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Drawing from the theory of reasoned action, this study investigated the moderators of the relationship between turnover intentions and performance at work.

This study employed hierarchical multiple regression to test three proposed hypotheses regarding the above relationship. It used 1,011 dyad data from employees and their supervisors from eight professional organizations in Vietnam from employees and their supervisors to reduce research bias.

Employee attitude toward change and the level of job engagement of the employee affected the nature of the relationship between turnover intentions and job performance. When the attitude toward change was less favorable, the relationship between turnover intentions and job performance was positive. However, when the attitude toward change was more favorable, the relationship between turnover intentions and job performance was non-significant. For the moderating role of job engagement, we found that for employees with a high level of job engagement, the relationship between turnover intentions and job performance was positive. However, for employees with a low level of job engagement, the relationship between turnover intentions and job performance was non-significant.

Unlike the implications from previous research, turnover intentions of employees might not adversely affect their performance. Under two conditions – a high level of job engagement and a less favorable attitude toward change - employees with turnover intentions might actually perform better.

Unlike the vast number of studies that have investigated the relationship between job performance and turnover intentions (as a proxy of turnover), this paper focuses on the relationship between turnover intentions and job performance to show evidence for two important boundary conditions.

The market-based monetary policy framework has been favoured by Economic Community of West African States (ECOWAS) economies. Hence, this study aims to investigate the effect of monetary policy channels on the sectoral value added and sustainable economic growth in ECOWAS. Data from the World Bank and International Monetary Fund over 2013–2019 were sourced for thirteen member countries. ECOWAS is found to have very high inflation level, interest and exchange rates.

The study adopted the Driscoll–Kraay fixed-effects ordinary least squares regression (OLS) estimator.

The findings revealed that while the effect of monetary policy channels on the agricultural sector value added is largely heterogenous and significantly in-elastic, the one on the industrial and services sectors are overwhelmingly homogeneous and negative, but insignificant for the services sector. Moreover, the effect of monetary policy channels on sustainable economic growth is also homogeneously asymmetric, with imminent stagflation, while the interactive effects of monetary policy channels are heterogeneous on sustainable economic growth and economic sectors. Therefore, an inflation targeting monetary policy stance is generally recommended with prioritised exchange rate stabilisation amid sufficient fiscal space.

This is amongst the first studies to investigate monetary policy channels, sectoral outputs and sustainable growth in the ECOWAS region with a rigorous analysis and found implications for policy.

Attaining the Sustainable Development Goal 2 (SDG2) of zero hunger continues to be a challenge in most parts of Sub-Saharan Africa. However, financial inclusion is seen as a potential pathway for reducing food insecurity among poor households. Mobile money is a financial inclusion instrument that is easily accessible to poor households and has the potential to increase the level of financial inclusion. This paper contributes to the literature by examining the determinants of mobile money adoption, its effects on household food security and the choice of coping strategies in Burundi, a post-conflict and fragile country.

Using survey data that involved 860 households in Burundi, we adopted the Household Hunger Scale (HHS) developed under the Food and Nutrition Technical Assistance Project to measure household food security. We further employ the endogenous switching regression treatment effects model for ordered outcomes and the multivariate probit model to achieve our aims.

The results of our study reveal that the adoption of mobile money is influenced by factors such as gender, marital status, age, formal education, membership in a social network, area of residence and access to a tarred road network. Additionally, the food security status of a household was determined by marital status, formal education, social network membership, access to tarred roads, off-farm income, access to credit and land tenure security. We confirm that mobile money adoption has a significantly positive effect on the food security status of households with heterogeneity in gender and area of residence. We also find that mobile money adoption reduces the likelihood of households adopting consumption-related coping strategies.

The promotion of mobile money should, therefore, be included in Burundi’s national food security policies.

This study contributes to the literature by providing empirical evidence on the effect of mobile money adoption on household food security and the choice of coping strategies in a post-conflict context.

Artificial intelligence (AI) has the potential to dramatically change the human approaches to work, and specifically to learning and development. While AI coaching can reduce costs and increase accessibility, it also presents both opportunities and threats to human coaches. The objective of this study was to conduct a systematic literature review of peer-reviewed research on the use of AI in coaching.

A systematic literature review (SLR) method was used to search eight databases for articles produced up to March 2024. Data extraction was conducted, with Quality Assessment undertaken independently, in parallel, using two researchers and a third arbiter. The ROBINS-I tool was used to assess the risk of bias in the included studies. A narrative synthesis of a total of 16 quantitative, qualitative or mixed-method studies covering n = 2312.

The SLR identified four key themes: Research design and AI integration, AI usefulness in coaching, impact of AI coaching and ethical considerations. The findings suggest that AI coaches can be effective, accepted, useful and match human coaches in competence for specific tasks.

AI coaching is a growing area of practice and research. This paper brings together the literature and identifies future research priorities and potential next steps in AI coach development.

