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This paper investigated the organizational adoption of big data analytics (BDA) in the context of supply chain planning (SCP) to conceptualize how resources are orchestrated for organizational BDA adoption and to elucidate how resources and capabilities intervene with the resource management process during BDA adoption.

This research elaborated on the resource orchestration theory and technology innovation adoption literature to shed light on BDA adoption with multiple case studies.

A framework for the resource orchestration process in BDA adoption is presented. The authors associated the development and deployment of relevant individual, technological and organizational resources and capabilities with the phases of organizational BDA adoption and implementation. The authors highlighted that organizational BDA adoption can be initiated before consolidating the full resource portfolio. Resource acquisition, capability development and internalization of competences can take place alongside BDA adoption through structured processes and governance mechanisms.

A relevant discussion identifying the capability gap and provides insight into potential paths of organizational BDA adoption is presented.

The authors call for attention from policymakers and academics to reflect on the changes in the expected capabilities of supply chain planners to facilitate industry-wide BDA transition.

This study opens the black box of organizational BDA adoption by emphasizing and scrutinizing the role of resource management actions.

The recently developed resource orchestration theory studies the processes by which managers handle resources to create competitive advantages. According to this theory, it is the way that resources interact with each other that results in such advantages. Resource integration, i.e. the alignment, or fit between resources, is one important outcome of resource orchestration processes. This paper aims to develop a scale and outline approaches to measuring such resource integration.

Using a typology of five types of resources derived from value theory, the authors develop a scale for measuring the fit between resource types, i.e. the degree of resource integration. The authors illustrate the method using a case example of an IT company and demonstrate how a variety of statistical methods including hierarchical cluster analysis, structural equation modeling, social network analysis and methods from biostatistics can provide measures of resource integration.

The authors develop a scale and associated measures that can help scholars systematically measure and identify firms with a high or low level of resource integration capability. This makes it possible to investigate further these companies and reconstruct how they support dynamic capabilities, as well as commonalities across firms with high and low levels of this capability.

Existing studies on resource orchestration have failed to provide us with a reliable measurement instrument that can be used both in cross-sectional work, and in repeated or time-series studies, allowing us to assess the degree to which a wider range of resources in an organization are integrated. The authors develop and demonstrate such an instrument.

The purpose of this paper is to analyze how the service infusion literature explains competitive advantage through services. The four strategic management theories – competitive forces, the resource-based view, dynamic capabilities, and relational view – are applied in the analysis.

A systematic literature review analyzes the links between the service infusion and strategy literature.

The review reveals that although discussion of service infusion applies strategic management concepts, the stream lacks rigor with respect to construct definition and justification. Additionally, contextual variables are often missing. The result is an over-emphasis of contextually bound measures, such as technology, and focal actors.

The growing trends toward social networks, co-specialization, actor dependency and shared resources encourage service infusion scholars to focus on network-related and relational capabilities, co-opetition, open business models, and relational rent extraction. Furthermore, service infusion research would benefit from considering strategy-based theoretical discussions, constructs, and constraints that would improve the scientific rigor, impact and contribution.

This paper represents a systematic attempt to link the service infusion literature with strategic management theories and thoroughly analyzes the knowledge gaps and possible misconceptions.

The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.

The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.

The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.

Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

This study aims to examine how external and internal conditions drive the impact of circular economy mechanism by decomposing into three policy networks in terms of reduce, reuse and recycle, to better understand the contingency model of climate change and effect of firm size on subsequent performance.

Drawing on circular economy network and resource-based view (RBV)-network-resilience strategy framework, a pooled longitudinal cross-sectional data model is developed using a sample of 4,050 Taiwanese manufacturing multinational corporations (MNCs) making foreign direct investment between 2013 and 2018. Structural equation modeling analysis is used to comprehensively examine and investigate each circular economy policy network in the context of climate change and firm size. Post hoc multigroup analysis (MGA) is also conducted.

