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This paper aims to explore the significance of resource sharing in business to capture new market opportunities and securing competitive advantages. Firms enter strategic alliances (SAs), especially for designing new products and to overcome challenges in today’s fast changing environment. Research projects have dealt with the creation of SAs, however without concrete referencing the impact on selected supply chain resources. Furthermore, academia rather focused on elaborating the advantages and disadvantages of SAs and how this affects structural changes in the organization than examining the effects on supply chain complexity and performance.

The authors collected and triangulated a multi-industry data set containing primary data coming from more than 200 experts in the field of supply chain management along and secondary data coming from Refinitiv’s joint ventures (JVs) and SA database and IR solutions’ database for annual reports. The data is evaluated in three empirical settings using binomial testing and structural equation modeling.

The results show that nonequity SAs and JVs have varying degrees of impact on supply chain resources due to differences in the scope of the partnership. This has a negative impact on the complexity of the supply chain, with the creation of a JV leading to greater complexity than the creation of a nonequity SA. Furthermore, the findings prove that complexity negatively impacts overall supply chain performance. In addition, this study elaborates that increased management capabilities are needed to exploit the potentials of SAs and sheds light on hurdles that must be overcome within the supply network when forming a partnership. Finally, the authors give practical implications on how organizations can cope with increasing complexity to lower the risk of poor supply chain performance.

This study investigates occurring challenges when establishing nonequity SAs or JVs and how this affects their supply chain by examining supply networks in terms of complexity and performance.

Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers.

Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry.

On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing.

Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.

The purpose of this paper is to achieve a collective understanding of the capabilities required for digital procurement transformation (DPT).

The authors contextualize theory about dynamic capabilities (DCs) to integrate the fragmented body of literature on procurement digitalization by means of a systematic literature review (SLR). By extracting and clustering capabilities, as well as proven performance outcomes from existing literature in the field, the authors develop a conceptual model of the DCs required for DPT.

The authors first introduce and define DPT and the corresponding motivations that trigger firms to invest in advanced digital technologies. Second, by adopting the DC lens, the authors provide an overview of nine microfoundations required for DPT and highlight the strategic options procurement leaders can use when strategizing about adopting combinations of digital technologies. Third, the authors present a future research agenda on DCs for DPT.

The developed conceptual model must be verified and enhanced through further empirical research.

The conceptual model can be used by procurement leaders as a starting point and framework when strategizing about digitally transforming the procurement organization.

The study is the first to synthesize previous research findings on procurement digitalization through an SLR in order to develop a fine-grained conceptual model that supports practitioners and researchers alike in better understanding the capabilities required for and potential performance outcomes of DPT.

This chapter is designed to provide an overview of the challenges facing Industry 4.0, focussing on the manufacturing sector, and highlighting the specifics of small to medium-sized enterprises. Recent technologies for data science, analysts, robotics, and other smart manufacturing trends are discussed, and the opportunities, difficulties, and limitations for breakthrough development are highlighted.

Supply chains are increasingly incorporating social perspectives into their activities. It is often argued that social initiatives in supply chain management (SCM) are a response to external pressures. However, it is still undetermined whether these initiatives can improve economic performance. Additionally, it is proposed that digital transformation in supply chains, also described as Smart Supply Chain, can support social performance. Therefore, this study aims to analyze the association between digital transformation, social performance in SCM and economic performance.

The study is based on a survey of 473 companies in Brazil, a country where social needs create high pressures on companies to respond to the country’s challenges. The data collected underwent testing using ordinary least squares regression and bootstrapping techniques to examine the mediation effects between Smart Supply Chain, social performance and economic performance.

The findings indicate that digital transformation supports social performance. Additionally, adopting social initiatives helps to increase firm performance in the context of an emerging economy, while social initiatives mediate between digital transformation and firm performance.

This paper provides a new perspective on the social side of supply chains by demonstrating the close relationship between digital transformation and social engagement initiatives. It argues that aligning digital transformation strategies and technologies with social performance is crucial for companies to establish stronger connections with stakeholders.

This study examines the interdependent relationships between green supply chain management (GSCM), carbon neutrality capability (CNC), digital transformation (DT) and firm performance (FP) among enterprises listed on the Chinese stock market. The primary objective is to provide a scholarly examination that may help these organisations to enhance their GSCM practices significantly.

Drawing on data from mainland Chinese publicly listed firms over the period from 2014 to 2021, this study posits and methodically evaluates four hypotheses: (1) GSCM practices are positively associated with an increase in CNC; (2) GSCM practices have a beneficial impact on FP; (3) DT moderates the GSCM–FP relationship and (4) CNC mediates the effect of GSCM on FP.

Contrary to initial assumptions, the findings suggest that GSCM practices may initially have a negative impact on FP. However, a concerted focus on CNC has the potential to convert this negative trajectory into a positive influence on FP. The findings further identify that DT has a significant moderating effect on the GSCM–FP relationship.

The findings of this study enrich the academic discourse concerning the symbiotic effects of GSCM, CNC and DT on FP. By systematically analysing these dynamics, the study underscores the critical importance of CNC and DT in the successful application of GSCM practices, thus offering valuable contributions to the literature on sustainable corporate operations.

The enabling technologies that emerged from information technology (IT) have had a considerable influence upon the development of marketing tools, and marketing has become digitalized by adopting these technologies over time. The purpose of this paper is to demonstrate the impacts of these enabling technologies on marketing tools in the past and present and to demonstrate their potential future. Furthermore, it provides guidance about the digital transformation occurring in marketing and the need to align of marketing and IT.

