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Prior research from transaction costs economics argued that central firms perform better because they have superior access to information to discipline their alliance partners. Central firms may also, however, face higher costs and risks of unintentional learning and weaken their competence through structural inertia. We propose that these costs and risks are influenced by the learning capacities of the firms in the network and can explain different outcomes for focal firm performance.

To test our predictions, we use instrumental variable–generalized method of moments estimation techniques on 15,517 firm-year observations from equity alliance portfolios in the global food industry across a 21-year window.

We find support for our predictions and show that the relationship between network degree centrality and firm performance is negatively influenced by partners’ learning capacity and positively influenced by focal firms’ learning capacity, while firms with low network degree centrality benefit less from their learning capacity.

Future developments in transaction cost economics may consider partner and focal firms’ learning capacity as moderators of the network degree centrality – firm performance relationship.

In alliance decisions, managers must consider that the combination of high network degree centrality and partners’ learning capacity can lead to high costs, risks of unintentional learning, and structural inertia, all of which have negative consequences for performance. In concentrated industries where network positions are controlled by a few large firms, policymakers must acknowledge that firms may face substantial barriers to collaboration with learning-intensive firms.

This study is the first to develop and test a comprehensive transaction cost analysis of the central firm’s unintended knowledge flows and structural inertia in alliance networks. It is also the first to incorporate theoretically and empirically the hazards of complex and unintended information flows on the relationship of network degree centrality to performance in equity alliance portfolios.

This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

This study aims to examine the share of foreign direct investment (FDI) in creating the value added (VA) of innovative and other industries in Poland in 2004–2020.

In terms of the empirical analysis of FDI stocks, their locations were divided into innovative and other industries. The differences in the creation of VA are presented by domestic and foreign enterprises. The impact of FDI stocks in individual industries on gross domestic product (GDP) changes was assessed using the vector error correction model (VECM).

FDI from innovative industries generated approx. 7% VA of the Polish economy in the years 2004–2020. In 2009–2018, the share of VA of foreign enterprises in innovative industries in Poland showed a faster growth (by 5 pp) than in other industries. The results of decomposition confirm that the level of explanation of GDP by FDI in innovative industries is higher than in other industries.

Changes in the classification of activities reduce the time series period available.

This study explains the participation of foreign and domestic enterprises in creating VA. The results are useful to pursuing the national investment policy.

The economic results of domestic and foreign enterprises in the host country affect the economic growth and development and ultimately the socio-economic conditions of life.

This work provides some additional explanations for the inconclusive results of international research into the impact of FDI on GDP or the spillovers effects. Its usefulness concerns the detailed impact of FDI by industrial structures on GDP.

Prior research has extensively explored the dynamics of knowledge creation and transfer within entrepreneurial ecosystems (EEs). However, the research on knowledge integration within EEs, particularly by entrepreneurs, remains scant. Garnering and effectively using knowledge in such a dynamic and complex environment can provide entrepreneurs with a valuable asset for gaining a competitive advantage. To address this gap, this study aims to explore how entrepreneurs garner and capitalise on knowledge within the EE environment by using a transactive memory system lens.

This study is based on 26 semi-structured interviews with different actors and members of the same ecosystem – the northern Finnish health tech ecosystem. The data were analysed using the Gioia methodology.

This study results found that transactive memory processes (i.e. knowledge specialisation, credibility and coordination) and structures (i.e. differentiated-, shared- and meta-knowledge) influence knowledge organising in EEs.

This study provides a conceptual interplay between the EE and the transactive memory system’s processes and structures.

This study aims to explore the spatial impact of an increase in the minimum wage on the labor productivity of star-rated hotels in China.

The impact is analyzed by using the dynamic spatial Durbin model.

The authors find a U-shaped link between the increase in minimum wage and labor productivity of star-rated hotels. The long-term impact of a minimum wage increase has a greater influence on labor productivity than its short-term effects. While there is no notable spatial spillover impact observed in the sample of 31 provinces in China, the authors do identify a spatial spillover effect of the minimum wage rises on the labor productivity of star-rated hotels in the central area. Furthermore, they observe heterogeneity across China. The eastern and western regions exhibit a U-shaped relationship, whereas the central region exhibits an inverted U-shaped relationship.

