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Coaching is a widespread form of human development that has grown considerably in recent years. However, it is not well understood in entrepreneurship and small and medium-sized enterprises (SMEs) and little is known about the success factors for coaching in SMEs. Thus, this article presents a theoretical framework for coaching SMEs. The paper reports on a study carried out to develop and validate a coaching model for entrepreneurship in SMEs.

A mixed methods approach was undertaken in SMEs in the Iranian pharmaceutical industry. Confirmatory factor analysis (CFA) was used to confirm the samples and the model's dimensions.

The results show five chief constructs of the entrepreneurial coaching model. In particular, the authors determine the importance of early goal setting and identify the essential characteristics of an effective entrepreneurial coach.

Firstly, the data relied solely on the pharmacy industry in Iran, indicating a need for future studies to explore coaching programs across various industries and countries. Additionally, a quantitative aspect of the research involved participants answering questionnaires based on their perceptions. This subjective nature introduces a potential for inaccuracies in participants' perceptions and expectations. Furthermore, the inherent bias of program stakeholders may have led to exaggerated responses. To mitigate these issues, it would be beneficial to conduct experimental and longitudinal research, which could address these concerns more effectively.

By utilizing a theoretical framework, the authors goal is to define the essential features of coaching in SMEs and compare it to other developmental interventions to highlight both commonalities and distinctions. This approach addresses the recent suggestions in coaching literature to distinguish coaching practices tailored for particular groups, specifically entrepreneurs involved in SMEs.

This study contributes to understanding the essential features for successful entrepreneurial coaching in SMEs.

The purpose of this paper is to better understand and study the architecture and system characteristics of the underlying support platform for crowd system, by recognizing the characteristics of service internet is similar to the coordination characteristics between the massive units in the underlying platform of crowd system and studying the form, nature and guidelines of the service internet.

This paper points out the connection between the underlying support platform of crowd system and service internet, describes the framework and ideas for researching service internet and then proposes key technologies and solutions for service internet architecture and system characteristics.

The research unit in the underlying support platform of crowd system can be regarded as a service unit. Therefore, the platform can also be regarded as service internet to some extent. The ideas and technical approaches for the study of service internet’s form, criteria and characteristics are also provided.

According to this paper, relevant staff can be guided to better build the underlying support platform of crowd system. And it can provide a highly robust and sustainable platform for research studies of crowd science and engineering in the future.

The purpose of this paper is to explore the increased research attention gained by sustainability in food supply chain management. Although previous review studies have focused on aspects such as traceability, food safety, and performance measurement, sustainability has rarely been considered as a means of integrating these issues.

The paper presents a comprehensive review of the literature on food sustainable supply chain management (FSSCM). Using systematic review methods, relevant studies published from 1997 to early 2021 are explored to reveal the research landscape and the gaps and trends.

The paper shows the building blocks and the main research directions in FSSCM, particularly considering the opportunities in “neglected” emerging countries. Insights are provided into the various elements of the sustainability supply chain in the food industry, which have previously been analysed separately.

Only a few researchers have systematically reviewed the literature or taken a bibliometric approach in their analyses to provide an overview of the current trends and links between sustainability and food supply chain management.

Written communication differences across cultures can set the tone for effective or disastrous business relationships. Although English has been the go-to language in business, managers from different countries can significantly differ in how they convey the firms' information. This study explored these differences by examining the documentation presented by foreign corporations as part of their initial public offering (IPO) in the USA, particularly Chinese firms.

This work examined cultural-related differences in written communications by looking at foreign corporations' descriptions of their strengths, strategies and challenges included in F-1 documents submitted to the Securities and Exchange Commission as part of the IPO process. The sample consisted of 97 American depositary receipts (ADRs) identified in the Bank of New York Mellon's ADR directory from 2003 to 2015.

This study found that Chinese firms significantly differ from other countries' firms in depicting their strengths, strategies and challenges.

Limitations have to do with the sample size. Future research may address this by considering other depositary markets, not just the USA.

