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Linking supply chain network complexity to interdependence and risk-assessment: scale development and empirical investigation.

Introduction

As a buyer firm plans of strategizing its relationship with its major suppliers, a plethora of vital questions peep in the minds of the decision making managers: what are the pros and cons involved? How complex is the network environment? What will be the level of dependence? What are the alternatives? What is the risk involved? While searching for answers to these frequently asked questions, often one word keeps flashing in mind, 'network complexity' The word "network complexity" nowadays has become a very common vocabulary in supply chain parlance and frequently heard in managerial discussions on network issues. It has been highlighted as a major breeding ground of supply chain issues and conflicting network wide partner relationships (Gilmore 2008; Bode, Wagner 2015). One of the most vital and omnipresent stumbling blocks that stares glaringly at the managers before such decision making is, how complex is the system or the process or the network in the question? Thus, network complexity has become inherent to today's business scenarios, which cannot be avoided; rather understanding the complexity involved and quantifying it becomes vital for making effective and viable decisions aimed at managing complexity. In modern business setting the competition for competitive advantage and excellence has progressed to the very next level where the competition is no longer between firms, rather between their respective supply chains. However, this does not undermine the fact that complexity and conflicting interest mark most of the supply chain network relationships. Thus the challenges that the supply chain managers face these days are also unique and are actually a mix of three streams of highly sensitive balancing blocks which the managers should carefully balance without underestimating any of them. Those three stumbling blocks that managers face and need to manage and balance are: complexity management, risk assessment and last but not the least understanding the interdependence of their own firm's processes on their partner firm's processes. However the understanding of network complexity, here after interchangeably also referred to as supply chain complexity or in a even generic sense supply chain network complexity, needs revisiting for a better clarity. Thus this paper aims at first establishing a rational framework linking those three stumbling blocks that managers face in course of their day to day supply chain management duties and then progresses to empirically validate the hypothesized relationships based on perceptions of the industry practitioners (i.e. the managers); thereby aiming at providing supply chain managers with a handy empirically proved understanding framework indicating how perception about supply chain network complexity impacts inter-dependence and risk assessment between partners in the network.

Extant literature highlights that complexity in the supply chain network is critical for understanding network transactions and relationships. Studies highlight that complexity impacts performance of the chain and also that of the individual network actors (Bode, Wagner 2015; Choi, Krause 2006; Waldrop 1992). Though, different studies have defined complexity from different perspectives, a clear and easily comprehendible working definition of supply chain network complexity remains lacking. So arises the need to propose an acceptable working definition and also develop a scale dealing with supply chain (SC) network complexity (Manuj, Sahin 2011; Milgate 2001). Extant studies took the view of SC network complexity as multi-dimensional and multi-faceted phenomenon affected by varied sources and described complexity from different perspectives: number of elements and subsystems (Bozarth et al. 2009; Manuj, Sahin 2011; Choi, Krause 2006; Choi, Hong 2002; Handfield, Nichols 1999) and high number of elements affecting the complexity and the supply chain network functioning in terms of disruptions (Chopra, Sodhi 2014; Bode, Wagner 2015); quality and nature of relationship (Lian, Laing 2004; Ferlie, Pettigrew 1996); Inter-relationship between network elements (Choi, Krause 2006; Wycisk et al. 2008; Johnsson et al. 2007); frequency of interaction (VanVactor 2011; Noorderhaven, Harzing 2009); degree of differentiation (Choi, Krause 2006; Burt, Doyle 1993), etc.

