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This study intends to examine the relationships between external knowledge sourcing (EKS), organizational ambidexterity (OA), and manufacturing performance (MP) in the context of large manufacturing firms within a dynamic environment setting. The research framework and derived hypotheses are grounded in the knowledge-based view (KBV) and dynamic capability (DC) theories.

A self-administered online survey was used in this study to gather data. Respondents were the operation leaders representing large manufacturing firms. The collected data were screened for invalid responses, and hypotheses were tested using structural equation modeling.

The study reveals that OA and EKS play key roles in achieving a better MP. Likewise, OA also mediates the relationship between EKS and MP.

Cross-sectional data were collected from large manufacturing firms within five focus sectors in Malaysia. A similar study can be conducted with more sectors of different contexts to confirm the findings.

Knowledge is critical for the firm to react to environmental dynamism, and the ability to manage it ambidextrously will enable the firm to enhance its performance.

This study offers empirical insights from the perspective of the large manufacturing firms in Malaysia, which are undergoing an Industrial Revolution 4.0 (IR4.0) transformation. This study bridges the knowledge gap by revealing the value that EKS can facilitate MP, exploring OA as the prevalent factor and demonstrating how KBV and DC can be applied in this study.

The proliferation of industry platforms has disrupted several industries. Firms adopting a platform business model have experienced a substantial expansion in size and scale, positioning themselves as the foremost valuable entities in market capitalization. Over the past two decades, there has been a substantial expansion in the body of literature dedicated to platforms, and different streams of research have emerged. Despite considerable efforts and the significant progress made in recent years toward a comprehensive understanding of industry platforms, there is still room for further harnessing the field’s diversity. As a result, the aim of this article is to examine the field’s structure, identify research concerns and provide suggestions for future research, thereby enhancing the overall understanding of industry platforms.

We conducted a thorough examination of 458 articles on the topic using bibliometric methods and systematic review techniques.

Through co-citation analysis, we identified five distinct clusters rooted in various bodies of literature: two-sided markets, industry platforms, digital platforms, innovation platforms and two-sided networks. Furthermore, the examination of these five clusters has revealed three key areas that demand further consideration: (1) terminologies, (2) classifications and (3) perspectives.

While previous reviews have provided valuable insights into the topic of industry platforms, none have explored the structure of the field so far. Consequently, as a first step toward advancing the field, we uncover the structure of the literature, identifying three major areas of concern. By addressing these concerns, our goal is to converge different clusters, thereby harnessing the diversity in the field and enhancing the overall understanding of industry platforms.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

The existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach.

Applying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed.

Interestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market.

Overall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.

This study aims to gain a deeper understanding of the industry practice to guide the formation of support tools with a rigorous theoretical backing. Cross-functional teams are an essential component in new product development (NPD) of complex products to promote comprehensive coverage of product design, marketing, sales, support as well as many other activities of business. Efficient use of teams can allow for greater technical competency coverage, increased creativity, reduced development times and greater consideration of ideas from a variety of stakeholders. While academics continually aspire to propose methods for improved team composition, there exists a gap between research directions and applications found within industry practice.

Through interviewing product development managers working across a variety of industries, this paper investigates the common practices of team utilization in an organizational setting. Following these interviews, this paper proposes a conceptual two-dimensional management support model aggregating the primary drivers of team success and providing direction to systematically address features of team management and composition.

Based on this work, product managers are recommended to continually address the positioning of members throughout the entire NPD process. In the early stages, individuals are to be placed to work on project components with explicit consideration toward the perceived complexity of tasks and individual competency. Throughout the development process, individuals’ positions vary based on new information while continued emphasis is placed on maintaining a shared understanding.

Bridging the gap between theory and application within product development teams is a necessary step toward improved product develop. Industrial settings require practical solutions that can be applied economically and efficiently within their organization. Theoretical reflections postulated by academia support improved team design; however, to achieve true success, they must be applicable when considering product development.

The aim of this study was to analyze the effects of carbon reduction efforts and preservation efforts on system benefits in the cold chain industry of fresh products.

This study develops an optimal decision game model for the fresh products in the cold chain, incorporating the retailer's preservation effort and the supplier's carbon emission reduction effort. It quantifies the relationship between carbon emission reduction effort, preservation effort and system profit. The model considers parameters like carbon trading price, consumer low-carbon preference and consumer freshness preference, reflecting real-world conditions and market trends. Numerical simulations are conducted by varying these parameters to observe their impact on system profit.

Under the carbon cap-and-trade policy, the profit of the fresh cold chain system is higher than that of the fresh cold chain system without carbon constraints, and the profit of the supplier under decentralized decision-making is increased by nine times in the simulation results. The increase in carbon trading prices can effectively improve the freshness level of fresh products cold chain, carbon emission reduction level and system profit.

This study comprehensively considers the factors of freshness and carbon emission reduction, provides the optimal low-carbon production decision-making reference for the fresh food cold chain and promotes the sustainable development of the fresh food cold chain.

This paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.

We followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.

Our results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.

The study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.

ATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.

The originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

This study analyzes the performance implications of adopting blockchain to support supply chain business processes. The technology holds as many promises as implementation challenges, so interest in its impact on operational performance has grown steadily over the last few years.

Drawing on transaction cost economics and the contingency theory, we built a set of hypotheses. These were tested through a long-term event study and an ordinary least squares regression involving 130 adopters listed in North America.

Compared with the control sample, adopters displayed significant abnormal performance in terms of labor productivity, operating cycle and profitability, whereas sales appeared unaffected. Firms in regulated settings and closer to the end customer showed more positive effects. Neither industry-level competition nor the early involvement of a project partner emerged as relevant contextual factors.

This research presents the first extensive analysis of operational performance based on objective measures. In contrast to previous studies and theoretical predictions, the results indicate that blockchain adoption is not associated with sales improvement. This can be explained considering that secure data storage and sharing do not guarantee the factual credibility of recorded data, which needs to be proved to customers in alternative ways. Conversely, improvements in other operational performance dimensions confirm that blockchain can support inter-organizational transactions more efficiently. The results are relevant in times when, following hype, there are signs of disengagement with the technology.

While digital transformation holds immense promise, organizations often fail to realize its benefits. This study aims to address how policies for digital transformation benefits realization are translated into practice.

The authors apply a qualitative, comparative case study of two large, public-sector health care organizations in Sweden. Through document and interview data, the authors analyze the process of translation.

The study finds that practice variation is primarily caused by two types of decoupling: policy-practice and means-ends. Contrary to previous studies, coercion in policy compliance is not found to decrease practice variation.

The limitations primarily stem from the empirical selection of two large public health-care organizations in Sweden, affecting the study’s generalizability. Reducing practice variation is more effectively achieved through goal alignment than coercion, leading to implications for the design of governance and control.

Policymakers should, instead of focusing on control-related compliance, work to align organizational objectives and policies to decrease practice variation for successful benefits realization.

The study contributes to better benefits realization of digital transformation initiatives in health care. As such, the authors contribute to a better functioning and more transformative health care in times of increased demand and decreased supply of health-care services.

The study challenges conventional wisdom by identifying that coercion is less effective than goal alignment in reducing practice variation, thereby enhancing the understanding of policy implementation dynamics in health-care settings.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?