The paper uses clinical research SLR methods applying these robust processes to the field of organisational research, to set a new standard through the use of a pre-determined research protocol, quality assessment and ROB, well providing a comprehensive literature review of AI coaching.

The purpose of this paper is to evaluate the existing scenarios for 3D printing (3DP) in order to identify the “white space” where future opportunities have not been proposed or developed to date. Based around aspects of order penetration points, geographical scope and type of manufacturing, these gaps are identified.

A structured literature review has been carried out on both academic and trade publications. As of the end of May 2016, this identified 128 relevant articles containing 201 future scenarios. Coding these against aspects of existing manufacturing and supply chain theory has led to the development of a framework to identify “white space” in the existing thinking.

The coding shows that existing future scenarios are particularly concentrated on job shop applications and pull-based supply chain processes, although there are fewer constraints on geographical scope. Five distinct areas of “white space” are proposed, reflecting various opportunities for future 3DP supply chain development.

Being a structured literature review, there are potentially articles not identified through the search criteria used. The nature of the findings is also dependent upon the coding criteria selected. However, these are theoretically derived and reflect important aspect of strategic supply chain management.

Practitioners may wish to explore the development of business models within the “white space” areas.

Currently, existing future 3DP scenarios are scattered over a wide, multi-disciplinary literature base. By providing a consolidated view of these scenarios, it is possible to identify gaps in current thinking. These gaps are multi-disciplinary in nature and represent opportunities for both academics and practitioners to exploit.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

Chinese consumers’ cross-border internet shopping, so called “haitao” is an emerging popular trend in China. Haitao can be understood as service innovation process because it creates new market spaces and provides differentiated values for Chinese customers.

This study aims to explore the service innovation strategies and success factors of haitao business in the Chinese market. The authors selected two successful haitao sites of Amazon.cn and Gmarket.co.kr, as representatives of a global player and a niche player, and conducted a comparative case study to analyze their service innovation strategies and key success factors.

This comparative case analysis based on value chain framework revealed some common success factors such as trust, advanced system and alliances as well as their service innovation efforts. Amazon has advantages such as efficient logistics system and global sourcing, whereas Gmarket has advantages such as product category, sales promotions, and payment system.

This study provides some implications for managers with localization, alliances and platform strategies.

Human or machine, which one is more intelligent and powerful for performing computing and processing tasks? Over the years, researchers and scientists have spent significant amounts of money and effort to answer this question. Nonetheless, despite some outstanding achievements, replacing humans in the intellectual tasks is not yet a reality. Instead, to compensate for the weakness of machines in some (mostly cognitive) tasks, the idea of putting human in the loop has been introduced and widely accepted. In this paper, the notion of collective hybrid intelligence as a new computing framework and comprehensive.

According to the extensive acceptance and efficiency of crowdsourcing, hybrid intelligence and distributed computing concepts, the authors have come up with the (complementary) idea of collective hybrid intelligence. In this regard, besides providing a brief review of the efforts made in the related contexts, conceptual foundations and building blocks of the proposed framework are delineated. Moreover, some discussion on architectural and realization issues are presented.

The paper describes the conceptual architecture, workflow and schematic representation of a new hybrid computing concept. Moreover, by introducing three sample scenarios, its benefits, requirements, practical roadmap and architectural notes are explained.

The major contribution of this work is introducing the conceptual foundations to combine and integrate collective intelligence of humans and machines to achieve higher efficiency and (computing) performance. To the best of the authors’ knowledge, this the first study in which such a blessing integration is considered. Therefore, it is believed that the proposed computing concept could inspire researchers toward realizing such unprecedented possibilities in practical and theoretical contexts.

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.

The purpose of this study is to shed light on the relationship between patent applications and long-term risk for small firms across the global financial crisis of 2008. During a crisis, firm risk often skyrockets, and small and medium enterprises face significant dangers to their business continuity. However, managers have a set of strategies that could be implemented to increase a firm’s resilience, sustaining competitive advantages and improving access to financial resource. The authors focused on the investigating the impact of patenting activities on small business risk in a time of crisis.

This is a quantitative study based on a sample of Italian firms that applied for a patent in 2005. The changes in corporate credit ratings over a five-year period are related to different proxies of patent activity using multivariate regression analysis.

Firms that filed for a patent were more resilient, compared to the control sample, during the financial crisis. Innovative activities resulting in patent application seem to deliver strategic resources useful to tackle the crisis rather than increase riskiness. The moderating effect of patents on risk sensitivity is stronger for small firms and when the number of patents or the patent intensity is larger.

Limited evidence is available on how patent applications are related to risks for small firms during an economic crisis. The authors highlight that the innovative efforts resulting in patent applications can support small business resilience. The authors also point out that the implementation of patent information in small firms' credit score modeling is still an uncommon practice, while it is useful in estimating firm risk in a way more robust to exogenous credit shocks.