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Reducing behavioral constraints to supplier integration: a socio-technical systems perspective.


Supply management problems continue to persist in many well-known firms (Gilmore, 2006; Koch, 2004; Wailgum, 2007), threatening shareholder wealth (Hendricks & Singhal, 2003, 2005), and leading firms to compete through supplier integration (Petersen, Handfield, & Ragatz, 2005). When supplier integration (SI) is not achieved (Heriot & Kulkami, 2001; Parker, Zsidisin, & Ragatz, 2008; Wagner, 2003), the literature suggests that it is because of behavioral constraints (Fawcett, Fawcett, Watson, & Magnan, 2012). Behavioral constraints are actions by employees that inhibit company goals (Mabin & Balderstone, 2003), and, within the SI context, relate to workgroups in a buying or supplying firm impeding an SI initiative. While research reveals useful approaches to addressing such inhibitors of collaboration (Fawcett et al., 2012), literature lacks an explanation for why SI induces behavioral constraints and, therefore, how to design an SI initiative to prevent such behaviors from arising. We address this by presenting and using socio-technical systems (STS) theory to conceptually advance explanations and solutions for behavioral constraints to SI.

While SI can be technically challenging, there are also social and environmental forces inhibiting it (Cai, Jun, & Yang, 2010; Min, Kim, & Chen, 2008; Petersen, Handfield, Lawson, & Cousins, 2008). SI is defined as the unification of processes within and between buying and supplying firms (Das, Narasimhan, & Talluri, 2006). This contrasts with supply chain integration that involves both upstream customers and downstream suppliers (Fawcett & Magnan, 2002; Frohlich & Westbrook, 2001). An SI design entails such activities as information technology investment, new process adoption, workflow redesign, planning and control collaboration, and interorganizational projects (Vijayasarathy, 2010). With our unit-of-analysis being a focal buyer--supplier dyad, we distinguish between the process and state of SI. The process of SI represents a collaborative intra- and inter-firm initiative within the dyad to unify supply systems (Petersen et al., 2008). The state of SI represents the degree to which these systems are unified in forming the SI system. While the SI initiative seeks a particular state of SI, the emergence of behavioral constraints results in an under-realized state of SI. In this paper, we use STS theory's framework on the interplay of social, technical, and environmental systems to explain why these behaviors emerge (Griffith & Dougherty, 2001; Pas-more, 1988). To do this, we organize the many STS features into conceptual classifications for developing our propositions and advancing STS theory as a means to understand issues relating to SI (Skilton, 2011). While STS theory historically is used to explain intraorganizational phenomena (Trist & Bamforth, 1951; Trist & Murray, 1993), our study seeks to apply STS theory to interorganizational phenomena, showing its usefulness for SI research and practice.

Supply chain literature has drawn upon various theories to understand SI-related phenomena. However, these theories are insufficient in explaining behavioral constraints to SI. For instance, while transaction cost theory has been used to characterize efficiency reasons for SI choices (see Lockstrom, Schadel, Moser, & Harrison, 2011; Thomas, Fugate, & Koukova, 2011), it fails to acknowledge how SI choices are made for social reasons (Petersen et al., 2008). Likewise, while resource-dependence theory explains how SI processes are affected by levels of dependence (see Cai & Yang, 2008; Paulraj & Chen, 2007), it fails to acknowledge how SI processes emerge from technical design choices firms make (Carter & Ragatz, 1991). Other theories like the political economy paradigm (Stern & Reve, 1980) acknowledge social issues as important but do not give fine-grained, explanatory structure useful for understanding SI. Similarly, system-design theory has aided SI-related understanding (see Fawcett et al., 2012), but in very general terms. STS advances a perspective of SI that gives the requisite specificity to describe social outcomes while going beyond the efficiency- or power-based perspectives of traditional theories. In viewing SI as a technical unification of two STSs, we identify specific social processes that respond to the technical unification and lead to behavioral constraints. Moreover, we suggest changes to an SI design that promote positive social responses, while revealing environmental contingencies to help managers decide where resources should be deployed.

Our conceptual research challenges two assumptions in the SI literature: (1) that SI designs are not the cause of behavioral constraints to SI, and (2) that negative consequences of socialization are the result of opportunistic and malicious intent. Rather, our STS perspective suggests that the way SI initiatives are designed results in unintended social processes that affect behaviors. As such, we contribute to SI theory building in several ways (see Ketchen & HuIt, 2011): (1) we wend STS theory to the inter-organizational context to provide a framework for understanding behavioral constraints to SI; (2) we consider a broad array of social, technical, and environmental factors that inform socialization, social capital, and dark-side streams of SI research (Bernardes, 2010; Petersen et al., 2008; Villena, Revilla, & Choi, 2011); (3) we suggest approaches for preventing problems by designing SI initiatives that account for social processes; and (4) we reveal fruitful avenues of SI research and foreshadow new roles that supply managers will have in years to come. In sum, our paper offers new interorganizational uses for STS theory while suggesting multiple yet holistic avenues for improving the theory and practice of SI.


Research has suggested that STS theory can help supply processes across organizations (Chen, Daugherty, & Landry, 2009; Flynn, 2008; Hartley & Jones, 1997). Because supply processes are STSs that transcend organizational boundaries (Glaser, 2008), inter-organizational factors can explain partnering behavior more than intra-organizational factors (Hofer, Kn.emeyer, & Dresner, 2009). Likewise, social factors in supply chains have been noted to affect technical development, implementation, and use (Choi & Liken, 2002). To this point, Clegg (2000) states:

  "It is interesting that the examples to illustrate (STS) have all
  involved change within individual companies, rather than across
  companies ... There would seem, however, to be no reason why
  socio-technical thinking should not be extended to supply
  chains, partnerships and other networked ways of working that
  cross company boundaries." (p. 475)

The STS concept originated from studies of British coal mining methods by the Tavistock Institute (Trist & Bamforth, 1951). Early STS studies observed that employee behavior and work design were so intertwined that technical processes could not be understood without understanding social processes (Emery, 1959; Trist & Bamforth, 1951). The original theory focused on how work practices could increase productivity without large capital expenditures (Trist, 1981). Researchers have since applied STS theory to many fields: human relations (Cherns, 1976; Emery, 1959; Miller, 1959; Pasmore, Francis, Haldeman, & Shani, 1982; Rice, 1958; Trist, Higgins, Murray, & Pollock, 1963); ergonomics (Clegg, 2000); applied psychology (Cooper & Foster, 1971); engineering design (Griffith & Dougherty, 2001); organizational behavior (Fox, 1995; Pasmore, 1988; Seiler, 1967; Susman & Chase, 1986); information technology (Mumford, 2006); knowledge management (Pan & Scarbrough, 1998); and general management (Cummings, 1978; Woodward, 1958).

The supply chain field has used aspects of STS theory: social reactions to factory automation (Susman & Chase, 1986), human resource benefits in cellular manufacturing (Huber & Brown, 1991), group norms in self-directed logistics teams (Deeter-Schmelz, 1997), psychological ownership in quality management (Manz & Stewart, 1997), socially built competencies in handling supply chain complexity (Gloss, Jacobs, Swink, & Webb, 2008), and behavioral implications of supply chain alignment (Glaser, 2008). Not only do these various applications relate to SI--particularly regarding information automation, work practices, and structural alignment (Vijayasarathy, 2010)--they also reveal the widespread need to understand how management practices and social realities relate.

Although two themes comprise the STS perspective--STS design principles and STS theory (Griffith & Dougherty, 2001)--this paper focuses on STS theory. STS design principles comprise a subset of STS theory and have a prescriptive, action-research focus meant to improve organizational design and the quality of work life (Chems, 1987; Clegg, 2000; Mumford, 2006). In contrast, STS theory provides a framework for understanding relationships within organizations without prescribing what should be done. The theory aims to improve understanding before changing organizational design by stating the underlying causes for social and technical phenomena. Underscoring STS theory is the view that managers have options in designing organizational processes--that is, because socio-technical processes are not strictly determined, managers have choices in how systems are designed (Trist et al., 1963). Therefore, understanding how STS processes interrelate enhances design choices, and our study seeks to answer why STS processes within and between firms affect behavioral constraints to SI.