Few papers which particularly focus at different aspects of complexity are: Milgate (2001) describing supply related complexity from uncertainty, technological intricacy and organizational systems perspective; Choi, Krause (2006) from number of suppliers, differentiation among suppliers and relationship among suppliers perspective; Bozarth et al. (2009) discusses detailed and dynamic complexity while other contemporary papers have highlighted the multifacets of quality of relationship, frequency and volume of interaction, number of entities, degree of differentiation and inter-relationship among entities while discussing complexity in healthcare perspective. Thus although much attention has been drawn, what lacks, is a unified definition of supply chain network complexity, a convenient consolidation of the complexity aspects. Even more importantly, the conceptualization of a supply chain network complexity construct and a multi-item scale for measuring network complexity remain wanting. Following Gilliam and Voss's (2013) procedure for construct definition development based on past literature, the definition for the constructs are developed in this study. Based on extant literature the preliminary definitions are developed and based on the definitions, the measurement items are generated, which are in turn subjected to judging by experts and professionals and also subjected to validity testing, finally followed by exploratory factor analysis the details of which are mentioned in methodology section; thereby aiming at reducing vagueness and ambiguity. In this study supply chain network complexity (SCNC) is defined as the extent of inter-relationship among the supply chain network partners/actors, their degree of differentiation in terms of practices, their frequency of interaction and their volume (i.e. numbers) in network (Bode, Wagner 2015; Bozarth et al. 2009; Handfield, Nichols 1999; Simon 1962; Choi, Krause 2006; VanVactor 2011; Noorderhaven, Harzing 2009; Milgate 2001; Prater et al. 2001; Meepetchdee, Shah 2007; Johnsson et al. 2007; Burt, Doyle 1993). In this study SCNC is investigated as an antecedent to two vital relationship and behavioral aspects of the network partners: interdependence (Kumar et al. 1995; Vijayasarathy 2010) and risk assessment (Tummala, Schoenherr 2011; Ha et al. 2011; Wagner, Bode 2006; Ellis et al. 2010). Interdependence and risk assessment are the two variables being introduced in the study as consequence of SCNC. Figure 1 shows the conceptual model. The rationale behind introducing these two constructs can be linked to theoretical underpinnings and key extant literature. Like most studies which have their basis deeply ingrained in the theoretical underpinnings, this study draws its inspiration from two related theories: relational exchange theory (Dyer, Singh 1998) and bilateral deterrence theory (Bacharach, Lawler 1981). Relational theories sees firms as social entities and thus highlights the vital influence or linkage between social constructs like trust, commitment, power, conflict, risk assessment, network complexity etc having controlling influence on interactions and exchanges. Dyer and Singh's (1998) relational view theory suggests that firm's critical resources can span firm boundaries and get embedded in inter-firm processes(routines) and interaction; thereby shifting the focus of prime interest to 'relationships'. Relational exchange theory highlights that cooperation, communication and trust plays pivotal role in exchanges and even at times surpasses formal contracts. In the study, the objective is to analyze the perceptual measures to understand how they affect the transactional norms. Many of those norms that are mentioned, gets affected by the different aspects of the complexity variable as discussed earlier and hence finds rational linkages, needing deeper probing. Herein comes the relevance of the theory can be linked. Another theory which can be indirectly linked is the bilateral-deterrence theory (Bacharach, Lawler 1981; Lawler, Bacharach 1987) which in a nut-shell suggests that one network entity's desire to engage in conflict depends largely on its understanding about the retaliation from the entities with which it involves in conflict and also the available alternatives it has, since retaliation poses a greater threat. Thus interdependence is an important measure for understanding each other's position and retaliation objectives. Greater the measure value, lesser should be the fear of conflict occurrence. As per the definition of the SCNC and the extant literature, complexity of the network entails the nature of bonding between entities, their number and inter-relationship ship is bound to affect the interdependence and also the eagerness for ensuring risk assessment in their transaction relationship. Extant literature highlights that interdependence involves how the network transaction partners perceive their dependence on each other and hence essentially involves both the buyer and supplier side (Vijayasarathy 2010; Kumar et al. 1995). This interdependence may be symmetric where both parties have equal dependence or asymmetric i.e. unequal level of dependency. In this study, interdependence is defined as the extent to which the supply chain network partners/ actors depend on each other and are unable to replace each other for their transaction relationship (Vij ayasarathy 2010; Kumar et al. 1995). Vijayasarathy (2010) gave an effective way of measuring interdependence using a summated scale involving respondent dependence and supplier dependence which also tackled the skewed dependence aspect by subtracting the absolute difference between the two dependence variables. This study adapts similar measures with modifications which are discussed in details in the measures section.

Risk assessment literature is in existence since a long time and extant conceptual research works highlights its relevance and importance in management context involving transactions and inter-actor relationships (Ha et al. 2011; Wagner, Bode 2006). Risk assessment has been portrayed as a vital building block of interaction among the network actors (Prahalad, Ramaswamy 2004). Different studies have discussed about risk in different perspectives. Ellis et al. (2010) discussed and empirically examined the link between supply disruption probability and magnitude with that of overall supply disruption risk. Using a single item scale for disruption risk they highlighted that a positive relationship existed between magnitude of disruption and probability of disruption with overall disruption risk. Further in a recent study Bode and Wagner (2015) linked upstream supply chain complexity drivers and supply chain disruption. Blackhurst et al. (2008) in their conceptual risk framework for supplier risk assessment indicated that risk as a whole is very generic and can be segregated as risk identification, assessment, decision-making and monitoring. Prahalad and Ramaswamy (2004) mentioned that risk assessment is one of the most vital aspects that decides the course of transaction relationships and is thus an integral building block of network-wide actor to actor relationship. In complex business scenarios, especially where options of switching between actors is high, where often frequency of interaction varies, and where the network entities remain inter-connected, assessment of risk becomes most vital because the strategies like transferring risk, taking risk, eliminating risk, reducing risk, etc that are implemented follow the risk assessment stage (Hallikas et al. 2004). The need for purchasing organizations to have superior risk assessment platform and techniques in place for superior business relationship with network actors have been highlighted by contemporary studies (Zsidin et al. 2004; Harland et al. 2003). However risk assessment literature lacks empirical support and also most studies are conceptual framework driven and mostly prescriptive in nature. Established scales, giving a fair understanding about risk assessment remain wanting. This study aims at plugging this gap and proposes a multi-item risk assessment scale and operationalizes that. Based on extant literature, in this study risk assessment (RAS) is defined as the extent to which the supply chain network partners/actors can make informed decisions by adequately assessing the stakes involved in their transaction relationship with network partners (Prahalad, Ramaswamy 2004; Tummala, Schoenherr 2011; Ha et al. 2011; Wagner, Bode 2006; Ellis et al. 2010).

