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The purpose of this paper is to examine the digitalization of operational processes and activities in lean manufacturing firms and explore the associated learning implications through the lens of cumulative capability theory.

Adopting a multiple-case design, we examine four cases of digitalization initiatives within lean manufacturing firms. We collected data through semi-structured interviews and direct observations during site visits.

The study uncovers the development of learning capabilities as a result of integrating lean and digitalization. We find that digitalization in lean manufacturing firms contributes to the development of both routinized and evolutionary learning capabilities in a cumulative fashion.

The study adds nuance to the limited theoretical understanding of the integration of lean and digitalization by showing how it cumulatively develops the learning capabilities of lean manufacturing firms. As such, the study supports the robustness of cumulative capability theory. We further contribute to research by offering empirical support for the cumulative nature of learning.

This study aims to provide an overall framework that connects and explains a macro-perspective of the findings from the five studies of this special issue. Through this, we aim to answer two main questions: How can Lean and Industry 4.0 be integrated, and what are the outcomes for workers from such integration?

The special issue received 64 papers that were evaluated in multiple stages until this final sample of five papers that describe different facets of the integration between Lean and Industry 4.0 and their relationship with worker activities. In this introduction, we review the main findings of these five studies and propose an integrative view and associated propositions. A discussion provides directions to advance the field further.

The framework shows that when Lean and Industry 4.0 are integrated, companies will face two types of tensions, dialectical and paradoxical, which require different managerial approaches. By managing such tensions, the Lean-Industry 4.0 integration can help improve social performance, as well as develop systematic problem-solving and cumulative learning capabilities. Five important themes for this field of research are outlined: the importance of work routines, legitimation, competence, sense and mental flexibility.

This study brings a new theoretical perspective to the integration of Lean with Industry 4.0-related digital technologies. The results go beyond the usual view of improving operational performance and dig into the effects on workers. It also shows that the integration process relies on and can enhance human capabilities such as learning and problem-solving.

Small and medium-sized enterprises (SMEs) are a major source of employment and revenue growth in developing nations like India, but they also face challenges from resource shortages, shifting consumer demand and heightened competition. This research aims to discover the aspects that enhance SMEs' competitiveness and performance.

By analyzing literature and consulting experts, 10 factors that boost a firm's competitiveness were identified. The total interpretive structural modeling (TISM) method was then used to determine their interaction and structural hierarchy. Neutrosophic-MICMAC analysis was employed to assess the driving-dependence power of each factor.

The study discovered that the factor, namely “entrepreneurial orientation,” was found to be a significant one. “Manufacturing strategy” was found to be extremely dependent on the remaining competitive advantage factors.

This SME-focused framework can be adopted by large businesses to enhance organizational performance by focusing on critical factors. The study depends on experts' judgment, which might be biased. Findings will assist SMEs in identifying significant factors influencing competitive advantage and relationships, increasing awareness of factors contributing to competitive edge.

The results of the research may encourage SME sector managers and practitioners to prioritize the factors that contribute to a firm's competitive advantage.

The majority of research on SME competitive advantage focuses on individual aspects. To add to the body of knowledge on the subject, this study applies the TISM technique to Indian SMEs to identify the contextual interactions among factors that increase long-term competitiveness.

This paper examines the nexus between supply chain social capital (relational social capital and structural social capital), supply chain responsiveness (operations system responsiveness and supplier network responsiveness) and firm performance. Additionally, the study examines the mediating role of supply chain responsiveness on the relationship between supply chain social capital and firm performance.

The authors test their hypotheses on a sample of 120 firms operating in Ghana. The measurement model and hypothesized paths were assessed using partial least squares structural equation modelling.

The findings revealed that structural social capital had a significant direct effect on firm performance, but relational social capital did not. It was also revealed that both relational and structural social capital have significant effects on operations system responsiveness and supplier network responsiveness. Additionally, operations system responsiveness fully mediated the effect of relational social capital on firm performance and partially mediated the effect of structural social capital on firm performance. Supplier network responsiveness, on the other hand, partially mediated the effect of both relational and structural social capital on firm performance.

This study contributes to the limited literature on supply chain social capital by unearthing the mechanisms through which supply chain social capital enhances firm performance. Specifically, the study demonstrates the intervening role of operations system responsiveness and supplier network responsiveness in the supply chain social capital–firm performance link.

The purpose of this literature review paper is to explore the concept of organization agility and its relevance in today's dynamic business environment. By conducting an in-depth review of existing academic and industry literature on organization agility, this study aims to identify the key factors that influence an organization's agility and the benefits and drawbacks associated with fostering agility.

Through the technique of bibliometric analysis, we provide the growth trajectory of the field by identifying the publication trends, prominent authors and countries and most prolific journal publishing in the concerned domain. We also provide the intellectual structure of the organization agility research by identifying the prominent themes that have been worked upon till date. In addition, with the backing of the theories, contexts, characteristics and methodology (TCCM) framework, we identify the most frequently applied theories, constructs and methods in organization agility research and provide new avenues for future research by analyzing the most frequently used theories, methods, constructs and research contexts.