Elements of Socio-Technical Systems Theory

Socio-technical system theory creates a framework to analyze how components interrelate to affect organizational outcomes while in relation to a relevant external environment (Emery, 1959). STS theory uses subordinate concepts and propositions to describe and explain the behavior of organizations and their members (Emery, 1959) while providing critical insights into the relationships among people, technology, and outcomes (Griffith & Dougherty, 2001). These relationships can also be seen in exchange relationships, such as when interpersonal ties form between firms during supplier selection processes (Huang, Gattiker, & Schwarz, 2008), or when logistics managers face resistance to implementing electronic log-book systems (Cantor, Corsi, & Grimm, 2009), or when psychological reactions to time pressures hurt technical knowledge flows (Thomas et al., 2011). Viewing supply management problems through an STS lens creates a foundation to explain how people and processes interact across organizations to influence better outcomes.

Subsystem Definitions and Assumptions. Socio-technical system theory encompasses three general subsystems': technical, social, and environmental (Barko & Pasmore, 1986; Carayon, 2006; Pasmore et al., 1982; Seiler, 1967). The technical system "consists of the tools, techniques, artifacts, methods, configurations, procedures, and knowledge used by organizational employees to acquire inputs, transform inputs into outputs, and provide output or services to clients or customers" (Pasmore, 1988, p. 55). The technical component creates a structure within which organizational members must operate (Emery, 1959). Organizations add value through technical systems. Because supply chain management methods--such as process integration, lean systems, enterprise resource planning, and supplier development--are designed to improve performance, they too are part of the technical system. The partial unification of this subsystem between buying and supplying firms is what encompasses SI (Das et al., 2006; Vijayasarathy, 2010).

The social system "is comprised of the people who work in the organization and all that is human about their presence" (Pasmore, 1988, p. 25), such as attitudes, beliefs, relations, cultures, norms, politics, behaviors, and emotions. Individuals and groups achieve ends through social systems, sometimes at the expense of the organization (Rice, 1958). People rely on interpersonal contact for self-identity; groups use social rewards and punishments to regulate members (Seiler, 1967). Supply chains also have social systems, with formal and informal networks that cross firm boundaries and influence behavior (Carter, Ellram, & Tate, 2007). Individuals and groups in a supply chain may therefore act for their own purposes, regardless of supply chain requirements.

The environmental system envelops the social and technical systems. Because socio-technical processes are nested and multilevel (Moray, 2000), the focal STS defines the relevant environment. Our study focuses on the buyer--supplier dyad, which is comprised of a buying firm STS and a supplying firm STS. Beyond these firms' bounds are entities not directly controlled by ownership or fiat (Ellis, Shockley, & Henry, 2011). These entities are part of the environmental system, which indudes the relevant governmental, economic, industrial, transportation, and cultural firm contexts (Pasmore, 1988). As such, those contextual forces surrounding the buyer--supplier dyad constitute the environmental systems of relevance. Because a focal STS adapts and affects its environment, an STS is considered an open rather than a closed system (Emery, 1959). Managers must pursue strategies, select resources, and implement technologies aligned with environmental stressors (Rasmussen, 2000). For example, public safety concerns can instigate the implementation of new technical reporting processes (Carter et al., 2007) or new social sustainability expectations (Tate, Ellram, & Kirchoff, 2010), all of which influence upstream and downstream supply chain practices.

Socio-technical systems emerge where social, technical, and environmental systems interact to create organizational outcomes (see Figure 1). All organizations are STSs (Cooper & Foster, 1971), as are many aspects of a supply chain (Choi & Liker, 2002). As Pasmore states, "whenever there are people, working together in a system with technology, in an environment that provides resources the system needs, there is the possibility of adapting STS thinking" (1988 p. 155). STS theory is not focused on social or technical system self-interaction (Emery, 1959), such as how enterprise resource planning systems impact six-sigma efforts (Nauhria, Wadhwa, & Pandey, 2009), or how groups impact employee satisfaction (Robinson & O'Leary-Kelly, 1998). Instead, STS theory describes how results emerge from interactions among social, technical, and environmental systems. This macro-level perspective suggests systemic causes for SI problems, creating new opportunities for understanding and addressing behavioral constraints to SI.

Pasmore et al. (1982) lay out assumptions made within STS theory (shown in Table 1) that can be grouped into descriptive assumptions (system comple-mentarities, variance diffusion, boundary location, design incongruence, and organizational choice) and prescriptive assumptions (open systems, quality of work life, and joint optimization). Descriptive assumptions depict how STS subsystems interact and how managers have a role in maintaining an STS. The prescriptive assumptions emphasize that successful STSs require adaptation and consideration of the human condition. Although the definitions, boundaries, and assumptions traditionally refer to a single organization, as buying and supplying firms unify that they should find these concepts helpful in managing SI (Clegg, 2000). For instance, the design incongruence assumption suggests that supply management activities should adapt to changing cultures and economic conditions (Carter, Maltz, Maltz, Goh, & Yan, 2010).


Assumptions of Socio-Technical Systems Theory (a)

Descriptive assumptions

System complementarities Variance  Technical and social
diffusion (b)                      characteristics reinforce and/or
                                   deter each other. Unplanned
                                   deviations from technical
                                   standards promulgate into the
                                   socio-technical system, creating
                                   disturbances and negative

Boundary location                  Necessary disconnects exist within
                                   organizations and between their
                                   environments, causing problems
                                   with control, coordination and
                                   knowledge and requiring managers
                                   to span these boundaries.

Design incongruence (d)            Organizational designs eventually
                                   become inappropriate within a
                                   changing environmental context.

Organizational choice              Organizations can be designed in
                                   different ways to achieve the same
                                   ends, and knowledgeable choice
                                   exists at all levels of the

Prescriptive assumptions

Open systems                       Organizational survival requires
                                   adaptive transactions with a
                                   continually changing external

Quality of work life               Organizations must consider human
                                   needs in the design of work,
                                   beyond the organizational benefits
                                   of joint optimization.

Joint optimization                 Organizations function optimally
                                   only when both the technical and
                                   social subsystems are designed to
                                   fit the demands of each other and
                                   the external environment.

(a.) Based upon Pasmore et al. (1982) in which these were referred to as
STS elements.

(b.) Pasmore et al, referred to this as support congruence, but the
underlying issue relates to STS characteristics helping or hurting
each other.

(c.) Pasmore et al, referred to this as variance control, but the
underlying issue relates to errors having multiple effects.

(d.) Pasmore et al, referred to this as continuous learning, but the
underlying issue relates to organizational designs losing their

Reciprocal Interactions. To further understand how the elements of a STS influence each other, Emery (1959), Seiler (1967), Pava (1986), Pasmore (1988), and Fox (1995) provide detailed features for the social, technical, and environmental systems within the STS perspective. These features provide building blocks for STS-based propositions and enable SI researchers to identify pertinent STS processes. We organize the features into distinct categories within each system to retain STS theory's uniqueness and generalizability while improving parsimony and comprehensibility in relation to SI (Wacker, 1998).

As shown in Table 2, we consolidate the technical features into four technical system concepts that have social implications. First are technical centralities (Ti) that represent the dominance and importance of technical process characteristics (Emery, 1959). Features related to this concept are (1) the levels of automation that determine relative worker contribution and (2) the variation in importance of process steps that determines an employee's role significance. Second are technical requisites (T2) that represent the surrounding criteria for technical functioning (Pava, 1986). Features related to this concept are (1) the task conditions (e.g., physical, psychosocial, knowledge) from which workers infer their worth and (2) the support dependencies from which the value of role relations emerge (Emery, 1959). Third are technical proximities (T3) that represent the closeness that technical activities have with each other and the environment (Fox, 1995). Features related to this concept are (1) spatiotemporal distributions that influence interpersonal contact and information exchange and (2) environmental contacts that influence boundary-spanning activities. Last are the technical flows (T4) that represent the stream of value-accumulating artifacts (e.g., products and ideas) (Fox, 1995). Related features are (1) input variations that influence stress in the workforce and (2) technical sequencing that influences worker skills and knowledge exchange.