1. Hypothesis development

In this study it is postulated that supply chain network complexity increases the extent of interdependence among the network partners/actors and also enhances the extent of risk assessment among the transacting network actors. The study also postulates that higher interdependence in turn should also foster greater extent of risk assessment among the network partners.

1.1. Supply chain network complexity and interdependence

Network Complexity is defined in terms of number of transacting actors, extent of inter-relationship among actors, degree of differentiation among them in terms of practices and also their frequency of interaction (Choi, Krause 2006; VanVactor 2011). Interdependence signifies the level of dependence of the buyer firm on the supplier firm and vice versa in terms of alternatives, ease of shifting from one set of partners to another, the cost involved etc. Usually when the number of actors on both fronts i.e. buyer and sellers are high which extant research characterizes as a complex network criteria, there remains a fear that the buyer or the seller can easily switch and hence they try to bind each other through firm specific investments aiming at increasing each other's onus in the relationship. Also in situation when the actors in the network are linked or related to one another closely then in case of a break-up in the transaction relationship, finding an alternative becomes difficult and this fosters moving into a state of higher dependence from both sides to secure such fallout. In case the degree of differentiation in practices is severely disparate the situation is complex as the one network actor cannot easily go in sync with the other partner with ease and hence through investments or contracts or binding systems try to enhance each other's dependence. When the interaction frequency is less, there remains scope of ambiguity and this leads to a complex situation and under this circumstances also fall out may occur. Thus when the supply chain network complexity is higher in order to secure future fallout in the transaction relationship, which might add high economic burden or implications, the network partners try to move into a stable relationship state which is marked by higher levels of interdependence. So it is hypothesized that:

H1. Supply chain network complexity has a positive effect on partner interdependence.

1.2. Supply chain network complexity and risk assessment

In the presence of complex network relationships an overall environment of uncertainty prevails where the network actors are often confused to open up and do business and often deter themselves from cooperating each other and try to play safe. Risk assessments (Prahalad, Ramaswamy 2004; Blackhurst et al. 2008; Ellis et al. 2010) plays a pivotal role in deciding the course of action for the downstream activities and concerns about informed decision making so as to completely understand the stakes involved in the transaction relationship. In scenarios of supply chain network complexity being on the higher side, the inherent uncertainty in the system remains at a higher level and gives more importance to even thorough understanding about the risks involved and assessing the consequences in the case of relationship catastrophe. Thus increased complexity levels should foster increased onus of risk assessment. So it is hypothesized that:

H2. Supply chain network complexity has a positive effect on partner risk assessment.

1.3. Interdependence and risk assessment

Informed decision making is the key aspect of risk assessment. The salient attributes that characterize risk assessment are making informed decisions about the transactions (i.e. the partners are aware about the details of the decision they are making), understanding the stakes involved in the relationship (what to gain and what might be lost), remaining aware about the implications/outcomes of the ensuing transaction relationship (i.e. aware about the expected and worst case unexpected outcomes) and most importantly understanding the responsibilities/liabilities involved with the transaction relationship. Ellis et al. (2010) highlighted that perceptual assessment of risk guides decision making. As the notion of loss is inherent in risk, understanding of vulnerability is critical in deciding the course of relationship as it gives a feeling of being exposed while including an element of uncertainty or risk (Handfield, Bechtel 2002; 2004). In the presence of high level of interdependence, both the transacting partners are at higher risk of incurring losses in case they fallout and hence often enter long-term binding contracts (Casciaro, Piskorski 2005), which necessitates a deeper understanding about what is at stake. Thus higher level of interdependence should foster higher degree of risk assessment as they need to understand the level of vulnerability/risk involved in the relationship. But when a relationship is marked by high degree of interdependence and the fallout of a relationship breakup might be catastrophic for both partners, the urgency of risk assessment increases since both are aware about the cost of breakup, keeping chance of opportunism remains low (Kumar et al. 1995; Wagner, Bode 2006). So, it is hypothesized that:

H3. Partner interdependence has a positive effect on partner risk assessment in supply chain network.

2. Methodology

The current research is aimed at deeply understanding the supply chain network complexity (SCNC) and risk-assessment (RAS) constructs and thereafter developing relevant scales for detailed empirical study. In the process, a survey was prepared using two new scales and another existing multi-item scale. There was the need for developing and using the new scales for SCNC and RAS, in the absence of relevant multi-item scales. For the inter-dependence construct, despite a pre-existing scale was identified as mutual dependence (Vijayasarathy 2010); it was further revalidated in the course of the study for better adaptability to the sector-context and country-context. The methodology steps followed are as follows.

