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This study aims to investigate the influences of perceived and preferred coaching behaviors and the discrepancy between them on job satisfaction, mediated by the quality of the relationship with the immediate supervisor and adaptive performance.

The research adopted a cross-sectional survey design. A total of 220 Korean employees, small-sized team members, were recruited from the automotive industry for the study. This research explored the relationship between perceived and preferred coaching behaviors using the Pearson correlation. Structural equation modeling was used to analyze the relationships among perceived and preferred managerial coaching behaviors, the discrepancy between them, the quality of the relationship with the immediate supervisor, adaptive performance and job satisfaction.

Perceived and preferred coaching behaviors exhibited a weak correlation. Perceived coaching behaviors indirectly influenced job satisfaction through the quality of the relationship with the immediate supervisor and adaptive performance. The discrepancy between perceived and preferred coaching behaviors directly and indirectly influenced job satisfaction via adaptive performance. However, all paths related to preferred coaching behaviors were found to be insignificant.

Although the results of this research may be generalized to the Korean automotive industry, the findings highlight perceived and preferred coaching behaviors and the discrepancy between them as independent variables. The findings shed light on the influences of managerial coaching on the quality of the relationship with the immediate supervisor within Korean workplace cultures and how coaching behaviors contribute to triggering subordinates’ adaptive performance. In addition, the study provides how managerial coaching influences job satisfaction in the workplace.

Based on the findings, an organization should cultivate self-directed learning environments to enhance employees’ adaptive performance. The coaching training session should be added to the leadership development program for new managers. Team leaders need to consider their members’ preferences during managerial coaching.

The variables, such as preferred coaching variables and the discrepancy between perceived and preferred coaching behaviors, along with the research framework, represent a novelty in managerial coaching, as well as within the Korean context.

Grounding on the goal-setting theory and flow theory, this study explored the mechanism underlying the association between transfer design (TD); identical elements and training transfer (TT). Specifically, the authors explored a moderated mediation process of trainer performance and motivation to improve work through learning (MTIWL) that has received less consideration in the TT literature.

Data were collected using the retro-perspective survey method. The first survey was administered offline (t1: the day when leadership intervention was completed. Subsequently, trainees were requested to participate in an online survey (t2: 12–14 weeks later). In all, 355 executives participated.

The results of structural equation modeling (SEM) analyses suggested that trainees’ MTIWL mediational impact between leadership intervention triggers (transfer design and identical elements), and TT was supported. In addition, the indirect impact of these variables on TT was found to be significant when the trainer had high performance than when it was low. This confirmed the trainer’s performance as a potential moderator in the TT process.

This study is limited to the exploration of leadership intervention variables on TT. The findings have implications for leadership professionals and scholars who use leadership intervention and motivation metrics to predict TT.

This study offers a moderated mediation mechanism for enhancing TT through leadership intervention triggers. The proposed conceptual model included MTIWL as mediator and trainer performance during leadership intervention as moderator.

This paper examines the mediation role of green innovation in the relationship between corporate social responsibility and environmental performance of manufacturing firms in Ghana.

The paper chose African emerging markets and surveyed managers from manufacturing firms. With 301 questionnaires qualified for this study’s final analyses, the authors adopt the multiple regression with mediation models to estimates the nexus among study variables.

Results evidence that both corporate social responsibility and green innovation has a positive and significant impact on environmental performance. Interestingly, the authors find that corporate social responsibility significantly improves environmental performance through green innovation indicating that firms could essentially build their dynamic resource and innovation capabilities in sustainability leading to enhanced environmental performance.

This paper develops a dynamic resource-based view of firm environmental performance illustrating how firms use resources to build strategic capabilities for competitive advantage, which leads to improved environmental performance. The paper highlights the mediation role of green innovation on corporate social responsibility and environmental performance relationships.

This study's results provide significant insights to owners and managers of manufacturing companies to integrate corporate social responsibility and green innovation to ensure environmental performance and sustainability. Furthermore, policy makers should encourage green innovation when design sustainable development systems in the manufacturing industry.

The paper provides a valuable model showing how green innovation mediates corporate social responsibility to improve environmental performance and build competitive advantages considering both small, medium, and large manufacturing enterprises in emerging countries.

This paper aims to investigate the interrelated role of relational commitment and dependence as drivers of key performance outcomes. Specifically, the authors provide a conceptual model of the impact of commitment on relationship value dependence and switching cost dependence. The authors further investigate how these dimensions of dependence offer differing noneconomic and economic paths to strategic and financial performance.

Survey data was collected from 296 purchasing agents across multiple industries located in the USA. The conceptual model and accompanying hypotheses were tested via partial least squares structural equation modeling.

The results show that the relational path is driven by affective and normative commitment, which are related to relationship value dependence. Conversely, calculative commitment is related to switching cost dependence. This economic path is related to both strategic and financial performance, whereas the relational path is more closely related to strategic as opposed to financial performance outcomes.