With the ever-increasing ambiguity and need for change (why), organization agility serves as the organization's backbone. It acts as a springboard for the organization, an anchor point that remains constant while other functional aspects constantly fluctuate and change. Organization agility can be defined (what) as the ability of organizations to quickly respond to market needs by sensing, renewing, adapting and succeeding in a turbulent market. To summarize, organizational agility matters at three fundamental aspects (where): strategic level or the market capitalizing level, internal operational level and individual level.

This paper is unique in the sense that it is the first comprehensive literature review in the field of organization agility research to use a hybrid methodology (bibliometric review with TCCMs).

This research analyses the impact of customers and government regulations on the implementation of socio-environmental practices in certifying organic agricultural products. It explores the dyad’s relationship between the focal company and its suppliers in the application of socio-environmental practices.

This study uses a quantitative methodology through a survey approach, with a sample of 206 agro-extractivists from the acai berry supply chain. The data are evaluated using regression analysis.

The main results reveal that customer pressure positively influences the implementation of social and environmental practices, but suggest a non-significant relationship between government regulations and the impact on environmental practices implementation. Social and environmental practices are positively related to operational performance. A moderating effect of organic certification is found in the relationship between customer pressure and the application of environmental practices.

The main contributions are exploring the use of socio-environmental practices in an emerging economy and organic certification as a moderating variable, revealing an “institutional void” that may hamper the enforcement of government regulations.

The rise of emerging economies in the innovation landscape has often been attributed to the positive spillovers of innovation capabilities from multinational corporations (MNCs). However, it is less certain that their innovative capabilities imported from the home country function effectively in the host country from the outset. This study examines the performance of the innovation capabilities of MNC subsidiaries in emerging economies over time by considering the gradual process of their learning about host countries.

We employed stochastic frontier analysis to measure innovation capabilities, our focal construct. For regression analysis, we applied the Mundlak estimator, a variant of the fixed-effects panel estimator, to a sample comprising subsidiaries of MNCs from technologically advanced nations operating in Korea between 2006 and 2016.

Our results indicate that the innovation capabilities of MNC subsidiaries initially underperform those of local firms but improve over time, eventually surpassing the capabilities of their local counterparts. Furthermore, our findings reveal that institutional distance amplifies the underperformance of the innovation capabilities of MNC subsidiaries.

This study contributes to the literature by extending both theoretical development and empirical measurement of innovation capabilities in cross-national settings. Additionally, it deepens our understanding of whether and how MNC subsidiaries adapt their innovation capabilities to the local market environment.

Despite the growing interest regarding the Brazilian rice industry, there is not much literature focusing on the role of the institutions in the process of technological capability accumulation and in the formation of the technological trajectories within this industry. This paper aims to discover the role of local institutions in the generation and dissemination of knowledge for creating the technological capability that can define different technological trajectories, using the Brazilian rice industry as an empirical field.

To achieve said objective, this paper uses secondary data (documental research) and a multiple case study design based on primary empirical evidence (content analysis and direct observation) about the Brazilian rice industry.

The paper’s main contribution is the empirical application of a framework that allows us to evaluate the institutions’ roles and activities and how these capabilities evolve as the firms’ technological levels progress and the technological trajectory is formed. Regarding aspects related to public policy, the authors found some implications that are mainly related to the need to consolidate this type of institution in developing countries with the goal of strengthening its technological capabilities, allowing these countries to operate on the technological boundary and to compete with developed countries.

There are few attempts to relate the technological capability, technological trajectories and institutions in the Brazilian rice industry. Therefore, to the best of the authors’ knowledge, the novelty of this study lies in the analysis of these theoretical approaches in this industrial sector, more specifically, in the Brazilian rice industry.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

The research aims to explore the relationships between the digital capabilities of micro, small and medium enterprises (MSMEs), their participation in global value chains and their adoption of innovative business models. Additionally, the study investigates how the prior experiences of entrepreneurial failure may influence or moderate these relationships.

To address these research objectives, the study draws upon data obtained from the Flash Eurobarometer 486 survey, a comprehensive dataset that explores the challenges faced by 13,197 European MSMEs as they navigate the complexities of growth, the integration of sustainable business models and the incorporation of digital technologies. To test the proposed hypotheses, the research employs multivariate logistic regression analysis.

Digital capabilities are found to be positively associated with business model innovation, while integration into global value chains is linked to a higher likelihood of implementing new business models. Interestingly, entrepreneurial failure did not significantly influence the relationship between digital capabilities and the adoption of new business models. In contrast, entrepreneurial failure significantly moderated the impact of global value chain inclusion on business model innovation, particularly in MSMEs with a history of failure.

This article provides practical guidance to entrepreneurs and companies interested in enhancing their digital strategies and engagement in global value chains, considering the entrepreneurs' business histories.