2.1. Pretesting and scale development

In this study, Churchill's method for developing and testing reflective scales was followed (Churchill 1979). The entire method can be segregated into four broad steps: first, construct development followed by checking its content and face validity; second, dimensionality testing; third, checking for internal consistency; fourth, checking and thus ensuring that convergent, discriminant, and nomological validity of the measures are fulfilled (Anderson, Gerbing 1988; Churchill 1979). In the first stage, based on the literature and existing definitions, pools of items were generated through thorough probing of the literature for the two constructs. After the successful generation of the initial pool, a substantive validity test was carried out followed by scale purification (Anderson, Gerbing 1991).

10 items for SCNC and 9 items for risk assessment were there in the initial pool which was given to two senior professors from the faculty of management of two reputed Indian business-schools and four industry experts from well known firms. Based on their feedbacks, necessary modifications were done to the initial pool items. This pool was given to 68 industry experts in the pretesting stage. Following Lawshe (1975), a substantive validity testing was carried out based on those expert responses. Such techniques had been used in another contemporary study for newly developed construct-scale validation (Ambulkar et al. 2015).

As part of the validity testing, the experts were asked to rate if the items were essential or non-essential in the construct and study contexts and were asked to give their responses (satisfied/dissatisfied). The coefficient ([C.sub.sv]) for measuring substantive validity was calculated as [C.sub.sv] = ([n.sub.c] - [n.sub.0])/N (Lawshe 1975; Anderson, Gerbing 1991) where nc represented number of respondents who assigned the items as essential and satisfied, which [n.sub.0] indicated respondents who marked the item as non-essential. In this way the [C.sub.sv] value varied between -1 to +1. Based on the highest and most appropriate values the items were chosen. Next, in consultation with 2 academic researchers who knew about the aforesaid technique, finally 4 items were chosen for SCNC and RAS constructs each. In the following steps the reliability and validity testing were done. Using SPSS software, the two constructs, based on the collected 68 responses in the pilot stage, were separately subjected to exploratory factor analysis and the results gave clear single factors explaining 73% of the variance in case of SCNC and 70% in case of RAS. The Keyser-Meyer-Oklin (KMO) indicating sample adequacy was 0.80 and 0.76 respectively. Also the chi-sq values were significant for both indicating single-factor solution to be significant. Convergent validity and reliability were also established from the cronbach alpha values of 0.81 and 0.87 respectively for SCNC and RAS and the range of factor loadings were all above 0.6. The discriminant validity was also checked using chi-square difference test (Stratman, Roth 2002). In the first confirmatory factor analysis (CFA), the two latent constructs were allowed to freely correlate and in the second CFA they were constrained to one. The difference in the chi-square between the unconstrained and constrained models involving the SCNC and RAS construct pair was calculated and it emerged significant, establishing discriminant validity. The predictive validity was established during the course of large scale survey study. It was established for the RAS construct, however for the SCNC construct it could not be established as it lacked an antecedent variable in the study conceptualization. However the nomological validity for both the studies were established as the final questionnaire before sending for survey was given to 10 new experts and 2 other academicians (different from those involved before) who agreed with the conceptualization of the constructs.

2.2. Sample and data collection for final study

The survey was distributed to professionals holding important positions and having responsible designations in the supply chain of reputed Indian firms belonging to 7 different sectors like manufacturing, retail, food processing, telecommunication, leather, textiles and chemical. However, this choice of sectors was based on a leading Indian business magazine's report for the financial year ending March 31st, 2014, where they highlighted these 7 sectors as top seven performing sectors. The database for the survey was obtained from two sources; first, a professional membership list of a regional supply chain professional body (the list was purchased from a market survey firm); second, list of alumni of two reputed Indian b-schools (obtained from known sources to the author), who graduated between 2000 and 2012 and had their alumni profile updated in their respective institutes with roles which fall within the generic purview of supply chain management. The survey was conducted using online mode only and the survey was hosted by using online platform. The respondents were sent online survey links along with a forwarding email explaining about the survey in details, its purpose and also the mandatory disclaimer. First, all the three databases were combined and subjected to a careful sorting exercise, looking for supply chain related roles and a consolidated list of 1914 unique individuals was generated. But out of those in the list, 120 entries had missing email IDs. Second, the online survey was administered to 1794 potential respondents, of which 64 emails had delivery failure issues and the respondents were not reachable through any other means, bringing the final effective distributed survey questionnaire number to 1730. Out of the 1730 potential respondents, 203 completed surveys were obtained at a response rate of 11.73% which is quite acceptable as per online survey response rate standards (Ambulkar et al. 2015). Table 1 provides the demographic details of the respondents. Majority of the respondents were Supply chain managers (53.7%) and most respondents were from manufacturing (38.4%) and retail (21.2%) sectors. The obtained responses can be considered to be quite representative keeping into consideration that the final consolidated list was prepared by combining the three databases giving specific attention to the roles and the original consolidated list had 49% names with roles as supply chain managers /executives /specialists, nearly 30% as vice-president/director/partner, around 17% as Sr. Executive/Buyer/Analyst, while the rest fell in the category of logistic managers. The respondents' profile of the final survey indicated proportionate responses which were within +/-5% of that of the original list. Again, looked from the sector perspective, the manufacturing sector accounted for around 41% of respondents in the original consolidated list, whereas textile and leather industry accounted for 3% each. In the final survey, the distributions of the respondents were acceptably proportionate because manufacturing sector accounted for 38% while textile and leather accounted for around 2% each. Thus the obtained response and the sample can well be generalized as representative.

