Supply Base Management for Product Innovation

Transcription

1 DECISION SCIENCES INSTITUTE Strategic Management of Supply Base for Product Innovation from Resource Dependence Perspective (Full Paper Submission) Muhammad Shakeel Sadiq Jajja Suleman Dawood School of Business Lahore University of Management Sciences, Pakistan. Shaukat Ali Brah Karachi School of Business and Leadership, Pakistan. Syed Zahoor Hassan Suleman Dawood School of Business Lahore University of Management Sciences, Pakistan. Vijay R. Kannan Jon M. Huntsman School of Business Utah State University, USA. ABSTRACT This paper uses the resource dependence theory to understand as to how buyers develop, control, align, and utilize supply base to obtain enhanced product innovation to improve buyer performance. The study uses data from 296 companies from India and Pakistan to test the hypotheses using structural equation modeling and PLS approach. Overall, the study finds that buyer s innovation strategy positively impacts product innovation through innovation intent, potential, and engagement of supply base. Also, the study finds support for the positive moderation effect of engaging suppliers on supply-base potential. Comparative analysis of Indian and Pakistani datasets endorse the direct relationships. KEYWORDS: Resource dependence theory, Buyer-supplier relationship, Product innovation, Empirical research, India and Pakistan

2 DECISION SCIENCES INSTITUTE Strategic Management of Supply Base for Product Innovation from Resource Dependence Perspective (Full Paper Submission) Muhammad Shakeel Sadiq Jajja Suleman Dawood School of Business Lahore University of Management Sciences, Pakistan. Shaukat Ali Brah Karachi School of Business and Leadership, Pakistan. Syed Zahoor Hassan Suleman Dawood School of Business Lahore University of Management Sciences, Pakistan. Vijay R. Kannan Jon M. Huntsman School of Business Utah State University, USA. ABSTRACT This paper uses the resource dependence theory to understand as to how buyers develop, control, align, and utilize supply base to obtain enhanced product innovation to improve buyer performance. The study uses data from 296 companies from India and Pakistan to test the hypotheses using structural equation modeling and PLS approach. Overall, the study finds that buyer s innovation strategy positively impacts product innovation through innovation intent, potential, and engagement of supply base. Also, the study finds support for the positive moderation effect of engaging suppliers on supply-base potential. Comparative analysis of Indian and Pakistani datasets endorse the direct relationships. KEYWORDS: Resource dependence theory, Buyer-supplier relationship, Product innovation, Empirical research, India and Pakistan INTRODUCTION Product innovation dimension of organizational strategy plays an important role in shaping organizational priorities and actions (Quinn, 2000). Recently a greater interest in innovation generation in a supply chain context has appeared. Sustaining competitiveness through innovation requires appropriate supply chain capability and practices. The managerial challenge is to develop supply chains capable of producing innovative products in effective, efficient, and consistent manner (Roy et al., 2004). Suppliers play a vital role in developing and rolling out innovative products in a competitive manner. Suppliers, similar to other innovation stakeholders of an organization, are major potential source of product innovation.

3 Empirical research studies on buyer-supplier relationship in the context of product innovation objectives use perspective of buyer as well as supplier, though the majority is based on data from buyers. Our review of relevant literature finds a pattern that studies using data of buyers seek to understand the impact of enabling and moderating factors for enhancing outcome variables related to product innovation. The studies highlighting supplier s perspective seek to understand as to which factors enhance and influence innovation enablers at supplierend in buyer-supplier relationship. In general, the trend of research on buyer-supplier relationship for product innovation is to understand as to which coordination and capability aspects of buyer-supplier relationship impact product innovation. Majority of the buyer-supplier innovation research revolves around explaining the linkages of similar coordination and capability aspects with product innovation outcomes. From theory development stand-point, there seems to be lack of research that seeks to understand as to how buyer s strategic priorities shape a capable and controlled supply base that in turn enhances buyer s product innovation. Similarly, in general, there is scarcity of research, conceptual as well as empirical, that focuses on innovativeness of buyer-supplier relationship (Arlbjørn & Paulraj, 2013). From empirical evidence stand-point, the research on innovation generation in supply chain relationships using data from emerging economies, particularly Indian subcontinent, appears to be minimal. The shifts of manufacturing operations and innovation focus to emerging economies have expanded the global innovation landscape during recent couple of decades (Lema et al., 2012). This paper uses the lens of resource dependence theory (Pfeffer & Salancik, 2003) to understand the impact of innovation strategy of supply chain focused buyers on their supply base to enhance product innovation and performance. The research framework split into three themes seeks to understand as to how buyers develop supply base resources, control them to keep retain them, and align and utilize them to obtain enhanced product innovation that in turn improves the buyer performance (Figure 1). Figure 1: Research Model THEORY AND HYPOTHESES Development of Supply Base Resources Resource dependence theory argues that individual organizations have lack the needed resources and ability to maintain necessary conditions to achieve their desired outcomes (Pfeffer & Salancik, 2003). Hence it makes sense when firms seek to find innovations from firms in their supply chain as well as from the entities having the innovation potential in broader environment.