TABLE 2 Technical System Features (a)

Feature                    Description of Feature and Impact on Social

T1: Technical              Automation: The use of devices (e.g.,
centralities               mechanical, electronic) for automatic
                           decisions and effort; this determines the
                           relative contribution of people.

                           Operational impact The criticality, focus,
                           and skill demands of activities vary; this
                           influences the significance of certain work

T2: Technical requisites   Condition: The situational task demands in
                           the work setting (e.g., physical and
                           psychosocial) or in the artifacts (e.g.,
                           products and ideas); these can be
                           over/under stimulating and distracting;
                           workers infer what is valuable by these

                           Support dependence: The degree to which
                           processes need other functions (e.g.,
                           maintenance, engineering) to maintain
                           proper conditions; this influences the
                           value of role relations. (b)

T3: Technical proximities  Spatiotemporal distributions: The layout
                           among and time between workers, machines,
                           and process steps; these influence
                           coordination and communication
                           requirements, interpersonal contact, and
                           information exchange.

                           Environmental contact: The importance of
                           inbound and outbound linkages with the
                           external environment; this creates demands
                           for boundary-spanning management and
                           coordination. (b)

T4: Technical flows        Input variance: The variation from upstream
                           inputs; this continually stresses
                           labor/skill requirements, straining
                           individuals, workgroups, and management.

                           Sequencing: The way unit operations (value
                           adding activities) are grouped into
                           production phases; this influences demands
                           for labor skills, shared information and
                           knowledge, and coordination.

(a.) Expanded from Fox (1995) and Emery (1959).

(b.) Characterizes the boundary conditions for the supply chain
management function.

The features of a social system from STS theory that have implications for the technical system are consolidated into the four concepts shown in Table 3. First are social positions (Si) that represent the locations within the organization's social structure (Pasmore, 1988). Features related to this concept are (1) the status landscape and (2) social networks, both of which informally challenge formal relations, controls, and knowledge. Second are social values (S2) that represent the cultural attitudes within the organization (Pasmore, 1988; Seiler, 1967). Features related to this concept are the (1) collective predispositions and (2) social needs, both of which influence how members behave, what is important, and how decisions are made regardless of technical needs. Third are social associations (S3) that represent the composite of functional memberships in organizations (Seiler, 1967). Features related to this concept are (1) social roles and (2) affiliations, which give employees purpose (Kuhn 1976) and influence levels of cooperation and control (Fox, 1995). Last are social experiences (S4) that represent the understandings that result from social interactions (Fox, 1995; Weick, 1995). Features related to this concept are sentiments (Seiler, 1967) and social endowments, both of which are key influences on the efficacy of choices made in the work place.

Pasmore et al. (1982) describe the way the social and technical systems interact as multiordered, primary and secondary effects. Primary effects are those that can be seen in Tables 2 and 3 as the direct reciprocal influences among features. For instance, technical centralities (T1) influence the contribution and significance of workers, which influence the attention given to them and their influence in the firm--that is, their social position (Si) (Emery, 1959). In turn, social position affects who is the source of knowledge in a firm--that is, the technical flows (T4) (Seiler, 1967). These primary effects are fairly obvious to see, such as when employees realize that real-time data improve customer service (Klein, 2007). Secondary effects, however, are more difficult to understand because of the delays and complexities within the reciprocal STS influences (Pasmore et a/., 1982). For instance, employees may not see that assuring real-time data requires higher levels of planning and communication, more software customization, and more trust between groups (Klein, 2007). Thus, a newly implemented technical system places a cascade of demands across multiple levels of an organization (Moray, 2000), obscuring the impact that a social or technical change can have on the STS.

TABLE 3 Social System Features (a)

Feature                  Description of Feature and Impact on
                         Technical System

S1: Social positions     Status landscape: The varying degrees of
                         importance and leadership among people;
                         these will challenge formally given
                         authority regarding influence and sources
                         of knowledge.

                         Social networks: The network of
                         interpersonal relations distributes
                         social knowledge and opportunities for
                         helpfulness; this creates forms of
                         reciprocity that challenge official
                         knowledge and duties.

S2: Social values        Collective predispositions: The shared
                         mental models, motivations, values,
                         norms, self-identity, fairness, and
                         psychological contracts; these each
                         compete with what is important to
                         organizational performance.

                         Social needs: The presence of personal
                         worker goals and interdependencies; these
                         threaten formally specified
                         organizational goals depending upon their
                         over- or under-specification.

S3: Social associations  Social roles (b): The nature of
                         responsibilities (i.e., work roles)
                         within the social organization; this
                         impacts cooperative behavior,
                         responsibility for variation in processes
                         and outputs, territories and resource

                         Affiliations: The influence of informal
                         group membership, accompanied by rewards
                         and punishments; this creates forms of
                         motivation and challenges formal
                         workgroup control.

S4: Social experiences   Sentiments: The collective emotional
                         role-experience of workers (i.e. inherent

                         attractiveness, dependence perceptions,
                         justice, subordination, self-worth,
                         trust, and social isolation); this
                         influences decision making and contradict
                         assumed rationality.

                         Endowments: The basic talents, acquired
                         skills, knowledge, expertise, and
                         professional standards; these create
                         technical dependencies, allow technical
                         deficiencies, and introduce
                         non-organizational standards in

(a.) Expanded from Fox (1995), through reexamination of Emery (1959),
Seiler (1967), Pasmore (1988)

(b.) Term introduced by Emery (1959) that Fox later divided into four
sub-features that can be summarized as follows: (1) codependency in
work roles, (2) output responsibility, (3) distribution of resource
allocation responsibility, and (4) responsibility diffusion of

Beyond the interrelations between social and technical systems, characteristics of the relevant environmental system interact with an STS as summarized in Table 4. First is equivocality (E1), which characterizes the nature of interactions between the STS and external subsystems. Features related to equivocality are (1) turbulence--the rate of change of external resources and demands (Pasmore, 1988), (2) complexity--the number of inter-relationships among external resources and demands (Pasmore, 1988), and (3) connectivity--the number and type of linkages with entities in the environment (Seiler, 1967). Turbulence and complexity influence STS-environment alignment and necessitate adaptations to support the survival of the STS. Through interactions and associated information flows, connectivity provides the means through which the STS gains awareness of the turbulence and complexity inherent within the environment. Second is opportunity (E2), which includes environmental features influencing the range of feasible STS designs that satisfy external demands. Features related to this concept are (1) resource alternatives--availability of technological, human, or organizational inputs to facilitate STS designs or adaptations (Pasmore, 1988) and (2) environmental mutability--the ability of the STS to modify the elements of the environment, influencing how constrained the STS is, and whether an internal versus an external focus is needed for survival (Pasmore, 1988).

TABLE 4 Environmental System Featuresa (a)

Feature                         Description of Feature and Impact on

E1: Environmental equivocality  Turbulence: Rate of change of external
                                systems' resources and demands;
                                environmental changes influence
                                information requirements and
                                understanding, and compromise
                                STS-environmental alignment
                                necessitating STS adaptations for
                                continued survival.

                                Complexity: Number of and relationships
                                among external systems' resources and
                                demands; known, unknown, and
                                conflicting interactions inhibit
                                complete understanding of the current
                                state and ability to accurately predict
                                the future state of environment.

                                Connectivity: Extent that external
                                systems permeate the boundaries of the
                                STS; the number and variety of linkages
                                with external systems influences the
                                availability of information that
                                enables the characterization of the

E2: Environmental opportunity   Resource availability: Number and
                                variety of inputs from external systems
                                that an STS may act upon for survival;
                                available alternatives influence the
                                flexibility with which an STS can adapt
                                to its environment.