However one important note should be made when studying supply chain professionals in the Indian context, especially their sectors and job-roles. In India manufacturing, retail, telecommunication, chemicals, etc. account for lion's share of the organized industrial sector, whereas very few leather and textile firms belong to that category which can be considered under organized sector. Now, that all the databases represented executives and managers who were either affiliated to a reputed professional body or graduated from two reputed b-schools, it can be rationally assumed that most of them went into organized sectors and very few got opportunities to venture into textile and leather industry as those firms recruit very few people from established b-school campuses (though exceptions are there and this comment should not be always generalized). Thus keeping all these factors into consideration, while understanding the study's sample profile and hence its possible scope of generalization will help managers and academicians in analyzing the study outcomes.

The respondents came from firms having sales figures as high as well above 100 million USD (converted @ 1 USD = 60 INR) and also those below 10 million USD. The experience level of the respondents also varied and most respondents (63%) belonged to between 5 years to 10 years experience band. A reminder email was sent to the respondents for bettering the response rate. Thus two waves of responses were received (before and after the reminder). Non-response bias testing was performed comparing the early responses (before reminder) with the later responses (after reminder) (Armstrong, Overton 1977; Ambulkar et al. 2015). Chi-square test results showed no significant difference between the first-wave and second wave along two categories of firm size by number of employees and firm revenue at level of 0.1; thereby assuring an unbiased sample.

Common method bias was also checked using Harman's single factor test (Harman 1976). In the current study the largest variance explained by a single factor was 32.17% which is not the majority of the total variance. Also further absence of common method bias was checked following latent factor test (Podsakoff et al. 2003). With introduction of a latent factor to the main model, no significant loss in the factor loadings was observed, indicating minimal common method bias in the current study.

2.3. Measures and data analyses

There are three overall research variables in the model: Supply Chain Network Complexity (SCNC), Risk Assessment (RAS) and Interdependence (ID). Supply Chain Network Complexity has been operationalized using four items, measured on a seven-point Likert scale (1 = strongly disagree and 7 = strongly agree). The items described different aspects of SCNC, varying levels of which should represent varying levels of network complexity. The items that characterized SCNC: high number of first tier suppliers, strong inter-relationship relationship among the tier-one suppliers, high degree of differentiation in terms of levels of operational practices among the actors, and Low frequency and the volume of interaction among the actors (Bode, Wagner 2015; Bozarth et al. 2009; Handfield, Nichols 1999; Simon 1962; Choi, Krause 2006; VanVactor 2011; Noorderhaven, Harzing 2009; Milgate 2001; Prater et al. 2001; Meepetchdee, Shah 2007; Johnsson et al. 2007; Burt, Doyle 1993).

Risk Assessment too has been operationalized using four items, measured on a seven-point Likert scale (1 = strongly disagree and 7 = strongly agree). The items characterizing RAS were: ability to make informed decisions while transacting, ability to understand the stakes i.e. risk involved in the relationship, awareness about the implications of the transaction relationship, and understanding the responsibilities/liabilities involved in the transaction relationship (Prahalad, Ramaswamy 2004; Tummala, Schoenherr 2011; Ha et al. 2011; Wagner, Bode 2006; Ellis et al. 2010; Zsidin et al. 2004; Harland et al. 2003; Blackhurst et al. 2008).

Interdependence scale measure was adapted with necessary modifications from Vijayasarathy (2010) where it was referred to as mutual dependence. However as the entire scale was being tested in a completely different context, the same steps were repeated. Totally six items represented buyer dependence (BD) and supplier dependence (SD). In order to ensure that the respondents had a similar understanding of the two different constructs as in Vijayasarathy (2010) study, the responses for the six items were subj ected to principal component analysis (factor analysis) using varimax rotation. The results supported a clear two-factor structure explaining 67.3% of the variance and had eigen values above unity. The factor structure is provided in table 2 and all the six items show good loadings (above 0.6) and all cross-loadings were below 0.2. Also the reliability was established by calculating Cronbach alpha which came to 0.76 and 0.81 for BD and SD respectively. The complete wording of the scale items are provided in the appendix section.