4 Looking from the stand-point of buyer-supplier relationship, resource dependence theory draws that supply chain focused innovative organizations, constrained by the needed resources like most organizations, would actively develop supply base of higher innovation potential. It is important to shape innovative supply base because firms are less likely to achieve supply chain strategy objectives without enhanced and aligned suppliers functioning(ahmadjian & Lincoln, 2001). Companies select their suppliers after investigating their managerial and technical capability for the desired outcomes (Kannan & Tan, 2006). The buyers encourage their suppliers to develop independent technological competence and work with multiple buyers to gain a variety of knowledge and skills. Hence we propose the following hypothesis: H1: Supply chain innovation strategy has positive impact on supply base innovation potential. Control of Supply Base Resources An important tenet of resource dependence theory is control which stems from imbalance of organizational interdependencies that can be categorized into outcome interdependence and behavior interdependence (Pfeffer & Salancik, 2003). The idea of behavior interdependence draws that if an agent has resources valued by others but has less incentive for sharing resources or has conflicting competitive objectives with others such agent will have more control in the interdependence. In the context of buyer-supplier relationship, resource dependence theory draws that supply chain focused innovative companies seek to find suppliers with matching competitive priorities to gain more control in the buyer-supplier interdependence. The literature on supply chain relationships argues for alignment of competitive priorities of partners for more focused supply chain functioning. Product innovation focused companies promote commitment with introduction of new products among their supply chain partners. Companies streamline competitive priorities among their supply chain partners to achieve desired objectives. This notion provides the foundation for following hypothesis: intent. H2: Supply chain innovation strategy has a positive impact on supply base innovation The clarity of purpose among suppliers provides the basis to develop the needed capabilities. Innovation intention among all stakeholders and contributors is an antecedent to the innovation capability (Lichtenthaler et al., 2011). This convergence of priorities creates and strengthens a mutual understanding of commitment to develop capabilities to sustain innovation. Hence, we hypothesize. H3: Supply base innovation intent has a positive impact on supply base innovation potential. In addition, drawing form the idea of behavior interdependence from resource dependence theory, innovation intent of suppliers seems to influence behavior of suppliers in favor of buyer s strategy (Pfeffer & Salancik, 2003). Innovation intent of suppliers reduces the conflict of objectives in the buyer supplier interdependence thus strengthening buyer s control over supplier actions. Buyers use this control to influence their suppliers in a way that suppliers develop the capabilities needed to perform better on buyer s priorities. In this context, suppliers would build innovation capabilities more persuasively to attract and maintain more business from innovation focused buyers. We hypothesize:

5 H4: Supply base innovation intent positively moderates the impact of supply chain innovation strategy on supply base innovation potential. Alignment and Utilization of Supply Base Resources According to resource dependence theory, interdependencies among organizations create the problem of uncertainty and unpredictability for the focal organization (Pfeffer & Salancik, 2003). In the context of buyer-supplier relationship for innovation, resource dependence theory draws that supply chain focused innovative companies will develop systems to increase supplier engagement in the innovation process to reduce uncertainty and increase predictability of actions. We hypothesize: H5: Supply chain innovation strategy has positive impact on supply base innovation engagement. Supply chain literature refers to capable suppliers as near innovators for developing innovative products and solutions for application in the buyer s market (Melnyk et al., 2010). Companies create a buyer-supplier innovation structure that flourishes innovation to benefit from knowledge generation and innovation capabilities of their suppliers. We hypothesize: H6: Supply base innovation potential has a positive impact on product innovation. The resource dependence theory draws that engagement and coordination lends control to both parties producing a sense of cooperation (Pfeffer & Salancik, 2003). Supplier perception of cooperative relationship from buyer provides the needed motivation to share their innovation with buyer, even in the presence of threat of disrupting their own operations with the changes that the innovation would demand if adopted by the buyer (Wagner & Bode, 2013). This needed sense of cooperation and collaboration comes from engaging suppliers in the innovation process. Hence, we hypothesize: H7: Supply base innovation engagement positively moderates the impact of supply base innovation potential on product innovation. Engagement of suppliers has direct positive impact on product innovation as well. Collaboration and integration with suppliers play an important role in achievement of supply chain goals (Flynn et al., 2010). The ability to work together to integrate capabilities of supply chain partners enhances the firm s ability to embark on incremental as well as radical innovations. We hypothesize: H8: Supply base innovation engagement has positive impact on product innovation. Performance Outcomes Frequent introduction of innovative products increases repeat purchase of new models and leads to increased market share. Innovation in cost effectiveness of products can expand the overall market size (Zu et al., 2008). We hypothesize: H9: Product innovation has positive impact on business performance.

6 RESEARCH METHOD Questionnaire Development The study seeks insights from the literature to develop the items of the research questionnaire to measure the research constructs. All questionnaire sections use a five point Likert scale. Data Collection The data is collected from senior managers of relevant functional units of companies from various industrial sectors of India and Pakistan. 850 companies from three stock exchanges of Pakistan. Similarly, 450 companies were selected from two chambers of commerce and industries from India. Data collection scheme following total design methodology of Dillman (2007) generates 296 workable responses (Table 1) thus providing effective response rate of 22.77%. Respondents Table 1: Respondents profile Industrial sectors Positions Pakistan India Total Sector Pakistan India Total Top Managers Automobile Senior Managers Process Middle Managers Engineering manufacturing Others FMCG/Food/Retail Total Pharmaceutical Textile Telecom/IT Others Not mentioned Total The single common factor analysis of the combined dataset of 296 companies shows that only 34.89% (less than 50%) variance is explained by the single component factor of all the items. Hence common method is not a significant problem in this (Podsakoff et al., 2003). RESULTS Results of Measurement Model The measurement model uses the collective data of 296 companies from India and Pakistan (Conover, 2011). Factor loadings of the items in the final model are greater than Model fit of the confirmatory factor analysis (CFA) model is acceptable (Chi-square = ; Chisquare/d.f. = 1.952; d.f. = 215; RMR = 0.036; CFI = 0.947; TLI = 0.938; IFI = 0.948; NFI = 0.899). Average variance extracted value of each construct is higher than 0.50 thus providing satisfactory evidence of the convergent validities of all constructs. A significant difference between chi-square values of constrained and unconstrained models of any pair of constructs

7 provides evidence for satisfactory discriminant validity of all constructs (Segars & Grover, 1993). Cronbach s alpha of all constructs is more than Results of Hypotheses Tests Using Collective Sample: The study tests for the hypotheses in two steps. In the first step the study tests the hypotheses proposing direct relationship. In the second step the study tests the hypotheses proposing moderation effects. The study uses full structural model technique of structural equation modeling using the collective dataset of 296 companies to test for the direct relationships. The full structural model includes the control variables (foreign collaboration, age of company, current exports, annual revenue, and number of employees) in calculation of the estimates. The model with control variables indicates an overall satisfactory model fit (χ 2 /df = 1.878; d. f. = 31; CFI = 0.917; TLI = 0.899; IFI =.918; and RMSEA = 0.061). Figure 2: Full Structural Model Estimates (Sample size = 296) Figure 2 shows the path estimates and statistical significance of the hypotheses test results. The full structural model test supports the hypotheses 1, 2, 3, 5, 6, and 8 proposing direct relationships at 99% confidence level. The study uses SMART PLS to test for the moderation hypotheses. The results of the moderation hypotheses test are shown in Table 2. The results indicate a lack of support for the hypotheses 4 arguing for a positive moderation effect of supply base innovation intent on the relationship between buyer s innovation strategy and supply base innovation potential. The statistically insignificant regression coefficient of the combination of SBII and SCIS indicate the lack of support for the moderation effect of SBII. Similarly, the impact of SCIS on SBIP remains statistically significant event after the inclusion of the combined effect of SBII and SCIS in the model. However, the results indicate a positive support for the hypotheses 7 arguing for a positive moderation effect of supply base innovation engagement on the relationship between

