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This paper aims to explore the growth of the South African additive manufacturing (AM) industry over the past 31 years through the lens of the innovation system (IS) perspective, examining the actor dynamics and mechanisms that facilitated or hindered the industry’s development.

The study used a case study research approach, analysing semi-structured interviews with eight South African AM experts and documentary evidence. The IS framework and the realist evaluation perspective were used, using a context-intervention-mechanism-outcome (CIMO)-based event history analysis (EHA) framework to explore the actor dynamics and mechanisms of the case study.

The study used a case study research approach, analysing semi-structured interviews with eight South African AM experts and documentary evidence. The IS framework and the realist evaluation perspective were used, using a CIMO-based EHA framework to explore the actor dynamics and mechanisms of the case study.

This paper contributes to the South African AM industry literature by providing an overview of the industry events over the past three decades and analysing the industry through the IS framework. The study is among the first to analyse the development of the South African AM industry, presenting innovation scholars and managers with valuable decision-making support by providing insights into the innovation activities performed during each stage of the industry’s development, who performed them, the sequence in which they were performed and the outcomes they delivered.

The purpose of this study is to demonstrate the effect of initial international joint ventures (IJV) structural conditions on two main equity-based instability facets: change of IJV ownership structure and acquisition of the IJV by one of the IJV partners. Drawing on the transaction cost theory, the authors examine three key initial structural conditions: IJV formation mode, number of partners and IJV’s ownership structure.

The authors apply the “Event history analysis” technique to test the hypotheses using a data set of 140 French-foreign JVs.

The findings show that the mode of an acquisitive IJV and unequal equity positions held by partners increase the likelihood of a change of IJV’s ownership structure and its eventual acquisition by one of the partners. In addition, the findings show that while an increase in the number of IJV partners is directly related to the change of IJV ownership structure, it has a statistically insignificant effect on IJV acquisition.

Drawing on “transaction costs” arguments, this study advances the literature by offering fine-grained results related to the effects of initial structural conditions on aspects of unintended instability in French-foreign JVs.

Studies of unicorns and gazelles can offer detailed information about the process of enterprise development but are unrepresentative as examples of entrepreneurial success. In presenting a novel method for outlier analysis, this article combines insights from case studies of unusual organizations with explanatory frameworks that management scholars have applied to broader samples of firms, irrespective of their survival.

The authors illustrate the approach to outlier analysis using a prominent case from economic history: the House of Rothschild, founded during the 18th century, which became the most famous investment bank in Europe. Following the iterative refinement of mechanisms using comparison data on Jewish enclave firms, this analysis sheds light on the sources of dissimilarity in outcomes between Rothschild and the comparison group.

The study results suggest that the House of Rothschild's longevity can be explained via the mechanisms of risk sequencing, intergenerational transfers and spatial brokerage. The authors show that these mechanisms are not idiosyncratic to one enterprise but instead generalize to other family firms.

Outlier analysis encourages a rapprochement between case study and large-N research. The high failure rate of new organizations means that those yielding a large amount of information to researchers tend to be exceptional. By obtaining data on a comparison group of startups founded by similar entrepreneurs, analysts can probe the mechanisms of success identified for unicorns or gazelles.

The high turnover of new graduate employees has become a concern for many organizations in Korea. This study explores when new graduate employees leave first jobs and what makes these employees decide to leave employees' organizations.

Using national panel data from South Korea, the authors employed a survival analysis and examined the factors that explain the turnover of new graduate employees.

The findings of this study reveal that many new graduate employees leave the employees' organizations within two years. Moreover, work conditions, work satisfaction and job-skill match were associated with new graduate employee turnover.

Based on the results of survival analysis derived from actual turnover data, not turnover intentions, the authors emphasize appropriate human resources (HR) intervention, a working environment and organizational culture, and employee development opportunities.

The authors enrich and extend the existing institutional anomie theory (IAT) in the hope of sharpening the understanding of the joint effects of selected cultural values and social institutional changes on women's pre-entrant entrepreneurial attempts. The authors theorize that women are culturally discouraged to pursue pre-entrant entrepreneurial attempts or wealth accumulation in a specific culture. This discouragement creates an anomic strain that motivates women to deviate from cultural prescriptions by engaging in pre-entrant entrepreneurial attempts at a faster speed. Building on this premise, the authors hypothesize that changes in social institutions facilitate the means of achievement for women due to the potential opportunities inherent in such institutional changes.

Using a randomly selected sample of 1,431 registered active individual users with a minimum of 10,000 followers on a leading entertainment live-streaming platform in the People's Republic of China, the authors examined a unique mix of cultural and institutional changes and their effects on the speed of women's engagement in live-streaming platform activity.

The authors find support for the impact of the interaction between changes in social institution conditions and cultural values. Unexpectedly, the authors also find a negative impact of cultural values on women's speed of engaging in pre-entrant entrepreneurial attempts.

The authors add institutional change to the IAT framework and provide a novel account for the variation in the pre-entrant entrepreneurial attempts by women on the platform.

