope to stimulate future research on political sourcing team pro- cesses with our study.

CONCLUSION

In responding to the initial research questions, our results suggest a sequentially mediated effect of goal misalignment via rational task and emotional relationship conflict on team politics. More- over, we show the potentially detrimental implications of misalignment, conflict, and politics in cross-functional sourcing teams. Hence, our main contribution is evidence for a granular extension of the conflict–outcome path (Peterson and Behfar 2003). Goal misalignment is identified as a root cause for con- flict and politics. We find that team conflict leads to political behavior that in turn reduces team member satisfaction and pro- cedural rationality, which is directly related to the overall perfor- mance of the sourcing decision (Riedl et al. 2013). Hence, this study shows that problems in cross-functional sourcing teams emerge in a stepwise escalation process, in accordance with the procedural perspective taken in OBB theory Complementary Necessary Condition Analysis (NCA) allows us to conclude that our predictors tend to explain high levels of politics and irra- tionality, which points to future research potentials directed at more subtle drivers of manipulation in sourcing teams. Based on our inquiry, future research on sourcing team conflict, team poli- tics, and their mutual relation is necessary to advise practice on suitable mitigation strategies that avoid negative outcomes while

reaping the benefits of cross-functional integration (Turkulainen and Ketokivi 2012).

ACKNOWLEDGMENTS

The authors would like to thank the handling editor Andreas Wieland as well as the associate editor and four anonymous reviewers for their continued support and feedback, which con- tributed significantly to the improvement of our manuscript. We would further like to express our sincere gratitude for the oppor- tunity to present and discuss our research at the JBL European Research Seminar 2018 in Rotterdam, which also proved invalu- able for the development of this research. An even earlier version of the manuscript was presented at the IPSERA Conference in Athens in March 2018.

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APPENDIX A Vignette material example: Assignment email (misaligned goals; Marketing & Sales)

Dear employee, As you know, our engine supplier went out of business. A

cross-functional procurement committee will soon be composed and I am sending you as the purchasing representative. I want you to make the best cut for the purchasing department and watch out for our strategic goals customer satisfaction and image. I can only pay out your bonus in case the optimal sup- plier with regard to these goals is chosen.

You will find some supplier information in an appended docu- ment. Analyze it thoroughly and make use of the spreadsheet I prepared for you. The goals are of equal importance to me, and they are not causally related across categories. Our research may be incomplete. Try to retrieve useful information during the negotiation to make the most profitable choice for purchasing.

Again, I need you to make sure that the costs are minimal and benefits are maximal regarding our goals customer satisfaction and image. If the committee’s choice reflects these goals in the end, I will pay out your full bonus.

Good luck, Your Supervisor Senior Director of Marketing & Sales Bronson Inc. Motorcycle Works

*** Please analyze your case material individually. Come up with your preferred supplier and a strategy to con-

vince the committee *** *** Complete Part 2 of the questionnaire as soon as you are

done with your analysis. Please stop at Part 3 *** Survey link | access code

APPENDIX B Vignette material example: Supplier information (same for all conditions; Marketing & Sales)

Akron

Akron’s sales channels are very flexible. They are able to surpass Bronson’s parts inventory and supply a spare engine to a Bron- son garage in less than 24 hours. This flexible solution could save Bronson 1,100,000€.

Akron has not yet discovered the potentials of comarketing bikes with the OEM. Hence, no Akron agents will be deployed for pop-up dealerships. To compensate the not very flexible Akron marketing, Bronson will need to invest 225,000€.

Laboratory tests of Akron engines’ quality have resulted in very good resistance to vibration and shocks. This could be a unique selling point to market off-road capable bikes leading to a 1,700,000€ benefit.

Competitors that have relied on Akron engines recently had to call back motorcycles. In case Bronson sources from Akron, CRM agents will be busy with this quality issue resulting in costs of 1,000,000€.

Akron has explicitly addressed barriers of career advancement and inequity for women in their workforce. The Ministry of Labor has praised Akron and their program, which will also affect Bronson’s image in case of a deal. Bronson can expect a 2,300,000€ benefit.

IACT

IACT sales officers have stated that they are absolutely unable to supply technical adaptions quickly. Bronson, however, has already marketed variations to its customers. Sending in a team to quickly develop supplier flexibility will cost 200,000€.

IACT is used to doing on-site marketing at fairs, shows, and other events. They could flexibly attend Bronson pop-up dealer- ships to make the events special for customers. This option could yield a benefit of 1,900,000€.