8 supply base innovation potential and product innovation. Moreover, impact of SBIP on PI turns statistically insignificant after inclusion of the combined effect of SBIE and SBIP in the model. Table 2: Moderation Test Results (On adding moderation variables in the model) Hypotheses Collective data set (191 from Pakistan from India) T-stat value Supported (Yes/No) H4 SBII Moderation (Combination of SCIS and SBII) No H1 SCIS=>SBIP H7 SBIE Moderation (Combination of SBIE and SBIP) Yes H6 SBIP=>PI Tests of Hypotheses Using Indian and Pakistani Datasets for Comparative Insights: The study conducts separate tests of the hypotheses using Indian and Pakistan datasets. Keeping in view the limitation of sample size the study uses SMART PLS for the comparative analysis. Firstly, the study tests for the direct relationships. The Pakistani dataset supports all hypotheses proposing direct relationships except the hypothesis 3 that argues for a positive impact of supply base innovation intent on supply base innovation potential. The Indian dataset supports all the hypotheses proposing direct relationships. The results are summarized in Table 3. Table 3: Results of tests of hypotheses proposing direct relationships using Indian and Pakistani datasets Hypotheses Pakistani dataset (Size = 191) Indian dataset (Size = 105) T-stat value Supported (Yes/No) T-stat value Supported (Yes/No) H1 SCIS=>SBIP Yes Yes H2 SCIS=>SBII Yes Yes H3 SBII=>SBIP No Yes H5 SCIS=>SBIE Yes Yes H6 SBIP=>PI Yes Yes H8 SBIE=>PI Yes Yes H9 PI=>BP 3.95 Yes Yes Secondly, the study tests for the moderation relationships using the separate datasets from India and Pakistan. The study uses SMART PLS to test for the moderation hypotheses. The Pakistani dataset does not support hypotheses 4 that argues for a positive moderation effect of supply base innovation intent on the relationship between supply chain innovation strategy and supply base innovation potential. However, the Pakistani dataset supports the hypotheses 7 that argues for a positive moderation effect of supply base innovation

9 engagement on the relationship between supply base innovation potential and product innovation. The Indian dataset however does not support either of the moderation relationships rejecting the hypothesis 4 and hypothesis 7. A summary of the results of the moderation tests using Pakistani and Indian datasets in given in Table 4. Table 4: Results of moderation tests using separate datasets from India and Pakistan Hypotheses Pakistani dataset T-stat value Indian dataset (Size = 191) (Size = 105) Supported (Yes/No) T-stat value Supported (Yes/No) H4 SBII Moderation (Combination of SCIS and SBII) No No H1 SCIS=>SBIP SBIE Moderation H7 (Combination of SBIE and SBIP) Yes No H6 SBIP=>PI DISCUSSION AND CONCLUSION Overall, the findings strongly endorse the hypotheses that propose direct impact of supply chain strategy of innovation focused companies on the features of their supply base including supply base innovation intent, potential, and engagement. Similarly, overall the data supports positive impact of supply base innovation intent, potential, and engagement on product innovation except in Pakistani dataset. However, the study finds mixed results for the moderation effects of supply base innovation intent and engagement. The combined and Pakistani datasets support the moderation effect of supply base innovation engagement. The Indian dataset does not support the moderation effect of supply base innovation engagement. The moderation effect of supply base innovation intent does not get support from the data. This paper uses the resource dependence perspective to study the impact of supply chain innovation strategy on development, control, and utilization of the needed supply base for product innovation. The paper proposes a hypothesized model and tests the model using empirical data from companies in India and Pakistan. The study finds that supply chain strategy of buyer has impact on development and utilization of supplier capabilities for innovation in buyer products. Moreover, the study finds that engagement of supplier in the innovation process enhances product innovation. REFERENCES Ahmadjian, C. L., & Lincoln, J. R. (2001). Keiretsu, governance, and learning: case studies in change from the Japanese automotive industry. Organization Science, 12(6),

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