This study demonstrates the impact of enabling technologies on the subsequent marketing tools developed through a content analysis of information systems and marketing conference proceedings. It offers a fresh look at marketing's digital transformation over the last 40 years. Moreover, it initially applies the findings to a general digital transformation model from another field to verify its presence in marketing.

This paper identifies four eras within the digital marketing evolution and reveals insights into a potential fifth era. This chronological structure verifies the impact of IT on marketing tools and accordingly the digital transformation within marketing. IT has made digital marketing tools possible in all four digital transformation levers: automation, customer interaction, connectivity and data.

The sequencing of enabling technologies and subsequent marketing tools demonstrates the need to align marketing and IT to design new marketing tools that can be applied to customer interactions and be used to foster marketing control.

This study is the first to apply the digital transformation levers, namely, automation, customer interaction, connectivity and data, to the marketing discipline and contribute new insights by demonstrating the chronological development of digital transformation in marketing.

Even though extant research highlights the crucial role some stakeholders play in helping corporations understand, manage and mitigate the occurrence of modern slavery in their supply chains and operations, there is a fundamental lack of understanding of all the relevant stakeholder groups and the specific roles they play. By adopting a stakeholder theory approach, this study aims to identify all the key stakeholders and their associated roles towards supporting corporations’ modern slavery monitoring, detection and disclosure activities.

A systematic literature review was conducted by following the PRISMA guidelines. Relevant literature included scholarly work focusing on the identification of key stakeholders and the roles they play in enabling corporations’ modern slavery monitoring, detection and disclosure activities.

Nine stakeholder groups and their roles were identified, such as governments, workers, IGOs, NGOs and suppliers. Examples of performed activities include conducting audits, providing training, monitoring occurrences of modern slavery, enforcing regulations, reporting on labour issues and evaluating corporations’ modern slavery reports.

A comprehensive understanding of key stakeholders and their roles enables better collaboration towards achieving transparency within corporations’ supply chains and operations. Other stakeholders can leverage these findings to enhance modern slavery reporting activities.

Clarity regarding key stakeholders and their roles may improve quality and quantity of reported modern slavery information, enhancing corporations’ public accountability.

This study adopts a stakeholder theory approach to provide a comprehensive understanding of key stakeholders and their roles in enhancing corporations’ modern slavery reporting activities.

The survival of companies today hinges on their adaptability and flexibility, with digital transformation (DT) and organizational slack (OS) playing crucial roles. Despite their recognized importance, these factors are often studied separately. This study aims to explore how OS facilitates DT and evaluate their synergies and trade-offs to improve performance.

Using data from the European Manufacturing Survey, structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA), we investigate causal relationships and possible combinations between different dimensions of OS and DT that contribute to business performance.

We confirmed the positive effect of OS and DT on business performance, highlighting the importance of organizational over technological factors. While not definitively establishing OS as a precursor to DT, our findings underscore the need for human and operational slack to improve performance, especially in less technology-intensive contexts.

Our findings evidence that decision-makers should integrate OS with DT initiatives to improve the firm’s competitiveness. However, it is worth noting that while OS seems essential in low-tech shopfloors, its importance is lower in high-tech environments. Furthermore, within the possible combinations, managers should promote operational slack and digitalization, as it seems fundamental to improve business performance.

This article contributes to the management field in three ways. First, it clarifies controversies by providing evidence of the positive roles of DT and OS as drivers of competitiveness for manufacturing firms. Second, we verify that OS is not directly linked to DT, challenging existing assumptions. Third, it investigates the combinations of OS and DT that drive business performance improvement, emphasizing their synergies and trade-offs.

This paper identifies consumer acceptance criteria of artificial intelligence (AI)-enabled products and services in the business. We first investigate the existing three models. They are the technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT) and the consumer acceptance of technology (CAT). We then discuss the applicability of these three models for AI-enabled products and services. Finally, we outline the shortcomings of the models and propose an AI-enabled product and service acceptance model (AIEPSAM). We also validate the proposed AIEPSAM model with empirical results using primary survey data.

To understand the customer’s point of view on AI applications in products and services, we identify some critical factors and present a conceptual framework of consumers' acceptance criteria based on existing literature, prior research and prominent technology management theories. Then, the study broadens the horizon beyond established principles associated with technology acceptance to accommodate AI-specific factors/variables like data privacy, explainability and apparent opacity of algorithms. In this paper, we propose an AIEPSAM and validate that model with primary survey data.

We argue that although TAM, UTAUT and CAT models are generally applicable to explain consumers' attitudes towards technology, these models alone are insufficient to encompass the entire spectrum of AI-related issues that must not be ignored. The proposed model, namely AIEPSAM, accommodates the limitations of the existing models and modifies the CAT model to make it suitable for the acceptance of AI technology.

We attempt to articulate the consumer acceptance criteria of AI-enabled products and services and discover useful insights, leading to the critical examination of TAM, UTAUT and CAT models and formulating AIEPSAM with validation through primary survey data. This study is not to criticize the TAM and other technology acceptance models but to incorporate AI-specific factors into those models. Through this study, we propose the required modifications in the existing technology acceptance models considering the AI-specific additional factors. The AIEPSAM will assist companies in building AI-enabled products and services and better understanding the technology emergence (TE) and technology opportunities (TO).