Relational Resource Antecedents and Operational Outcome of Supply Chain Collaboration:

Srivastava, Prashant; Srinivasan, Mahesh; Iyer, Karthik N. S.

doi:10.5325/transportationj.54.2.0240

Cited by 15 publications

(9 citation statements)

References 135 publications

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“…As indicated by the R 2 values, the model explains 47 percent of the variance in perceived usefulness, 75 percent of the variance in behavioral intention, 52 percent in the variance of usage behavior, 21 percent of the variance in delivery performance, 22 percent of the variance in flexibility performance, 26 percent of the variance in quality performance, 30 percent of the variance in cost performance, and 19 percent of the variance in innovation performance. These values are comparable to explained variances reported in recent PLS studies that appeared in top‐tier logistics and supply chain management journals (e.g., Golicic, Fugate, and Davis 2012; López‐Navarro, Moliner‐Tena, and Rodríguez‐Artola 2011; Srivastava, Srinivasan, and Iyer 2015; Venkatesh, Chan, and Thong 2012). To estimate the effect of the exogenous variable, perceived ease of use, we calculated Cohen's f 2 .…”

Section: Resultssupporting

confidence: 86%

“…Measurement and structural models were built in partial least squares (PLS), utilizing the SmartPLS software (Ringle, Sarstedt, and Straub 2012; Ringle, Wende, and Will 2005). Our application of PLS is consistent with prior practice in logistics and supply chain management research (e.g., Kale et al 2009; López‐Navarro, Moliner‐Tena, and Rodríguez‐Artola 2011; Srivastava, Srinivasan, and Iyer 2015).…”

Section: Resultssupporting

confidence: 82%

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Mobile Devices and Applications for Supply Chain Management: Process, Contingency, and Performance Effects

Schoenherr

2016

Transportation Journal

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While the adoption, use, and impact of mobile devices and applications (MDA) in downstream (B2C) settings have received considerable attention, studies on these issues in the upstream (B2B) context have been largely absent. The present work fills this gap and investigates the use of MDA for supply chain management (SCM). In doing so, we address several shortcomings noted in extant technology acceptance research and merge this framework with the resource-based view. First, arguments are developed for the central role played by behavioral intention in the relationship from perceived ease of use and perceived usefulness to usage behavior, positioning behavioral intention as an important process element. Second, competitive pressure is theorized to represent an influential contingency, moderating the impact of behavioral intention on usage behavior. Third, the impact of usage behavior on five important strategic performance measures necessary for competitive success (delivery, flexibility, quality, cost, and innovation performance) is assessed, pinpointing the value of MDA for SCM across these crucial measures of performance. And fourth, information uncertainty is positioned as a contextual force that can enhance the value of using MDA for competitive performance. Survey data from 217 adopters of MDA for SCM are used to test our expectations.

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Section: Resultssupporting

confidence: 86%

Section: Resultssupporting

confidence: 82%

Mobile Devices and Applications for Supply Chain Management: Process, Contingency, and Performance Effects

Schoenherr

2016

Transportation Journal

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“…The manufacturing plant or site is specified as the unit of analysis, with targeted respondents being plant managers. The manufacturing context has been a popular one to investigate (e.g., Mejza, Laosirihongthong, and Prajogo 2013; Srivastava, Srinivasan, and Iyer 2015), and seemed to be particularly fitting given the objectives of the present study. Once data are collected, they are validated by the group and compiled in a database.…”

Section: Methodsmentioning

confidence: 99%

Leveraging Suppliers for Product Innovation Performance: The Moderating Role of Intellectual Capital

Schoenherr

2018

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This article scrutinizes the link between supplier leveraging and product innovation performance with the contingency of intellectual capital. Supplier leveraging refers to the utilization of knowledge obtained from suppliers for the benefit of the firm and product innovation performance to the firm's ability to introduce new and radically different products. Intellectual capital is represented by the trichotomy of structural, human, and social capital. Applying the knowledge-based view, a positive moderation effect emanating from structural and human capital is hypothesized, with however a negative moderation effect emanating from social capital. The expectation for the positive moderation is grounded in the enabling knowledge processing capabilities of structural and human capital, enhancing the effectiveness of supplier leveraging for the generation of product innovation performance. The negative moderation of social capital is suggested due to it likely limiting the transferability and aggregation capacity for knowledge, in addition to also potentially questioning the legitimacy of the suppliers' information, and thus making knowledge leveraged from suppliers less effective in informing product innovation performance. Data collected from 576 manufacturing firms confirm these hypotheses. Overall, by considering the interplay between supplier leveraging and intellectual capital, and its effect on product innovation performance, this study offers important new insight on the dynamics governing product innovations within supply chains.

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“…With the high demand of development of buyersupplier relationships in the context of economic exchanges, operational collaboration has attracted growing attention from operations and supply chain management researchers. Effective operational collaboration offers several advantages to exchange partners in a supply chain through improving operational productivity (Srivastava et al, 2015), enhancing efficiency and effectiveness (Lee et al, 2010), risk mitigation and strengthening the effect of marketing exploration on performance (Ho and Lu, 2015).…”

Section: Introductionmentioning

confidence: 99%

Leveraging informational and relational capabilities for performance

Yang

Xie

Liu³

et al. 2018

IJLM

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Purpose The purpose of this paper is to examine the antecedents and consequences of operational collaboration of a buyer and its key supplier in economic exchanges in a supply chain. Design/methodology/approach The analyses of the sample data collected from Chinese manufacturers in this study include measurement model and structural model using structural equation modeling. Findings It shows that firm IT capabilities and supplier relationship management are positively associated with operational collaboration, which in turn enhances dyadic quality performance through improved process efficiency. Research limitations/implications Drawn upon the resource-based view and social contagion theory, it explained the intention of forming an operational collaboration in a buyer – supplier relationship employing a motivation-opportunity scheme. Second, the findings greatly advance our understanding of the indirect linkage between IT capability and quality conformance of the dyads. Practical implications This study suggests that practitioners should enhance the IT capability of their firms as well as implement supplier relationship management program in the organizations so that the buying firms can exchange cost information, provide assistance and pursue joint investments with their suppliers with the facilitation of advanced IT capabilities. Social implications Emerging market manufacturers are suggested to take measures to reduce transaction cost and cycle time uncertainty for quality assurance. Originality/value First, this study empirically explored the determinants of effective operational collaboration from the lens of motivation-opportunity scheme through integrating resource-based view and social contagion theory. Second, it examined the effect of operational collaboration on dyadic quality performance for both buyers and suppliers. Third, it examined the drivers of effective operational collaboration in the context of Chinese manufacturers.

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Relational Resource Antecedents and Operational Outcome of Supply Chain Collaboration:

Cited by 15 publications

References 135 publications

Mobile Devices and Applications for Supply Chain Management: Process, Contingency, and Performance Effects

Mobile Devices and Applications for Supply Chain Management: Process, Contingency, and Performance Effects

Leveraging Suppliers for Product Innovation Performance: The Moderating Role of Intellectual Capital

Leveraging informational and relational capabilities for performance

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