The findings of this study allow government agencies to get a more comprehensive comprehension of the actual consequences of minimum wage hikes on the tourism and hospitality sector, thereby establishing a solid basis for them to develop appropriate policies. Moreover, it offers a variety of suggestions aimed at enhancing the quality and efficiency of hotel management.

Research on the effects of minimum wage standards is scant in the hospitality industry. Based on human capital investment theory, this study examines the effect of the minimum wage standard hikes on labor productivity of star-rated hotels from the spatial perspective, filling the existing research gap.

The purpose of this study is to explore the means by which exporters foster innovation via the learning-by-exporting effect and to appraise the moderating role of employee human capital in the export–innovation relationship.

Leveraging the linked-survey-secondary data from the Human Capital Corporate Panel (HCCP) spanning 2011–2017, with 890 observations from 228 Korean exporters, this study utilizes Generalized Least Squares (GLS) regression to empirically test the proposed hypotheses.

The results indicate that exporting significantly boosts a firm’s innovation performance by encouraging the generation of new concepts in products, services, technologies and/or production lines. Moreover, the presence of international talent and highly educated staff positively moderates the relationship between export intensity and innovation performance.

By integrating organizational learning and human capital theories, this study yields theoretical and managerial insights by elucidating the roles of exporting and human capital in advancing innovation performance, thereby guiding corporate export strategies and human resource policies.

Some existing studies have begun to discuss how trade will change the environment from a country or province perspective. However, so far, only a limited number of studies have provided evidence at the product level. This study aims to investigate the environmental impacts of trade at the product level.

The effects of importing intermediates and capital inputs on energy performance are examined using theoretical analysis. Empirical analyses are conducted using data on product trade, and the effects of importing intermediate inputs and capital inputs on energy efficiency are identified using a Propensity Score Matching-Difference in Difference (PSM-DID) estimation.

The results demonstrate that importing intermediates and capital inputs effectively enhance energy efficiency. Importing these inputs from foreign markets leads to increased productivity and ultimately improves energy performance.

This research provides new evidence on the relationship between importing and energy use at the product trade level. It offers insights into enterprise behaviors regarding importing intermediates and capital inputs, contributing to a deeper understanding of the environmental effects of trade. Additionally, a micro-theoretical model is developed to examine the impacts of imports on energy efficiency, complementing existing literature with theoretical insights.

This paper tries to retrospect the mounting application of machine learning (ML) and artificial intelligence (AI) in the human resource management area. The document applies bibliometric analysis and uses relational techniques to explore dimensions of documents in the field. The results highlight publication trends, most impactful authors, countries and institutes in the research area. The science mapping along with co-citation and bibliometric coupling analysis revealed major developments in the field. The thematic mapping and trend analysis highlighted the past and emerging trends towards significant and impactful research in the areas of robotics, big data, AI and data analytics. This paper sets the base for future researchers by coordinating and combining various past researches to help in understanding the evolution of ML and AI in human resource management and expansion of knowledgebase.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

Prior research has extensively examined how bringing technology from work into the non-work life domain creates conflict, yet the reverse pathway has rarely been studied. The purpose of this study is to bridge this gap and examine how the non-work use of smartphones in the workplace affects work–life conflict.

Drawing from three literature streams: technostress, work–life conflict and role boundary theory, the authors theorise on how limiting employees' ability to integrate the personal life domain into work, by means of technology use policy, contributes to stress and work–life conflict. To test this model, the authors employ a natural experiment in a company that changed its policy from fully restricting to open smartphone access for non-work purposes in the workplace. The insights gained from the experiment were explored further through qualitative interviews.

Work–life conflict declines when a ban on using smartphones for non-work purposes in the workplace is revoked. This study's results show that the relationship between smartphone use in the workplace and work–life conflict is mediated by sensed stress. Additionally, a post-hoc analysis reveals that work performance was unchanged when the smartphone ban was revoked.

First, this study advances the authors' understanding of how smartphone use policies in the workplace spill over to affect non-work life. Second, this work contributes to the technostress literature by revealing how, in specific situations, engagement with ICT can reduce distress and strain.