The validity and reliability of the scales were established, following Vijayasarathy (2010) and Casciaro and Piskorski (2005), but with certain key modifications to suit the study requirements, interdependence (ID) was calculated as follows: ID = [(BD + SD) - Absolute (BD-SD)]/2. This division by 2 was necessary to bring the ID items to the same 7-points scale being used for the rest of the variables. Uniqueness of this ID variable measures happen to be that, all the three interdependence items were calculated separately corresponding to the BD and SD item scores and also the ID score was adjusted for the skewed dependencies (by subtracting the absolute differences between BD and SD) to capture appropriate magnitude of it. Subsequently again the SCNC, RAS and ID items were subj ected to exploratory factor analysis (EFA) to ensure that the factor structures hold well.

The EFA results involving the perception measures for SCNC, RAS and ID items, indicated an acceptable KMO value of 0.868 and also the Bartlett's coefficient got significant at 0.1%. Three well laid and rotated factor structures emerged from the principal component analysis results using varimax rotation. The loadings were all good (above 0.6) and most importantly all the three variables (SCNC, RAS and ID) showed satisfactory Cronbach alpha values above 0.8. The complete results are provided in Table 3.

2.4. The measurement model

After successful EFA analysis, confirmatory factor analysis (CFA) was carried out to assess the reliability, validity and dimensionality of the constructs. The CFA results indicated acceptable values for CFI (comparative fit index): 0.981; TLI (Tucker-Lewis Index): 0.976; GFI (Goodness of Fit Index): 0.882 (close to 0.9); IFI (Incremental Fit Index): 0.978; [CFI, GFI, IFI and TLI should be near 0.9 and above (Anderson, Gerbing, 1988)]; CMIN/df i.e. [chi square]/d.f. = 1.69 (preferably should be below 2) and RMSEA (root mean square error of approximation): 0.027 (preferably below 0.05) (Anderson, Gerbing 1988; Hu, Bentler 1999). Table 4 shows the CFA results. The measurement items are provided in Appendix. Factor loadings, composite reliabilities (CR), average variance extracted (AVE) and squared multiple correlation (SMC) were examined to assess convergent validity. All the factor loadings came out to be above 0.6 and significant at p < 0.001, suggestive of high levels of convergence (Hair et al. 2010). Composite reliability of all three factors came out to be greater than 0.7, further supporting convergent validity and internal consistency (Hair et al. 2010). The AVEs for all the three constructs were each greater than 0.5, supporting convergent validity. Details of the measurement model have been provided in Table 4. The AVEs for each of the constructs also sufficiently exceeded the squared correlations with the other constructs, indicating support for discriminant validity (Table 5).

3. Analysis and results

3.1. The structural model

In this study structural equation modeling (SEM) using Amos was used to test the hypothesized relationships, indicated in the study model (Fig. 1). The model fit indices for the structural model appears acceptable and satisfying as per the acceptable practices (Anderson, Gerbing 1998; Hu, Bentler 1999; Iacobucci 2010). The SEM results yielded acceptable fit statistics: [chi square]/d.f = 1.44; CFI = 0.947; IFI = 0.956; TLI = 0.942; GFI = 0.863; RMSEA = 0.043.

Amos output indicated that the standardized path coefficients between supply chain network complexity and interdependence (c = 0.229, p < 0.001) was highly significant supporting hypothesis 1 (H1); between supply chain network complexity and risk assessment (c = 0.029) was found to be insignificant rejecting hypothesis 2 (H2); between interdependence and risk assessment (c = 0.231, p < 0.001) was highly significant too, thereby supporting hypothesis 3 (H3). Please refer to Figure 2. The details of the structural model output are provided in Table 6.

The study findings thus supported H1, that greater SCNC was positively linked with greater levels of interdependence and again it was found that higher interdependence between partners in a supply network led to greater support for higher degrees of risk assessment, supporting H3. However the non-significance of the path linking SCNC and RAS refuted H2 and threw up findings contrary to rational understanding. Thus the outcomes indicated an indirect effect of network complexity on risk assessment through interdependence.

3.2. Discussion and implications

This study presents a clear definition of SCNC and RAS including operationalizing of the constructs. The newly developed 4 item scale for SCNC examines the impact of network complexity on interdependence and risk assessment constructs. As noted from the extant literature, complexity happens to be a multi-faceted aspect and hence its presence and perception needed a suitable instrument. This study attempted at bridging that long awaited gap by providing a handy scale for measuring network complexity and empirically examining the impact of SCNC on the transaction relationship variables of interdependence and risk assessment. The 4 item risk assessment scale also comes in handy and helps in quantification of the long discussed conceptual risk assessment construct. Network complexity as discussed in this study indicates a business environment which is marked by presence of high number of suppliers and hence the inter-relationship and frequency of interaction between the network actors becomes critical. When the complexity is high then the chance of shifting or switching actors poses a threat because the actors tend to remain inter-related. The cost involved in switching may become high for both the supplier and the buyer. Moreover the fear remains about the fact that the switching partner may involve in spilling over the crucial key information when moving over to other network actor for transaction and also it entails cost.