                                Mutability: Ability of an STS to change
                                external systems to support continued
                                operations; the number and variety of
                                external demands influence the ability
                                of the STS to create an environment
                                that is well-aligned with its goals,
                                values, and overall well-being.

STS, Socio-technical system.

(a.) Represents an integration of salient principles advanced by Pasmore
(1988), Seller (1967), Thompson (1967), and Weick (1995).

The above-mentioned STS concepts provide the basis for the SI propositions given in the next section.


In this section, we develop STS-based propositions regarding behavioral constraints pertaining to SI. Propositions one and two relate to within-firm social events (i.e., regarding social position and social value); propositions three and four relate to between-firm social events (i.e., regarding social associations and social experiences); and proposition five relates to environmental contingencies (i.e., regarding environmental environmental and opportunity). For each within- and between-firm proposition, we give STS-based causes for behavioral constraints and STS-based designs for mitigating behavioral constraints. While we state each proposition formally and generally, specific examples are offered that embody the generalized proposition. Because designs are context-specific, they must be applied after careful assessment of particular situations.

Figure 2 shows the STS perspective of SI. Before SI, buying and supplying firms are separate STSs where traditional boundary-spanning functions manage exchanges: purchasing/supply management (PSM) and sales/marketing. However, to be competitive, the dyad improves efficiency and effectiveness through integrating its technical systems as shown in the bottom of Figure 2 with functional collaboration within and between firms (Das et al., 2006; Frohlich & Westbrook, 2001; Sanders, 2007; Swink, Narasimhan, & Wang, 2007). We assume the SI process is supported by top management and is thus a firm-level choice (Fawcett et al., 2012; Handfield, Petersen, Cousins, & Lawson, 2009). Because SI is a state of unity between technical systems (Das et al., 2006), combining two decoupled STSs requires changes to technical features (i.e., centralities, requisites, proximities, and flows) that induce social events instigating behavioral constraints. We do not attempt to explain particular behavioral constraints (e.g., resistance to change and intentional practice modification) as those are beyond the scope of this paper, but we give possible behaviors for illustrative purposes.

Social Positions within Firms during SI

As the SI initiative changes the technical centralities (T1 in Table 2) of buying and supplying firm processes, threats to social positions (Si in Table 3) can occur within a partner firm and cause behavioral constraints. As illustrated in Figure 2, SI seeks to increase technical system coupling, so that the functions in buying and supplying firms coordinate activities seamlessly. The dyad benefits in this; between firms, operations functions can communicate material needs directly (Das et al., 2006), while engineering functions coordinate designs directly (Petersen, Handfield, Ragatz, 2003). However, the redistribution of communication and material flows within the dyad influences functional centralities. For example, before SI, materials and designs were coordinated through traditional boundary-spanning functions. Yet during SI, the importance of sales/marketing managers may diminish as technologies that automatically communicate needs reduce the need for intervention (Do Cho & Chang, 2008). Similarly, as the state of Si increases, the PSM coordination role may be reduced (Emmelhainz, 1987; Gelderman & van Weele, 2005), threatening its social position in the buying firm. The SI initiative, therefore, can threaten existing social positions, particularly within the traditional boundary-spanning functions, while concurrently giving social position opportunities for a firm's engineering, production, or IT functions. Interestingly, while literature suggests that high PSM social position is needed for instigating an SI initiative (Paulraj & Chen, 2007), the STS perspective suggests that PSM social position can be threatened as SI is implemented.

As social position is threatened, behavioral constraints will emerge. According to STS theory, workers within functions have their own goals beyond those of the organization (S2 in Table 3), and maintaining one's functional social position is significant (Seiler, 1967). Threats to social position from technical cen-tralities can be met with resistance (Pasmore, 1988), that is, sabotage, withdrawal, reducing commitment, and ignoring requests (Kirkman & Shapiro, 1997). Such resistance may manifest itself in the types of technical choices employees make (see Table 1). For example, threatened functions with control over the Si design can co-opt the technical change for their own goals (Lozeau, Langley, & Denis, 2002), designing or using the technical system to uphold their social positions. Fears of being outsourced similarly threaten social position (Morrissey, 2006). Functions may seek to maintain others' reliance on them, requiring information to be routed through them to retain control, thereby diminishing the syncretism (Oreg, 2003). A threatened function can also constrain the SI designs to be similar to existing technical processes and not accommodate actual technical needs (Clegg, 2000), inducing "work-around behaviors" in the system (Bendoly & Cotteleer, 2008).

Proposition la: During the SI process, threats to functional social position within a firm increase the likelihood for behavioral constraints.

While threats to social position exist when functions are removed from supply processes, designing compensating technical centralities and requisites into the SI initiative can alleviate these threats and reduce behavioral constraints. An STS perspective seeks SI designs that "satisfy higher order needs" such as social position (Pasmore, 1988, p. 49) and to counterbalance social position losses with social position gains. This means new roles but equivalent social positions: refocusing activities and creating new dependencies, de-automating certain tasks, raising skill levels and improving work climate (Adler & Kwon, 2002; Llewellyn & Armistead, 2000). High-status employees can be useful, perhaps, in other functional roles and can be transferred accordingly.

Further, traditional boundary-spanning functions can be given a facilitating role rather than an operating role in the new SI system. This is particularly important when merging technical systems with substantially different technical features. Firms struggle with integrating systems when they have differing production cycles (Kauremaa, Smaros, & Holmstrom, 2009), organizational cultures (Braunscheidel, Suresh, & Boisnier, 2010), organizational structures (Nahm, Vonderembse, & Koufteros, 2003), or process designs (Green & Inman, 2006). The SI initiative can preempt social position threats by including a communication campaign showing how such facilitating roles will be needed in the future. While these are only examples, the important STS insight is that rather than target the behavioral constraints directly, designed-in technical features can prevent the behavioral constraints from occurring.

Proposition lb: Behavioral constraints from threats to functional social position are reduced by designing the SI initiative with technical centralities and requisites that maintain social position equivalence.

Social Values within Firms during SI

Behavioral constraints can arise from new technical requisites (12 in Table 2) that stem from the SI initiative and create divergences within a firm's social values (S2 in Table 3). Divergence occurs when at least one functional unit forms social values substantially different from other functions in the firm, creating dysfunctional cultural diversity (Pasmore, 1988). Functional subcultures with divergent social values can form as new technical requisites alter internal information and knowledge needs (Sackmann, 1992). In accordance with Pasmore et al. (1982), multiorder social value changes will result from new technical requirements for coordination, skills, and knowledge. For instance, Shook (2010) describes how task demands and collaboration requirements of lean manufacturing change company values toward problem discovery and resolution. Carter and Ragatz (1991) show that EDI systems increase the required level of interaction and interdependence among functional units. In general, supply partnership research shows that a buyer's corporate initiatives can change the mindset of a supplier's employees (Bjorklund, 2010), allowing one partner to be a catalyst for change in the other (Hartley & Choi, 1996). This partially explains why changes to buyer--supplier relations involve social value change (McIvor & McHugh, 2000).

Socio-technical system theory further notes that social value change is discontinuous and the way it affects functions depends on the nature of technical system changes (Pasmore et al., 1982). According to Seiler (1967), discontinuous social value change raises behavioral uncertainties; that is, because social systems describe how employees should act, if social values are inconsistent across functional units within a partner firm, then predictability problems arise. This is problematic in SI because intra-company, cross-functional technical coordination is crucial (Das et al., 2006), and diverging social values make cross-functional activities difficult to coordinate and, subsequently, problematic (Goebel, Marshall, & Locander, 2003; Hartley, 2000; Rozemeijer, van Weele, & Wegg-eman, 2003). As an example, Whitfield and Landeros (2006) describe how the PSM function had difficulty implementing a supplier diversity program because other functions did not share in the value of it. Behavioral constraints like inter-functional conflict, communication difficulties, misalignment of goals, and unpredicted choices create disconnects and uncoordinated processes (Harris, Ogbonna, & Goode, 2008; Keaveney, 2008). Diverging social values, then, create inter-functional tension, decreasing the likelihood for intra-company coordination.