The results will be significant for potential ADRs investors; they must be conscious of these differences in the written documentation submitted by Chinese firms compared to other foreign firms. The market should also be aware of these differences, as the Chinese seem less open to sharing information about the under spinning of their operations and financial prospects.

This study aims to explore the relationship between chief executive officer (CEO) power and stock price crash risk in India. Furthermore, it seeks to analyse how insider trades may moderate the impact of CEO power on stock price crash risk.

A study of 236 companies from the S&P BSE 500 Index (2014–2023) have been analysed through pooled ordinary least square (OLS) regression in the baseline analysis. To enhance the results' reliability, robustness checks include alternative methodologies, such as panel data regression with fixed-effects, binary logistic regression and Bayesian regression. Additional control variables and alternative crash risk measure have also been utilised. To address potential endogeneity, instrumental variable techniques such as two-stage least squares (IV-2SLS) and difference-in-difference (DiD) methodologies are utilised.

Stakeholder theory is supported by results revealing that CEO power proxies like CEO duality, status and directorship reduce one-year ahead stock price crash risk and vice versa. Insider trades are found to moderate the link between select dimensions of CEO power and stock price crash risk. These findings persist after addressing potential endogeneity concerns, and the results remain consistent across alternative methodologies and variable inclusions.

This study significantly advances research on stock price crash risk, especially in emerging economies like India. The implications of these findings are crucial for investors aiming to mitigate crash risk, for corporations seeking enhanced governance measures and for policymakers considering the economic and welfare consequences associated with this phenomenon.

This paper aims to present a detailed mechanical design of a seven-degrees-of-freedom mobile parallel robot for the tungsten inert gas (TIG) welding and machining processes in fusion reactor. Detailed mechanical design of the robot is presented and both the kinematic and dynamic behaviors are studied.

First, the model of the mobile parallel robot was created in computer-aided design (CAD) software, then the simulation and optimization of the robot were completed to meet the design requirements. Then the robot was manufactured and assembled. Finally, the machining and tungsten inert gas (TIG) welding tests were performed for validation.

Currently, the implementation of the robot system has been successfully carried out in the laboratory. The excellent performance has indicated that the robot’s mechanical and software designs are suitable for the given tasks. The quality and accuracy of welding and machining has reached the requirements.

This mobile parallel industrial robot is particularly used in fusion reactor. Furthermore, the structure of the mobile parallel robot can be optimized for different applications.

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement.

A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.

The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy.

A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

When the management of an information technology (IT) manufacturing firm perceives a need for innovation due to any threat in the external environment, it will be prompted to use organizational resources to support innovation and improve organizational performance through the implementation of the innovation. The purpose of this paper is to explore whether an IT manufacturing firm’s budget slack, information quality of information system (IS), process innovation and product innovation would interact to collectively form an innovation capacity, which is termed “innovation capability configuration (ICC)”, and whether ICC mediates the relationship between perceived innovation requirement and organizational performance.

To answer these questions, a structural equation model was built and a questionnaire survey was conducted to collect data from research and development and production managers of IT manufacturing companies listed on the Taiwan Stock Exchange and Over-The-Counter markets.

The results showed that budget slack, IS information quality, process innovation and product innovation are all significantly related to ICC, in which high-quality information and low level of budget slack are the key factors that underpin the innovation capacity. In addition, ICC has a full mediation effect, that is, perceived innovation requirement positively influences ICC, which, in turn, improves organizational performance.

Because all items in a questionnaire were answered by a manager, the common method variance might exist in this study. In addition, the effective recovery rate of the questionnaire was not high due to which the non-response bias might occur. Following the research limitations, several future research recommendations are proposed.

This study offers managerial implications for the development of an IT manufacturing firm’s innovation strategy and structure to smooth the implementation of innovation in the severe environment.

The study is the first attempt to integrate the four elements clearly illustrating the ICC, which is a more complete innovation strategy, thus contributing to improve the past fragmental studies and clarify some controversial points existing in the extant innovation research.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.