The logistical perspective on flexible call-offs with IACT has missed to consider that increased warehousing will also require Bronson to hire permanent workers instead of external services. This reduces Bronson’s flexibility and leads to an increase in the originally estimated costs by factor 10/9.

Competitors have experienced a rattling noise in some IACT engines. This noise has been attributed to a quality problem. Bronson should expect a 1,325,000€ campaign investment to cover up the recent critical press when sourcing from IACT.

Although IACT engines are not necessarily racing quality per se, IACT has signed two famous GP3 pilots to praise their qual- ity at races and fairs. Bronson can use these deals for its own good and collect a benefit of 2,100,000€.

IACT has been ranked in the lower third in last years “Best Place to Work” survey. Experts estimate a weakening of image manifesting in 250,000€ costs for Bronson in case of a deal.

Eagle Motors

You met a technical sales representative from Eagle Motors at the last marketing congress. He told you that Eagle Motors is struggling to adapt an existing quality testing unit to their engines. He stated that Bronson will have to develop a whole new unit, resulting in twice the estimated costs compared to an adaption of the old unit.

Eagle Motors engines perform well on racetracks, yet their quality is not suitable to ride heavily off-road. In case Bronson buys Eagle Motors engines, marketing would have to write off contracted sponsoring of off-road events costing 250,000€ in total.

Engines by Eagle Motors are of high quality and thus evenly compatible to consumer and racing bikes. This great efficiency

Cross-Functional Sourcing Teams 25

during marketing Bronson’s bikes in race events would yield a 1,625,000€ benefit.

Eagle Motors is unable to supply adaptions with low lead time and state that they are unwilling to change their processes to match Bronson’s marketing. Withdrawing the marketing cam- paign due to this lack of flexibility would create costs of 1,525,000€.

Latest customer reviews by BikeMagazine showed that bikes with Eagle Motors engines were consistently rated low in cus- tomer satisfaction. Hence, Bronson should calculate with costs of 800,000€.

Forza Macchina

The gazette Race&Ride recently reported that customer satisfaction with Forza Macchina engines’ acceleration is among the best in the industry. Marketing this outcome will create a 500,000€ benefit for Bronson in case Forza Macchina engines are procured.

Forza Macchina engines have always done well in GP races, however need some investment to adapt racing quality to con- sumer demands. The benefit will therefore be smaller than with Eagle Motors, yet significant at 500,000€.

Forza Macchina is a traditional company, yet also has a tradi- tion of minor quality issues. No mass call-backs are expected, yet Bronson will need to account for costs of 200,000€ for pleas- ing angry customers with gifts and vouchers.

Forza Macchina has recently cut back on its sales force to fos- ter its engineering backbone. This means that Forza Macchina’s sales force is not flexible enough to attend pop-up dealerships. This will result in costs of 600,000€.

Forza Macchina’s sales representatives are aware that OEMs need to be flexible to respond to customer wishes. They agreed to implement additive manufacturing such that Bronson market- ing can come up with several different body kits resulting in a 1,400,000€ benefit.

APPENDIX C Vignette material example: Supporting spreadsheet (misaligned goals; Marketing & Sales)

26 H. Franke and K. Foerstl

APPENDIX D Experimental process scheme

APPENDIX E

Post hoc robustness check: analysis of R² and effect sizes (df ceteris paribus) Repositioned constructs have been highlighted in gray

E.1: Politics as intervening mechanism between conflict types

* p < .05; ** p < .01

Non hypothesized links are estimated but not displayed for comprehensiveness. Grey constructs have been repositioned for model comparison .

Goal misalignment

Task conflict

Relationship conflict

Procedural rationality

Team member

satisfaction Political behavior

.538**

-.051

R² = .290

R² = .491

R² = .681

R² = .519

R² = .597 -.076

.689**

.217**

.587**

-.372** .181**

Cross-Functional Sourcing Teams 27

E.2: A “politics–conflict link”

APPENDIX F NCA bottleneck tables for political behavior (Y1 left) and procedural (ir-) rationality (Y2 right)

Y1 X1 X2 X3 Y2 X1 X2 X3 X4

Pol. Beh. (%)

Goal mis.* (%)

Task conf. (%)

Rel. conf. (%)

Proc. Rat.†

(%) Goal mis.* (%)

Task conf. (%)

Rel. conf. (%)

Pol. Beh. (%)

0 NN NN NN 0 NN NN NN NN 10 NN NN NN 10 NN NN NN NN 20 NN NN NN 20 NN NN NN NN 30 NN NN NN 30 NN NN NN NN 40 NN NN NN 40 NN NN NN NN 50 NN NN NN 50 NN NN NN NN 60 NN NN NN 60 NN NN NN 14.1 70 NN 10.2 4.0 70 NN 14.3 NN 30.1 80 3.5 31.2 14.2 80 18.7 34.3 NN 46.0 90 35.0 52.2 24.4 90 49.0 54.2 24.0 62.0 100 66.5 73.2 34.5 100 79.3 74.1 96.2 77.9

*post hoc measured using a scale from Jehn (1995). †Reversed for NCA; all results based on CE-FDH.