Proposition 2a: During the SI process, diverging social values within a firm increase the likelihood for behavioral constraints.

The basic premise of STS theory is that technical change inevitably involves social change and vice versa. As such, instead of resisting social value change within a firm, the SI initiative can be designed to facilitate collective social evolution (Harris a al., 2008). That is, an S'TS perspective would seek SI designs that facilitate social processes within a partner firm that enable convergence and transcendence to a new social value structure in the face of technical change. Pasmore (1988) describes approaches to achieve this through technical designs that (1) are flexible and (2) facilitate social processes. The former approach is familiar to the supply chain community as employee involvement (Mclvor & McHugh, 2000) that is meant to temper social value conflicts by modifying the technical requisites to fit existing social values. Our interest is with the latter approach.

Table 2 shows multiple approaches to suggest how an SI initiative can be designed to facilitate social processes to aid co-evolution and prevent social values from diverging within a firm. Technical requisites can be designed to specify dependencies, such as creating overlapping responsibilities for the sake of bridging relations, avoiding isolation, and promoting shared values (see Glaser, 2008). Technical requisites could also be so arranged as to influence the level of coordination among functional units. For example, imposing a requirement that training for the new SI systems is to be conducted by in-house personnel can facilitate social processes (Klein & Weaver, 2000). Further, creating in-house discussion boards for SI problems helps the social network to operate. The design of technical proximities provides a means to facilitate co-evolution; the tightening spatiotemporal arrangements and colocation of groups by product line or process raise interpersonal contact and promote shared goals (see Gittell, 2000). Because functions within a firm have the common purpose of firm survival, higher functional interdependence encourages shared agreements upon which to operate (Pasmore, 1988). This proposition carries the assertions of Petersen et al. (2008) one step forward to suggest within-firm socialization needs to be achieved to enable SI, not just between-firm socialization. Thus, the more technical interactions that functions and employees have within a firm, the more likely new social understandings will emerge, helping social value convergence.

Proposition 2b: Behavioral constraints from diverging social values are reduced by designing the Si initiative with technical requisites and proximities to facilitate social processes.

Sodal Associations between Firms during SI

We address in P1 and P2 within-firm social processes that are often neglected in the SI literature. In P3 and P4, we address between-firm social processes that have received attention in the SI literature but that require more refinement. As such, our next proposition involves behavioral constraints from new social associations across firms (S3 in Table 3) that result from the SI initiative's change in technical proximities (T3 in Table 2). Extant research describes SI as a mutual process between buying and supplying firms (Terpend, Tyler, Krause, & Handfield, 2008). STS theory states that bringing people together through closer technical proximities will increase interpersonal relations, information exchange, and interdependence (Emery, 1959). Tighter coordination of activities and the need for faster decision making bring boundary-spanners into more regular contact with higher information exchange and more relatedness (Fox, 1995). Social interactions over time heighten the possibility for social associations across firms. Petersen et al. (2008) describe this between-firm phenomenon as a socialization process, and Bernardes (2010) notes such processes build social capital that influence buyer-supplier relations. While this has been shown to increase the likelihood of SI, a downside is also as likely.

When social associations form across the buyer-supplier dyad, SI behaviors and decisions will increasingly be influenced by the attractiveness of these social associations, leading to behavioral constraints to SI. According to STS theory, technical designs are not rigid but are adaptable (Pava, 1986). Even firms using the same technical tools (software, hardware, or knowledge routines) can choose different work arrangements (Pasmore, 1988). As the satisfaction and personal benefits received through social association increase, cohesion increases, the influence of group norms grows, and formal technical controls are challenged (Seiler, 1967). Attractive social associations create interpersonal social goals that can conflict with formal organizational goals. Emery (1959) observes that this behavior constrains organizational effectiveness because behavior is aimed at achieving social rather than technical goals. For instance, during joint product development, closely associated buyer and supplier design engineers may agree that stringent product specifications are needed because of professional pride regardless of customer needs; over-engineering complicates designs and leads to higher costs (Keaveney, 2008). Likewise, personal associations between PSM and supplier sales personnel leads to choices that avoid animosity (i.e., acquiescing to maintain harmony), at the risk of compromising technical benefits (Sheth & Parvatiyar, 1995). Ulterior social motives create behavioral constraints by compromising implementation requirements, extending execution time, and obfuscating technical criteria, thus disabling the technical unification of processes.

Proposition 3a: During the SI process, the attractiveness of social associations between firms increases the likelihood for behavioral constraints.

While the attractiveness of social associations with a partner firm may constrain SI, technical designs can control social associations and keep technical goals salient. Studies in the "dark side" of close buyer--supplier relationships suggest that an ideal degree of association is desirable. Anderson and Jap (2005) show that social associations promote trust, but are leveraged to ignore product quality irregularities. Cu, Hung, and Tse (2008) find that close social relations facilitate channel effectiveness but stunt the flow of new ideas. Similarly, empirical findings imply a curvilinear relationship between behavioral constraints and social capital, thereby suggesting an optimal level of relatedness (Villena et al., 2011). Designing the SI initiative to manage the degree of relatedness and keep technical goals salient will diminish the behavioral constraints that stem from social associations between firms.

SI initiatives that are designed with technical proximities and flows (T3 and T4 in Table 2) that manage social associations between firms can be useful, such as assuring spatiotemporal variety between firms through job rotations or through computer-supported networking that can control tie strength (Wellman et al., 1996). Formal codes of conduct based on external professional standards can counteract the influence of between-firm informal connections (Wilkinson, 1992). Requiring specific information to accompany SI decisions (e.g., formalized costs and competitive analyses) necessitates the involvement of departments from both firms with less professional affiliation (e.g., engineers and accountants). This creates norms of procedural justice, assures the alignment of SI choices and goals, and mitigates the utility of social justifications (Tyler, Dienhart, & Thomas, 2008). Last, because social associations are attractive for information access (Seiler, 1967), such attraction can be reduced by assuring that the SI design contains sufficient technical information flows to provide individuals with explicit knowledge needed (Ogden, Petersen, Carter, & Monczka, 2005). Without designing these preventative measures into the SI initiative before engaging in collaboration, behavioral constraints to SI will more likely have to be addressed postintegration.

Proposition 3b: Behavioral constraints from social associations are reduced by designing the SI initiative with technical proximities and flows that manage between-firm relatedness to assure technical goal salience.

Social Experiences between Firms during SI

Changes to the technical flows (T4 in Table 2) between firms can bring about social experiences (S4 in Table 3) that reduce confidence in the SI system and induce behavioral constraints. Such unassuring social experiences that SI invokes directly or indirectly lead to the discomforts observed by Fawcett et al. (2012). The direct experience from SI is that the source of input variance changes (Halley & Nollet, 2002): A supplying firm's demand variance source becomes the buying firm's downstream boundary (Walton & Gupta, 1999) and the buying firm's supply variance source becomes the supplying firm's upstream boundary (Kull & Closs, 2008). Because firms have a history of investing monetarily and cognitively to handle input variation (Pasmore, 1988), new and unexpected input variances strain firms and functions (Emery, 1959; Pasmore et al., 1982). The resulting ambiguity leads to uncertainty, anxiety, and cognitive dissonance in which negative sentiments emerge (Seiler, 1967). These sodal experiences erode confidence and heighten distrust of the SI system, creating more obstades and resistance to collaboration (Fawcett et al., 2012; Nyaga, Whipple, & Lynch, 2010).