MGA shows that the reduce policy network is positively and negatively related to manufacturing know-how and production size, respectively. The impact of reuse policy network can enhance the competence of large firms. The recycle policy network is more prominent in terms of competence enhancement of climate change.

MNCs are seeking to build circular economy policy networks to a greater extent, given climate change pressure and guidelines.

This study adds to the circular economy and RBV-network-related literature on climate change and interactions to enhance performance, echoing the recent call on the sustainability of the circular economy of MNCs.

Although both institutional export assistance and entrepreneurial orientation (EO) contribute separately and positively to export performance, the interplay between them has received little attention. This study examines the role of international EO in deriving performance benefits from governmental and nongovernmental export assistance.

In this longitudinal study, two surveys were administered at two different times: In 2011, 705 Bangladeshi apparel exporters were surveyed, and in 2019, a subsequent survey of 198 firms in multiple industries was conducted. The aim of the surveys was to assess the relationships between governmental and nongovernmental assistance, EO and export performance.

The results of the first survey show that, while nongovernmental assistance influences performance directly and via EO, governmental assistance has only direct effects. Furthermore, the negative influence of government assistance on EO reduces the total effects and renders them nonsignificant. The results of the second survey demonstrate that government EPPs have both direct and indirect positive and significant effects on market performance, indicating a partial mediation, whereas quasi-governmental assistance has positive and significant direct effects as well as negative but nonsignificant indirect effects. Nongovernmental EPPs have both direct and indirect significant effects on international performance, indicating a partial mediation.

The study has important implications for researchers studying export assistance and its impact on firm performance. Instead of adopting a parochial view of government assistance, this study categorizes such assistance into three types – government, quasi-government and nongovernment. Furthermore, this study bridges the export assistance and international entrepreneurship literature by including EO.

Entrepreneurs must emphasize the use of government assistance in order to enhance export performance. However, to promote both entrepreneurship and performance, they must emphasize nongovernment assistance. Exporters should also capitalize on the assistance extended by various quasi-governmental agencies to bolster export performance.

Given the performance advantage of export assistance, this study highlights the contribution of the private sector in promoting export entrepreneurship while shedding light on the pernicious role of (quasi-)governmental assistance in export entrepreneurship.

This study aims to examined the impact of business intelligence (BI) and supply chain ambidexterity (SCA) on operational performance (OP), contributing to dwarf knowledge in small- and medium-sized enterprises (SMEs) in the context of emerging economies. The mediating role of SCA was considered in the proposed model.

The study used the quantitative method to investigate the interdependencies between variables. As a result, 216 senior and middle managers/owners of SMEs in Ghana were surveyed using a purposive and convenient sampling method. SPSS version 23 and Smart PLS version 3 were used to conduct the research.

While the direct link among BI, SCA and OP was confirmed. The outcome also showed that SCA plays a significant mediating role between BI and OP among SMEs.

The outcome of the study indicates that SCA encourages the use of BI to generate superior OP among SMEs. This knowledge will improve the performance of SMEs and their ability to withstand the competition in the global market.

With the discovery of this study, the theory of a resource-based view now has some empirical evidence behind it. As a result, SMEs prioritize aspects that could improve their operations and implement tactics that would nurture better performance and competitive advantages.

This article examines the mechanisms through which big data analytics capabilities (BDAC) contribute to creating sustainable value and analyzes the mediating roles that supply chain management capabilities (SCMC), as well as circular economy practices (CEP), play through their impact on sustainable performance.

Following a literature review, a serial mediation model is presented. Hypotheses regarding direct and mediating relationships are tested to determine their potential for sustainability impact and circularity. Partial least squares structural equation modeling (PLS-SEM) has been applied for causal and predictive purposes.

The results indicate that big data analytics capabilities do not have a direct positive impact on sustainable performance but influence indirectly through SCMC and CEP.

Although some authors have addressed the associations between IT business value, supply chain (SC), and sustainability, this paper provides empirical evidence related to these relationships. Additionally, this study performs novel predictive analyses.