3.3. Managerial and academic implication

The findings from this study should be of immense importance to the business community and especially the managers in understanding network complexity and its impact on the relationship and transaction between actors in a supply chain networks. This study discusses and develops new multi-item scales to measure network complexity and risk assessment constructs, both of which existed as conceptual constructs in the extant literature and were lacked a direct measurement instrument. Often these were measured by other reflective measures and their discussions remained at abstract levels without detailed quantification. This study also validated the measures for interdependence scale and adapted it, so as to use it with other Likert scales in similar studies. Previous studies were predominantly conceptual and lacked quantification. This will help the managers in quantifying their business network complexity and in the process aid in decision making. This research highlights that contrary to rational understanding, managers are more concerned about the level of dependence before probing for assessing the risk rather than just the network complexity. From academic perspective, this study opens up new direction of management research in the field of network complexity and marks the shift from predominantly conceptual studies to those of empirical investigations.

Conclusions

The aim of this study was to understand how firms perceive network complexity in the supply chain and explore how network complexity subsequently impacts the understanding of interdependence and risk assessment in the transaction relationship. The findings from this paper help in establishing a rational framework linking three stumbling blocks that managers face in course of executing their day to day supply chain management duties. This study empirically validates the hypothesized relationships; thereby providing supply chain managers with a handy empirically proved understanding framework. This study extends the extant literature, develops and operationalizes two constructs called supply chain network complexity and risk assessment, besides validating the interdependence construct in its new usable form derived from the summated composite score of two constructs: supplier dependence and buyer dependence. This study also examined the relationship between the constructs and demonstrated that network complexity has a positive and direct impact on interdependence, which in turn directly and positive affects risk assessment; however supply chain network complexity directly do not influence the risk assessment aspect in a firm network. The studies contribution lies in providing a consolidated definition of Supply chain network complexity and development of SCNC and risk assessment scales besides revalidating the interdependence construct and its scale elements. This study will open newer horizon of supply chain management research and add to its body of knowledge. It can be viewed as a unique attempt to link three somewhat rationally related but empirically unlinked streams of literature.

As with all social science research, there are some limitations of this study too. First, the study took into consideration the perceptions of the managers without segregating them as per the complexity levels of the network they belong to. All the respondents may not have given their responses with similar network complexity into consideration. Second, the study used cross-sectional data, which limits the study's ability to draw causal linkage, especially because interdependence and complexity may have some effects which lingers over time and might reveal newer aspects when looked over time. Third, the study considered mostly respondents from non-service sectors and service sector networks were grossly ignored. Therefore, future studies may segregate network complexity into groups depending on different level of complexity and study that impact on the consequent variables. Also the study findings may suffer in terms of limitation of generalization, especially to service sector scenarios. Future probing can be done keeping the service sector into consideration. Finally the supply chain network complexity construct is nascent and may be extended to take a more generic sense and also explore firm relationships involving dyadic relationships which might throw up different yet interesting findings. Moreover due to lacking in the extended conceptualization of what can be antecedents to the SCNC construct, all validity tests were done expect predictive validity testing. Thus there remains a scope of introducing a rational antecedents and then trying to establish the predictive validity.

Samyadip CHAKRABORTY

Department of Operations and IT, ICFAI Business School (IBS) Hyderabad, IFHE University, Dontanapalli, Shankarpally Road, Hyderabad - 501504, Telangana State, India

E-mail: samyadip@ibsindia.org

Received 08 December 2014; accepted 25 May 2015

http://dx.doi.org/10.3846/btp.2015.620

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APPENDIX

Construct                     Scale items used in
                                the large-scale
                                     study

Supply Chain                  Our firm's supply
Network Complexity            chain network is
                              characterized by:

                      SCNC1   Large number of
                              first tier
                              suppliers.

                      SCNC2   Strong inter-
                              relationship among
                              the tier-one
                              suppliers.

                      SCNC3   High degree of
                              differentiation in
                              terms of levels of
                              operational
                              practices among the
                              actors.

                      SCNC4   Low frequency of
                              interaction among
                              the actors.

Risk Assessment               While transacting
                              with major
                              suppliers:

                      RAS1    Our firm can make
                              informed decisions.

                      RAS2    Our firm understands
                              the stakes involved
                              in the relationship.

                      RAS3    Our firm remains
                              aware about the
                              implications of the
                              transaction
                              relationship.

                      RAS4    Our firm understands
                              the
                              responsibilities/
                              liabilities involved
                              in the transaction
                              relationship.