The indirect effect from SI occurs from the resequencing of work during the SI process. SI increases coordination and interaction between like-functions across the buyer--supplier dyad (Koufteros, Cheng, & Lai, 2007). Through interaction, a common language and standardized approaches to collaboration evolve, increasing opportunities for functional counterparts to transfer tacit knowledge across firm boundaries (Nonaka & von Krogh, 2009). While enhanced tacit knowledge transfer increases the ease with which functional managers can complete tasks, it decreases the explicit technical knowledge available for system-wide dissemination (Pan & Scarbrough, 1999). In this way, SI can enable socialized knowledge to those engaged in tacit knowledge transfers between firms, but can disable explicit knowledge to those other functions making between-firm decisions. The result for these other functions is increased ambiguity and reduced confidence in the SI system (Weick, 1995), leading to the resistance to change or distrusting of information observed by Fawcett et al. (2012). As an example, when engineers between firms tacitly coordinate designs, buying personnel will be less informed explicitly of technical specifications, creating coordination difficulties and unassured experiences in the SI system. While personnel could be instructed to keep other functions informed, formal incentives typically do not reward such cross-functional actions (Oliva & Watson, 2011).

Proposition 4a: During the SI process, unassuring social experiences between firms increase the likelihood for behavioral constraints.

The above-mentioned behavioral constraints result from inattention to social experiences that STS theory would view as inevitable when technical systems are changed for SI purposes (Pasmore, 1988). Avoiding such behavioral constraints before an SI initiative begins, therefore, requires designing technical flows and centralities that bridge social-to-technical linkages. For instance, negative affect from novel variance is largely caused by an underinvestment in educating employees about the forces involved in SI (Fawcett, Magnan, & McCarter, 2008). Beginning an SI initiative with technical training to educate buying and supply firm personnel on likely future variances can prepare them. Other ways to build in knowledge flows are to design technical systems that provide access to upstream and downstream visibility (Barratt & Oke, 2007) and to require that technical briefings be communicated. Investing in these technical flows can prevent negative affective experiences and resistance.

Social processes can be technically supported, such as by using technical systems to facilitate socialization. Firms continue to struggle to balance their IT systems and supply chain strategies (Thun, 2010). Evidence shows that, through the use of electronic collaboration tools, a buyer-supplier dyad can improve collaboration and knowledge (Costa & Tavares, 2012; Fayard, Lee, Leitch, & Kettinger, 2012). Automating the tacit-to-explicit process can relieve the burden of employees having to do so. Using social networking technology, between-firm blogging, and even text messaging are approaches to make social experiences explicit (Raghuram, 1996). In many ways, if the technical system remains connected to the social system, then social experiences will not be left unattended and the meaning of SI will not be lost.

Proposition 4b: Behavioral constraints are reduced by designing the SI initiative with technical flows and centralities that give attention to social experiences.

Environmental Influences during SI

While firms' social and technical features interact during SI, we propose environmental contingencies to influence such interaction. Regarding environmental equivocality (El in Table 4), the effects of turbulence, complexity, and connectivity will motivate technical system changes that increase the likelihood for behavioral constraints. Prior to SI, the STS of the buying and supply firms will have environments comprised of unique sets of resources, demands, and stakeholders (Pasmore, 1988). In the course of SI, however, each firm's connectivity becomes relevant to the other as joint approaches to coordinated decision making displace unilateral actions. Inherently, then, SI leads to more complex decision frames as the aggregated resource constraints and demands of partner firms impose a greater number of decision variables along with an increased number of known, unknown, and conflicting inter-relationships (Carayon, 2006). Drawing from STS theory (Pasmore, 1988), the equivocality inherent in an environment characterized by high levels of dyadic connectivity suggests that external issues will have greater influence during SI. High equivocality constrains the number and type of technical systems that (1) are feasible within both the buyer's and supplier's environment and (2) accomplish the intended goals of the integration effort. Accordingly, equivocality increases the likelihood for and the degree of technical system adaptations that, in accordance with P1 a, P2a, P3a, and P4a, increase the likelihood of behavioral constraints to SI.

Recent integration efforts between Apple Inc. and Fox-conn provide insights into environmental equivocality, the need for technical adaptations, and behavioral constraints. Foxconn is a large electronics manufacturer with high connectivity to China's government and labor markets (Weir, 2012). Foxconn has achieved production efficiencies through human resource techniques that are viewed as intolerable to some Western societies (Duhigg & Greenhouse, 2012). In contrast, Apple is highly connected to various stakeholders who demand fair treatment of workers (Weir, 2012). Each firm's ties to stakeholders with disparate aims added substantial complexity to their SI efforts and led to behavioral constraints. For example, in response to environmental demands for increased corporate social responsibility, Apple sought to integrate its strategy with Foxconn by having Foxconn reduce worker hours, increase worker pay, and improve worker living conditions (i.e., change the technical requisites, T2 in Table 2). However, Foxconn resisted change and implemented both superfluous and deceptive solutions to pacify concerns, installing managerial appointees to represent worker groups and unions, and instructing employees to mislead auditors so that labor violations might go undetected (Duhigg & Greenhouse, 2012).

Proposition 5a: During the SI process, environmental equivocality increases the likelihood for STS-based behavioral constraints.

Whereas equivocality limits the number of technical solutions that are mutually acceptable to partner firms during SI, we propose that environmental opportunity--resource alternatives and mutability--has the opposite effect and reduces the likelihood for behavioral constraints. First, resource alternatives represent the number and type of resources from which the buying and supplying firms may select during SI. An environment with a high variety of resource alternatives allows discretion in technical system design by facilitating a rich set of possible technical solutions to accommodate SI. The inherent degrees of freedom in technical system design increase the likelihood that the firms can (1) design a technical system that satisfies environmental demands, (2) preserve the primary features of their existing technical and social systems, and (3) meet the goals of the SI effort. For example, in the case of collaborative planning, forecasting, and replenishment, a variety of environmental resources may be available like translation tools and web intermediaries that allow unification of information flows despite having different information systems (Pramatari 2007). This allows partner firms to continue using existing internal automation tools to synchronize technical flows while preserving technical central ities and, in turn, reducing the likelihood of behavioral constraints.

Second, mutability also reduces the likelihood of behavioral constraints to SI, yet it does so by drawing on firms' abilities to change the environment rather than react to it (Pasmore, 1988). That is, in facing dysfunctional social reactions to technical changes, firms can influence a mutable environment in a way conducive to SI. For instance, the dyad's customers may be flexible in requirements or the dyad's suppliers may be flexible in deliveries (Lao, Hong, & Rao, 2010; Zhang & Tseng, 2009). By enabling the creation of a more favorable environment, mutability renders external demands and resource constraints less relevant (Pasmore, 1988). Hence, a highly mutable environment increases the likelihood that the buyer and supplier can enact mutually acceptable technical features that meet SI objectives while preserving the primary features of both parties' STS. In so doing, firms may avoid behavioral constraints to SI.

Proposition 5b: During the SI process, environmental opportunity and mutability decrease the likelihood for STS-based behavioral constraints.


In this paper, we demonstrate how STS theory provides a novel perspective to aid theoretical and practical insights to SI. Research has generally been asymmetric in its approach to the social and technical influences within Si, focusing on either one or the other, but not both. This is likely because theories such as transaction cost economics theory generally ignore social factors (Ireland & Webb, 2007), while theories such as social capital theory generally ignore technical factors (Monczka, Petersen, Handfield, & Ragatz 1998). Although some SI literature compensates for asymmetry by combining theories (Hofer et al., 2009; Ireland & Webb, 2007; Krause, Handfield, & Tyler, 2007; Lai, 2009), the single framework of STS theory enables a self-contained systems perspective that can move Si literature forward.

Our propositions demonstrate the usefulness of STS theory to SI literature in three ways. First, we extend Fawcett et al. (2012) to explain why behavioral constraints result from specific social processes in reaction to an SI initiative. We point to the importance of social positions and values within firms, as well as social associations and experiences between firms. In suggesting these root causes, STS theory explains why inhibitors emerge from a technical collaboration effort. Second, in being attentive to inevitable social responses to an SI initiative, we suggest technical designs that can aid the SI process. Technical elements of the SI design--that is, the centralities, requisites, proximities, and flows--each have a role to play. STS theory shows that the approach taken to SI should be carefully considered prior to the initiative. Third, environmental contingencies are proposed that prepare managers for the particular challenges they face in the SI process. SI literature must account for factors beyond the buyer-supplier dyad, and STS theory aids in that endeavor. In sum, the macro view presented in our propositions opens SI research to STS theory for explaining phenomena involving the social, technical, and environmental processes ever present in supply systems.