Goal misalignment

Political behavior

Relationship conflict

Procedural rationality

Team member

satisfactionTask conflict

.498**

-.372**

-.051

R² = .248

R² = .521

R² = .682

R² = .518

R² = .598 -.077

.216**

.323**

.262**

.555**

* p < .05; ** p < .01

Nonhypothesized links are estimated but not displayed for comprehensiveness. Grey constructs have been repositioned for model comparison .

28 H. Franke and K. Foerstl

APPENDIX G Necessary Condition Analysis (NCA): A quick overview based on Dul (2016)

NCA adopts a necessary but not sufficient logic, which differs from the regular linear sufficiency logic of traditional correla- tional or regression-based analysis techniques. It seeks to identify factors (X) that are necessary for some outcome (Y) to exist and can complement traditional methods of data analysis with addi- tional detail. Resources for NCA can be found in Dul (2016) and at http://www.erim.nl/nca. NCA is implemented in R.

Y: RC (%) X: TC (%)

0 NN 10 1.1 20 11.2 30 21.2 40 31.3 50 41.3 60 51.4 70 61.4 80 71.5 90 81.6 100 91.6

Starting point of NCA is a scatter plot of X and Y. An empty “upper left corner” of the scatter plot is an indication for a neces- sary condition (see example output based on this study’s data below). The plot shows a regular OLS line (green) and two default ways of determining a so-called ceiling, either as a step function using the “frontier” datapoints (red, CE-FDH: ceiling envelopment with free disposal hull) or as an OLS line through the frontier points (orange, CR-FDH: ceiling regression with free disposal hull). Important outcome of NCA is the effect size of a

possible necessary condition, which is computed from the overall area and the area of the so-called ceiling zone (upper left corner). For this case, the effect size d based on the orange line is .425, a value considered high for NCA (Dul 2016). Recently, a signifi- cance level p has been added to NCA (Dul et al., 2018), which is below .01 in this case.

Another main outcome of NCA is the so-called bottleneck table (see on the right). The table reflects the plot and allows to infer at which level X is constraining Y and how much. In this case, Y is absent when X is 0 and the stepwise function increases quite steadily (see plot). However, other cases often reveal that some Y is present even though X is 0 (“NN”) or shows big jumps in the stepwise function. The constraint of X on Y can thus be more complex, and the bottleneck table gives further information on its nature.

The relation between task conflict and relationship conflict seems to be, at least statistical, close to an ideal necessary condi- tion relationship in our data. It is important to add that NCA, as most common analysis techniques, needs thorough theoretical arguments to support a conclusion especially when NCA is used to test deduced theory rather than to explore inductively and post hoc as we do in this study.

SHORT BIOGRAPHIES

Henrik Franke (Dr. rer. pol., EBS Business School) is Research Associate at the German Graduate School of Manage- ment and Law (GGS), Heilbronn. His research addresses behav- ioral influences in cross-functional supply chain management teams. His work has been published in Journal of Business Logistics, Journal of Purchasing & Supply Management, and

-1 0 1 2

-1 0

1 2

3

OLS CE-FDH CR-FDH

Task conflict (standardized factor score)

R el

at io

ns hi

p co

nf lic

t (s

ta nd

ar di

ze d

fa ct

or sc

or e)

Cross-Functional Sourcing Teams 29

European Management Journal. He serves as (Junior) Editorial Review Board member for the Journal of Supply Chain Manage- ment and the International Journal of Operations & Production Management.

Kai Foerstl (Dr. rer. pol., EBS Business School) is Professor of Supply Chain Management at GGS, Heilbronn. In his research and teaching, he focuses on cross-functional supply chain teams,

reshoring/insourcing, and sustainable global sourcing. His research has been published leading outlets such as Journal of Business Logistics, Journals of Supply Chain Management, and International Journal of Operations and Production Manage- ment, and other scholarly and managerial outlets. He serves on numerous editorial review boards as associate editor and reviewer.

30 H. Franke and K. Foerstl

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