Mobile apps usually have a short lifespan, and this prevents the majority from surviving long enough to generate revenue for their developers. To address this issue, this study aims to recognize the role of engagement and immersion and develops a cognitive-affective theoretical framework to associate these factors with the lifespan of an app. Moreover, the authors focus on gaming apps because of their dominance on mobile platforms, and because a few of them become lucrative while the majority perish within a few weeks.

This study uses a longitudinal survey-based approach to collect data on one of the popular gaming apps (Pokémon Go), which received international attention. The design focuses on identifying factors that extend the lifespan of the app and affects users’ decision to continue or stop using the app over time. The authors use the survival analysis and structural equation modeling to analyze the theoretical framework.

The authors find the centrality of the users’ experience of immersion to extend the mobile app lifespan. Engagement influences immersion and immersion predicts users’ decision to continue or stop using the app. Users’ experience of immersion increases the probability of the apps’ survival by 12%.

To the best of the authors’ knowledge, this is the only study that proposes a theoretical framework, meticulously investigates the factors that can lead to survival of a mobile app and uses longitudinal data for the empirical analysis.

This study aims to develop a model of learning-by-hiring in which knowledge gains may occur at the time of recruitment but also after recruitment when other incumbent organizational members assimilate a recruit’s knowledge. The author’s model predicts that experienced recruits are more likely to catalyze change to their organization’s core technological capabilities.

The continuous-time parametric hazard rate regressions predict core technological change in a long panel (1970–2017) of US biotechnology industry patent data. The author uses over 140,000 patents to model the evolution of knowledge of over 52,000 scientists and over 4,450 firms. To address endogeneity concerns, the author uses the Heckman selection method and does robustness tests using a difference-in-difference analysis.

The author finds that a hire’s prior research and development (R&D) experience helps overcome inertia arising from her or his new-to-an-organization “distant” knowledge to increase the likelihood of core technological change. In addition, while the author finds that incumbent organizational members resist technological change, experienced hires may effectively induce them to adopt new ways of doing things. This is particularly the case when hires collaborate with incumbents in R&D projects. Understanding the effects of hiring on core technological change, therefore, benefits from an assessment of hire R&D experience and its effects on incumbent inertia in an organization.

First, the author does not recommend managers to hire scientists with considerable distant knowledge only as this may be detrimental to core technological change. Second, the author recommends organizations striving to effectuate technological change to hire people with considerable prior R&D experience as this confers them with the ability to influence other members and socialize incumbent members. Third, the author recommends that managers hire people with both significant levels of prior experience and distant knowledge as they are complements. Finally, the author recommends managers to encourage collaboration between highly experienced hired scientists and long-tenured incumbent organizational members to facilitate incumbent learning, socialization and adoption of new ways of doing things.

This study develops a model of learning-by-hiring, which, to the best of the authors’ knowledge, is the first to propose, test and advance KM literature by showing the effectiveness of experienced hires to stimulate knowledge diffusion and core technological change over time after being hired. This study contributes to innovation, organizational learning and strategy literatures.

The purpose of this paper is to show that research on policy diffusion mechanism of Government Procurement of Public Services (GPPS) is beneficial to improve the efficiency of policy formulation and implementation.

In view of the four dimensions which are internal demand, external pressure, policy innovation environment and service characteristic, a system of factors affecting policy diffusion is established. On this basis, a Multilayer Fuzzy Cognitive Map (MFCM) model for policy diffusion of GPPS is constructed. Nonlinear Hebbian Learning algorithm and genetic algorithm are applied to optimize the two components of the MFCM model, which are relationship between nodes at the same layer and influence weights between nodes at different layers, respectively. Taking Nanjing municipal government purchasing elderly-care services in China as the empirical object, simulation of policy diffusion based on the MFCM model is carried out, aiming to obtain the key factors influencing policy diffusion and the dynamic diffusion mechanism of GPPS policy.

Research results show that, compared with monolayer Fuzzy Cognitive Map, the MFCM model converges faster. In addition, simulation results of policy diffusion indicate that economic development level of jurisdiction, superior pressure, administrative level and operability of services are key influencing factors which are under four dimensions correspondingly. And the dynamic influencing mechanism of key factors has also been learned.

This paper constructs the MFCM model, which is a new approach based on several monolayer FCMs, to study the policy diffusion mechanism.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

This paper aims to investigate the divestment behavior of emerging market multinationals from Latin America – multilatinas – by examining how their foreign market entry decision impacts the likelihood of subsidiary divestment.

The hypotheses are tested using Cox’s proportional hazard rate model in a longitudinal database of Brazilian multinational companies established in 43 countries.

Results indicate that these subsidiaries can thrive in environments that bear similarities to their home country, being less likely to divest in institutionally weak countries. Contrary to developed country multinationals, these firms benefit from foreign entry decisions that entail handling partnerships abroad; thus, wholly-owned greenfield (WOGF) investments have a higher likelihood of being divested.

To the best of the authors’ knowledge, this paper is the first to analyze foreign divestment from multilatinas, accounting for how entry mode strategy and host country institutions may impact these firms’ de-internationalization.