Theoretical Implications

This paper contributes to theory in multiple ways. In describing why behavioral constraints emerge from Si, we demonstrate how social attributes that are intimately linked to supply systems can inhibit SI. For instance as Proposition la implies, functions resist SI not because the initiative is new, but because their social position is threatened. Also as Proposition 2b suggests, conflicts emerge not because functions cannot get along, but because the SI process can change underlying values. Moreover, our propositions have theoretical value in suggesting a myriad of behaviors constraining SI beyond those observed by Fawcett et al. (2012). Based on STS theory, we suggest such behaviors as co-opting the routing of information (Pla), diverging abilities to communicate (P2a), basing decisions on social harmony (P3a), and failing to keep knowledge explicit (P4a). By highlighting social processes as root causes and by suggesting unexpected symptoms for constraints to SI, our research provides a novel perspective useful for SI.

Our research provides a perspective not common to supply chain research: Technical systems change social systems. Typically, literature argues that certain social attributes, like having the right culture, must be present before implementing technical systems (McIvor & McHugh, 2000). By contrast, the STS perspective of our propositions adds that technical systems are antecedent to social attributes. Because social attributes and processes are a root of behavioral constraints, SI research requires a deeper understanding of the specific technical elements that induce particular social processes. Each of our "b" propositions provides insights into these inter-relationships and gives suggestions for creating SI designs that account for social processes. We note that while we propose how social processes can be negative, social change may actually be positive. For instance, linking suppliers to high quality buyer processes may improve attention to detail in the supplying firm (Naveh & Erez, 2004). Recognizing that social systems change as a result of SI, the STS perspective opens SI research to explore positive and negative social implications of technical change.

Important insights are revealed in Proposition 5: Behavioral constraints to SI are influenced by the relevant environment. This is the open-system perspective upon, which STS is based (Pasmore et al., 1982). A partner firm's STS should be seen as a system adapted to survive in its unique environment. When firms seek to synchronize processes, any modifications to each other's STS could disrupt the adapted balance with the environment. Therefore, how dramatic the technical change will be or how free firms are in designing their SI system deeply depend on both firms' environmental characteristics. We see this in reports of buyers becoming familiar with the supplier's suppliers (Choi & Linton, 2011) or using predictive analysis of supplier bankruptcy because of environmental troubles (Harrington & O'Connor, 2009). Yet, more theoretical guidance is needed. Our Proposition 5 suggests to researchers and managers the environmental properties to assess and for which to prepare. In a global competitive environment increasingly linked through information and social networks, theories like STS will be increasingly useful.

Our advancement of STS theory contributes to contemporary discourse of theories that incorporate aspects of social capital and socialization. For instance, we offer a caution in that once a function like PSM attains high social capital (Bernardes, 2010), it may constrain initiatives that change its critical social position. Additionally, in contrast to literature that conceptualizes socialization in terms of shared understanding (Petersen et al., 2008), our theoretical development advances a much broader view of socialization. Drawing from core STS principles, we posit that social positions, values, associations, and experiences represent important attributes of social processes that affect shared understanding. Further, our conceptualization of STS challenges the sign and directionality of causal relationships between socialization and integration. Whereas Petersen et al. (2008) assert that the behavioral norms established through socialization facilitate higher levels of SI, our broader conceptualization of social systems provides theoretical rationale that supports a contrary view in which (1) SI causes changes to social processes and (2) these social processes may hinder SI.

Last, our assertions that social and technical attributes may inhibit SI efforts lends to a growing stream of research that extols the dark side of closer buyer--supplier relationships. This line of inquiry suggests that close supplier relationships do not always achieve their intended aims (Dyer & Hatch, 2006), nor are they achieved without substantive coordination cost or supply risk (Goffin, Lemke, & Szwejczew-ski, 2006). Such concerns refute the implicit notion that tighter buyer--supplier linkages are unconditionally advantageous. While such literature has highlighted that close social ties may also enable opportunism (Anderson & Jap, 2005) or inhibit technical innovations (Choi & Linton, 2011), little theoretical grounding is given as to how to optimize the degree of social relatedness. Drawing from STS theory, our propositions advance a rationale as to why a dark side emerges from the inherent, a priori technical designs of the SI initiative, and suggest technical designs to manage the level of relatedness between firms. In doing this, we guide evidence-based research into how to address the dark side of SI that literature has revealed.

Managerial Implications

Our propositions extend literature to provide a novel SI perspective for managers by showing how careful consideration is needed prior to engaging in a collaborative SI initiative. While Fawcett et al. (2012) suggest strong managerial commitment, changed mindsets, and thorough communication, our perspective complements this by suggesting SI designs prior to collaboration can prevent inhibiting forces from needing to be overcome. Our macro view shows that firms should assess where their STS differs from their partner firms before integrating to improve upfront SI designs that avoid problems. The practice of sending engineers to supplier locations long before recommending technical supplier changes (Liker & Choi, 2004) can provide opportunities to learn partner firm STS and environmental features. Our propositions provide the beginnings of a roadmap for such visits. Managers can assess how changes to technical centralities and requisites will affect within-firm social positions and associations, and how technical proximities and flow will affect between-firm social associations and experiences. By adopting a macro view of SI, in which integration is perceived as a joining of two distinct STSs, firms are better prepared to deal with the challenges of SI initiatives.

The SI design strategies suggested here have a dominant theme: Assure that technical designs attend to inevitable social processes. Because the STS perspective assumes that social and technical processes tightly couple, social change will follow technical change in some way; the concern is how. Our propositions provide the insight that if social change is inevitable then design should incorporate that knowledge. For instance, as Proposition 3b suggests, because between-firm social associations are expected and behavioral constraints are inevitable, designs should be made to manage the degree of relatedness. Literature is clear that people at work have needs and behaviors beyond simplistic models of rationality (Pasmore, 1988; Weick, 1995). Although inter-organizational and supply chain behavior research is still nascent, it is clear that supply chain practices affect and are affected by behavior. In our propositions, we advance managerial concepts and guidelines that enable direct acknowledgement and accommodation of socially induced behavior to ultimately achieve SI goals.

We advise managers to consider their partner's environment while planning SI initiatives. Whereas extant literature suggests the influential role of one's own environment in driving coordination behaviors (Ellis et al., 2011), managers receive little direction as to the constraining and enabling roles of partners' environments. Through Propositions 5a and 5b, we assert that equivocality and opportunity have opposing effects on the number and variety of technical designs that are feasible SI solutions for both the buyer and supplier. Our theory, then, suggests to managers that equivocal environments likely require more significant investments in social systems to avoid behavioral constraints to SI. In contrast, partnering with a firm that can marshal resources from external systems or actively change its environment is likely to diminish the need for such investments.

We point executives to the importance of social competence and STS learning. Regarding social competence, we see that managing perceptions of social position advances SI efforts. Threats associated with perceived loss of social position represent a formative rationale motivating employees to withdraw their SI support. Similarly, the emergence of between-firm social experiences, and the behavioral constraints that ensue, point to SI phenomena that cannot be ignored. Building social competence where managers are astutely aware of social conditions and future social changes is shown in our propositions to be of significant value. Walmart's supplier sustainability initiative provides a useful example (Plambeck & Denend, 2008) where an SI practice (i.e., sustainability) was successfully implemented because Walmart accounted for the sodal processes involved in persuading suppliers to implement the practice. Regarding STS learning, socio-technical principles advocate the importance of knowledge acquisition and assimilation for systematic adaptations (Pasmore et al., 1982). We demonstrate that the degree of socio-technical interrelatedness is quite high in the buyer--supplier dyad. Accordingly, our theory suggests that resources and strategic support are insufficient to assure SI goals (Bernardes, 2010). Rather, a learning orientation is required that advocates exploratory SI practices that cultivate knowledge of the dyad's STS landscape. Only through experimentation and continual learning can the particular STS processes in an SI initiative be known. While our presentation provides the general relationships to expect, SI advantages will be gained if managers learn and discover the specific STS challenges they face.