Interdependence

Buyer Dependence      BD1     There are other
                              major suppliers
                              capable of providing
                              us with comparable
                              orders.

                      BD2     Total cost of
                              switching to a
                              different set of
                              major suppliers
                              would be
                              prohibitive.

                      BD3     It is difficult to
                              replace our major
                              suppliers while
                              maintaining
                              comparable profit
                              margins.

Supplier Dependence   SD1     Other buying firms
                              can provide our
                              major suppliers with
                              comparable orders.

                      SD2     Total cost of
                              switching to a
                              different set of
                              buying firms would
                              be prohibitive for
                              our major suppliers.

                      SD3     It is difficult for
                              our major suppliers
                              to replace us with
                              other buying firms
                              and maintain
                              comparable profit
                              margins.


Samyadip CHAKRABORTY is an Assistant Professor in the department of operations and IT at IBS Hyderabad, IFHE University, Hyderabad, India. He completed his PhD from ICFAI Business School, IFHE University, Hyderabad, India. He has been a visiting doctoral scholar at the University of Toledo, College of Business and Innovation, Toledo, Ohio. He holds an MBA degree from the University of Liverpool, UK. His area of research interest and focus is healthcare operations and healthcare supply chain management. He has publications in: Business Theory and Practice, International Journal of Applied Engineering Research, IUP Journal of Supply Chain Management, Knowledge Management Journal (India), SS International Journal of Economics and Management, International Journal of Business Economics and Management Research, Macmillan Advanced research series, ECONSPEAK, among other scholarly outlets.

Caption: Fig. 1. Conceptual model

Caption: Fig. 2. Path model

Table 1. Demographic characteristics of the sample

Job title                                 Number of    % of total
                                         respondents   responses

Supply Chain Manager/                        109         53.69
  Executive/Specialist
Vice-president/director/partner              54           26.6
Sr. Executive/Buyer/Analyst                  32          15.76
Logistic Manager                              6           2.96
Others                                        2            1

Sector/type of firm                       Number of
                                         respondents

Manufacturing                                78          38.42
Retail                                       43          21.18
Food processing                              19           9.35
Telecommunication                            21          10.34
Leather                                      03           1.48
Textile                                      03           1.48
Chemical (including                          36          17.73
  pharmaceuticals)

Firm Sales (data in INR                   Number of
converted to US$)                        respondents

$10 million or less                          45          22.17
More than $10 million to $50 million         62          30.54
More than $50 million to $100 million        81           39.9
Above $100 million                           15           7.39

Experience (in years)
Less than 5 years                            39          19.22
More than 5 to 10 years                      128         63.05
Above 10 years                               36          17.73
Total                                        203          100

Table 2. Results of the principal component
analysis of the factors contributing to
interdependence measure

Items       Factors

        Buyer dependence   Supplier Dependence

BD1          0.617                0.036
BD2          0.795                0.119
BD3          0.679                0.101
SD1          0.064                0.718
SD2          0.112                0.632
SD3          0.023                0.761

Table 3. EFA findings

KMO: 0.868 Bartlett: significant at 0.1%
Total variance explained: 69.7%

Factors           Measurement    Item      Cronbach's
                     Items      loadings     Alpha

Supply               SCNC1       0.889       0.906
Chain                SCNC2       0.670
Network              SCNC3        0893
Complexity           SCNC4       0.705

Risk                 RAS1        0.865       0.892
Assessment           RAS2        0.755
                     RAS3        0.806
                     RAS4        0.872

Interdependence       ID1        0.852       0.850
                      ID2        0.795
                      ID3        0.863

Table 4. Measurement model: CFA results

Construct         Items   Std Estm.   p-Value   SMC     AVE     CR

Supply            SCNC1     0.97         *      0.94   0.741   0.91
Chain             SCNC2     0.67         *      0.45
Network           SCNC3     0.94         *      0.89
Complexity        SCNC4     0.71         *      0.50

Risk              RAS1      0.81         *      0.63   0.502   0.80
Assessment        RAS2      0.79         *      0.61
                  RAS3      0.71         *      0.51
                  RAS4      0.93         *      0.92

Interdependence    ID1      0.81         *      0.67   0.549   0.79
                   ID2      0.75         *      0.55
                   ID3      0.88         *      0.76

Note: * significant at p < 0.001

Table 5. Discriminant validity table

                        SCN     RAS     ID

Supply Chain           0.741
  Network Complexity
Risk Assessment        0.094   0.502
Interdependence        0.166   0.025   0.549

Table 6. Hypotheses testing results

Hypotheses          Paths           Standardized   p-Value   Results
                                        path
                                    coefficients

H1           SCNC[right arrow]ID       0.229         ***     Accepted

H2           SCNC[right arrow]RAS      0.029        0.638    Rejected
                                                    (NS)

H3            ID[right arrow]RAS       0.231         ***     Accepted

Note: *** Implies significant at p < 0.001 & NS implies
"not significant"


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