This study extends research on Business-To-Business (B2B) relationships by leveraging social exchange theory to examine the interrelated roles played by two forms of dependence on performance outcomes. Thus, the authors answer Scheer et al.’s (2015) call for research into the two distinct types of dependence – relationship value and switching cost dependence – and their roles in determining B2B relationship outcomes. The findings contribute to the literature by integrating social exchange and relationship marketing concepts to develop a dual pathway approach to B2B partnerships.

The results suggest that dependence is not necessarily negative for firms. Specifically, buyers can and do still exhibit positive performance, both strategic and financial, in relationships with suppliers even when dependent on the relationship. Regardless of whether buyers are dependent due to a relationship or economic factors, both can, in different ways, lead to positive strategic and financial outcomes. Together, the authors contribute to the understanding of B2B partnerships by offering guidelines for both buyers and suppliers in the dyad.

The authors derive a comprehensive model depicting primarily relational and economic paths to performance through different types of commitment and dependence. The authors contribute to the literature by demonstrating that relational and economic paths to success are not the same, highlighting how firms could influence performance even when the relationship is not necessarily characterized by generally positive relational benefits and behaviors.

Knowledge-intensive entrepreneurial firms (KIE) strongly rely on scientific and strategic research and development (R&D) capabilities to achieve higher performance levels. Hence, the purpose of this paper is to disentangle the effects of scientific capabilities and strategic R&D on KIE performance; and how the constituent elements of these dimensions can be configured to generate conditions for high performance.

The authors’ empirical setting involves companies that submitted projects to the Innovative Research in Small Businesses (PIPE) program in Brazil. The authors then run partial least square structural equation modeling to verify how scientific and strategic R&D capabilities influence the performance construct. Second, the authors apply fuzzy-set qualitative comparative analysis to identify configurations that are equifinal in terms of generating superior performance.

Findings indicate a strong association between scientific capabilities and KIE performance. The configurational approach outlines the existence of multiple paths to success, but human capital stands as a core condition throughout estimations.

The authors’ assessment has implications for how KIE firms are managed according to their organizational profiles and trajectories. Also, it advances the authors’ comprehension on how entrepreneurship policies can better target these distinct profiles.

The authors’ analysis provides new evidence on the inherent complexity behind the generation of high performance in KIE when addressing their portfolios of knowledge-related capabilities. More than that, the authors were able to identify the existence of heterogeneous profiles that can equally lead to higher levels of performance.

To answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?

This paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.

Results show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.

The findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.

This paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.

This study aims to realize efficient, fast and safe robot search task, the belief criteria decision-making approach is proposed to solve the object search task with an uncertain location.

The study formulates the robot search task as a partially observable Markov decision process, uses the semantic information to evaluate the belief state and designs the belief criteria decision-making approach. A cost function considering a trade-off among belief state, path length and movement effort is modelled to select the next best location in path planning.

The semantic information is successfully modelled and propagated, which can represent the belief of finding object. The belief criteria decision-making (BCDM) approach is evaluated in both Gazebo simulation platform and physical experiments. Compared to greedy, uniform and random methods, the performance index of path length and execution time is superior by BCDM approach.

The prior knowledge of robot working environment, especially semantic information, can be used for path planning to achieve efficient task execution in path length and execution time. The modelling and updating of environment information can lead a promising research topic to realize a more intelligent decision-making method for object search task.

This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.

The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.

The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.

The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.

This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

This study aimed to discover the factors affecting employee performance by testing the relationship of change management, job satisfaction, organizational commitment and leadership style on employee performance in Indonesian sharia property companies.

The study population was all members of “Sharia Property Developer” (DPS) across Indonesia with criteria of having subordinates at least one person and is listed as a DPS member. The samples used were 71 people from the 200 members of DPS across Indonesia. The sampling method used was based on R² value and significance level with an 80% statistical strength. Data analysis was carried out using smartPLS software to test the relationship of change management, job satisfaction, organizational commitment and leadership style on employee performance.

The utilization of SEM in Smart PLS for change management with the ADKAR method had a negative value of 6.2% in affecting employee performance and 4.6% in affecting job satisfaction. Job satisfaction insignificantly affected employee performance by 7.5%. Leadership style and organizational commitment positively affected performance by 57.9% and 25.6%, respectively.

This study did not limit respondents’ education levels. Twenty percent of respondents were middle and high school graduates. Respondent’s position was mostly the highest leader in the company by 58%. Indicators in the ADKAR model did not implement the construct validity test since the researchers did not find precedent studies that discuss the indicators of the ADKAR model in detail.

Factors that positively and significantly affected employee performance can be used to plan employee performance of DPS member companies.

The company must create a program to produce meaning in working, shape leaders to have discipline by putting appropriate employees as leaders.

This study used change management, organizational commitment, job satisfaction and leadership style as exogenous variables, job satisfaction and leadership style as intervening variables. The study model modified the previous study regarding employee performance improvement because it utilized the change management with the ADKAR model. The study objects were sharia property companies, where the researchers did not find previous studies discussing employee performance in sharia property companies.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.