Last, are the traditional boundary-spanning functions like PSM truly prepared for the future as SI becomes common? PSM and sales/marketing are likely aware that SI will change their positions. In fact, literature suggests that a strong PSM social position is needed for firms to consider SI as a strategy (Paulraj & Chen, 2007). The STS perspective also suggests that as SI is implemented, PSM's social position will be threatened. Yet the macro view provided by the STS perspective suggests broader, facilitating purposes for boundary-spanning functions. The difficulty in unifying STSs is evident (Fawcett et al., 2008) and, thus, shows that new facilitating roles are needed. The STS perspective prepares an SI initiative a priori for these eventualities. Our propositions point to a number of skills typically not considered supply chain related. For example, a role for cross-functional facilitators between firms will be needed. This means an increasingly nonoperational role within the supply process, but rather a role of monitoring and consulting as to its ebbs and flows. Are the PSM and sales/marketing functions appropriately prepared for these roles? Additionally, are the traditional boundary-spanning functions able to monitor the economic landscape and competitive landscape of their partner firms? Are these functions ready to acquire the financial, engineering, and operational skill sets to support explorative activities, such as strategic cost analysis, multiechelon SCM, and CPFR, that facilitate STS adaptations? Clearly, if firms are destined to form higher levels of connectivity then many of these activities will be required, but are firms prepared to do so? These questions need to be considered carefully to derive full benefits from SI.

Future Research

Our STS-SI model can be empirically examined through various means, such as industry surveys or case analyses. For instance, survey data of cross-industry SI instances can test if STS reasons exist for why buying and supplying firms experience behavioral constraints. Most likely this would entail multilevel analysis because SI requires multiple functions nested within a buyer--supplier dyad, which are also nested with specific industrial environments. Scale development reflecting the key STS concepts is guided by our Tables 2-4. Measures could be developed to assess the degree of difference experienced before, during, and after an SI initiative, while seeking associations with the various behavioral constraints and SI technical designs suggested. Alternatively, case analyses guided by STS theory can investigate how specific STS concepts lead to or mitigate particular behavioral constraints, especially interesting would be the process of social value change and divergence brought on by discontinuous technical changes. Both of these empirical approaches may provide new insights into how social processes emerge during the SI process and empirically validate the utility of STS theory applied to the inter-organizational context.

Researchers can also employ alternate methods to examine how phenomena like social position threats affect SI efforts. In particular, researchers could design behavioral experiments using supply chain simulations (Eckerd & Bendoly, 2011) where study participants are assigned the various roles. Initially, participants would perform typical supply chain functions--managing material replenishments, corn municating requirements, coordinating supplier involvement--and different treatments of social position would be given within the simulation. Then, an SI initiative would begin where the participant is allowed to choose different degrees of involvement in the supply process. Moreover, researchers could experiment with different approaches toward SI, modifying the Table 4 characteristics of the environment. Finally, before-and-after questionnaires would measure the presence of various STS components, the perceptions of participants, and behavioral constraints observed. Using experimental methodologies, STS can be a fertile perspective for such behavioral work.

As organizational boundaries blur, theories used to describe intra-organizational processes will become more applicable in future research. While our paper extends STS theory to the inter-organizational context, other SCM research has similarly carried out so with theories such as dynamic capabilities (Fawcett, Wallin, Allred, Fawcett, & Magnan, 2010) and resource-based perspectives (Page11, Wu, & Wasserman, 2010). Applying new theories to SI expands, but also complicates what researchers and managers need to consider. Eventually, just as STS theory has developed a coherent set of normative STS principles, future research should similarly update a set of SI principles--a repository of evidence-based practices. In addition, questions will need to be answered like how costly is social change to a firm? When should technical designs be compromised for social benefit? Seiler (1967) emphasizes that true system understanding requires investigating the trade-offs within an STS. Although incorporating social goals into technical designs makes SI more challenging, STS theory provides the detailed, systemic view that future SI researchers can use to describe and improve supply chain processes.


While our theory offers novel insights into SI phenomena, there are several limitations to our study. As with any conceptual work, data will be needed to test our propositions. Our suggestions for future research help move research in that direction. We also note that our theory is limited to the buyer--supplier dyad and does not address other STS processes that induce constraints across three or more firms. Our theory focuses on issues germane to the SI process, not with issues long after SI has occurred. This is another limitation because quite often disruptions occur in stable, integrated supply systems with important effects on competitive performance. Last, we note that STS theory is inherently limited to influences across social, technical, and environmental systems, not within these systems themselves. Such a limitation misses important within-system factors that just as likely can constrain Si.

Despite these limitations, our research provides a robust platform for the advancement of STS theory within the context of SI. In extending Fawcett et al. (2012) and echoing behavioral supply chain research (Siemsen, 2011), we assert that technical systems do not exist in isolation; rather, technical systems are c_losely coupled with social systems that are jointly embedded in the environment. Hence, SI problems require researchers and managers to consider the more systemic causes for the variations observed. This paper provides a means for such consideration by extending STS theory from an intra- to an inter-organizational context. Through presenting STS theory's pertinent tenets as related to Si, we provide a framework explaining and preventing behavioral constraints to Si. Our STS-based propositions will help managers prepare and design more effective SI initiatives. When Emery and Trist developed STS fifty years ago, businesses were significantly less interconnected: Earlier work examined the social and technical systems within a given organization. As organizational boundaries blur, STS theory becomes relevant when examining systems spanning multiple firms. Research has shown the usefulness of STS theory within organizations; future research will show how supply chains can benefit from STS theory.


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(1.) Some theorists incorporate organizational structure as a fourth subsystem (Seiler, 1967), but the broad definition of technology would include organizational structure (a.k.a. organizational design) because designs are for organizational purposes.


Arizona State University


University of Kentucky


Michigan State University

Thomas J. Kull (Ph.D., Michigan State University) is an assistant professor of supply chain management in the W. P. Carey School of Business at Arizona State University in Tempe, Arizona. His primary research interests are behavioral issues in operations and supply chain management, and in supply chain risk issues. Dr. Kull has published his work in a variety of journals including the Journal of Operations Management, Decision Sciences, IEEE Transactions on Engineering Management, and the European Journal of Operational Research.

Scott C. Ellis (Ph.D., State University of New York/Buffalo) is an assistant professor of supply chain management in the Gatton College of Business & Economics at the University of Kentucky in Lexington, Kentucky. His research interests center on the study of purchasing and supply management processes and functions, with particular emphasis on the application of behavioral and economic theories to the study of risk, innovation transfer, and collaboration within buyer-supplier relationships. Dr. Ellis' work has appeared in the Journal of Operations Management, Decision Sciences and the Journal of Supply Chain Management, among other outlets.

Ram Narasimhan (Ph.D., University of Minnesota) is the John H. McConnell Endowed Professor of Business Administration, and a University Distinguished Professor, at the Eli Broad Graduate School of Management at Michigan State University in East Lansing, Michigan. His research focuses on strategic issues in supply chain management, including strategic sourcing, integration across supply chains, buyer-supplier relationships, empirical modeling of decision linkages in supply chain management, and analytical modeling of supply chain decisions. Dr. Narasimhan's most recently published articles have appeared in Decision Sciences, the Journal of Operations Management, the International Journal of Production Economics, Supply Chain Management Review, and Mathematical and Computer Modeling.
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Author:Kull, Thomas J.; Ellis, Scott C.; Narasimhan, Ram
Publication:Journal of Supply Chain Management
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Date:Jan 1, 2013
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