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A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

The paper aims to investigate the difference in total factor productivity (TFP) among those firms with and without outsourcing in a developing country like Vietnam. Also, it explores the effect of outsourcing activities on total factor productivity with a specified concentration on the Vietnamese small and medium-sized enterprises (SMEs).

The panel data set of SMEs used in this study was originated from biannual surveys conducted under the collaboration between educational organizations and government agencies: Stockholm School of Economics (SSE), Department of Economics – the University of Copenhagen, the Institution of Labor Studies and Social Affairs (ILSSA) in the Ministry of Labor, Invalids and Social Affairs (MOLISA). In this study, the model is developed based on the production function in accordance with the model of Girma and Görg (2004). The firms’ TFP is the difference between the actual and the predicted output as with the approach by Levinsohn and Petrin (2003).

This study finds out that firms with outsourcing have higher total factor productivity than those without outsourcing activities. In addition, the more firms spend on outsourcing, the higher total factor productivity they can gain. Outsourcing to SMEs in a developing country can significantly increase its TFP by means of either maintaining core competencies or searching external resources in conducting some internal activities.

Although outsourcing has been widely applied by large firms, the research studying its impact on productivity at firm level is limited. Especially, this study can shed light on the impact for the case of SMEs in a developing economy.

This study aims to use the ego depletion theory to examine the impact of hindrance stressors on knowledge sharing behaviors by investigating the mediating role of ego depletion and the moderating role of self-enhancing humor.

Data were obtained from a two-wave sample of 226 dyads, including employees in the manufacturing industry and their direct supervisors. The hypotheses were tested by hierarchical regression analyzes and Hayes’ PROCESS macro.

The results demonstrated that employees’ self-enhancing humor style could alleviate the impact of hindrance stressors on employees’ ego depletion state and buffer the negative indirect effect of hindrance stressors on employees’ knowledge-sharing behaviors.

Although the authors collected mediator and dependent variables from different sources, this study used a cross-sectional research design, making it difficult to draw causal conclusions. Besides, hindrance stressors, ego depletion and self-enhancing humor style were all reported by employees.

Through the study, the authors highlight the important role of the self-control view in explaining proactive behavior in the workplace and a great awareness of the unforeseeable consequences of ego depletion for employees.

The paper aims to provide a comprehensive investigation of the relationship between corporate governance (CG) structure and firm performance in Chinese listed firms from 2001 to 2015. The authors’ motivation derives from the fact that the CG system in China is different from those in the US, the UK, Germany, Japan and other countries.

A large unbalanced sample, covering more than 22,700 observations in Chinese listed firms, was used to explore, by means of a system-generalized method-of-moments (GMM) estimator, the relationship between CG structure and firm performance to remove potential sources of endogeneity.

Results show that Chinese CG structure is endogenously determined by the CG mechanisms investigated: there is no relationship between board size (including independent directors) and firm performance; CEO duality has a significantly negative effect on firm performance; concentration of ownership has a significantly positive influence on firm performance; managerial ownership is negatively correlated with firm performance; state ownership has a significantly positive effect on firm performance; and a supervisory board is positively correlated with firm performance.

The findings provide policymakers and firm managers with useful empirical guidance concerning CG in China.

Few integrative studies have examined the impact of CG structure on firm performance in China. This study adds new empirical evidence that the relation between CG structure and performance in China is endogenous and dynamic when controlling for unobserved heterogeneity, simultaneity, and dynamic endogeneity.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

The purpose of this paper is to conceptualize how past corporate social responsibility (CSR) affects the occurrence of organizational misconduct by the means of moral licensing.

To this end, the authors conduct a conceptual review and develop a framework illustrating how moral credits and moral credentials (moral licensing) may institutionalize irresponsibility and lead to subsequent misconduct.

The authors propose a conceptual framework that describes the relationship between past CSR and organizational misconduct by the means of moral licensing. Based on initial literature-based findings, this paper provides confirmatory evidence for the authors’ framework and illustrates that past CSR might be used as a moral licensing tool that eventually fosters the occurrence of organizational misconduct.

The authors propose future researchers account for the moral licensing effect when examining the antecedents of misconduct and explore the potential moderators of this effect.

The authors recommend that organizations establish management control systems that specifically address the issue of moral licensing when evaluating CSR initiatives. The authors also propose that organizations should adhere to a consistent CSR strategy that potentially fosters the selection of moral leaders who are not prone to moral licensing.

To the best of the authors’ knowledge, this paper is the first to connect corporate social responsibility, moral licensing and organizational misconduct from a conceptual perspective.

The objective of the study is to identify both reasons for ignoring and for paying attention to the country of origin (COO) by consumers when choosing brands of durable goods.

In this paper a qualitative approach was applied, i.e. 25 in-depth semistructured interviews with Polish consumers of durable goods purposively selected out of those who had previously participated in a quantitative survey conducted by the authors.

Among the reasons for paying attention to the COO when choosing brands of durable goods, cognitive (rational), affective (emotional) and normative factors have been identified, while among the reasons for ignoring the COO by consumers, the authors identified only cognitive (rational) and affective (emotional) factors.

The conclusions can be applied by brand managers, e.g. when making decisions whether and why to communicate COO dimensions.

This paper contributes to the international marketing literature as it develops a deeper insight into consumer behavior with reference to the consumers’ reasons for paying attention to and ignoring the COO, with very little published on the latter in particular. Furthermore, it is one of still relatively few qualitative studies conducted so far on COO taking a perspective of a consumer, especially the one from an emerging market from the European Union (EU).

The alienation of megaproject environmental responsibility (MER) behavior is destructive, but its mechanism has not been clearly depicted. Based on fraud triangle theory and the fuzzy set qualitative comparative analysis (fsQCA) method, this study explored the combined effect of antecedent factors on alienation of MER behavior.

Based on the fraud triangle theory and literature review, eight influencing factors associated with the alienation of MER behavior were first identified. Subsequently, the fuzzy-set qualitative comparative analysis was used in this study to reveal configurations influencing alienation of MER behavior.

The study found nine configurations of MER behavioral alienation antecedent factors, integrated into three types of driving modes, i.e. “economic pressure + learning effect,” “institutional defect + moral rejection,” and “information asymmetry + economic pressure + expectation pressure.”

By analyzing the configuration effects of various induced conditions, this study puts forward a comprehensive analysis framework to solve the alienation of MER behavior in the megaprojects and a practical strategy to control alienation of MER behavior.

Distancing people socially as a precautionary measure against the mushrooming of COVID-19’s health and economic crisis has deleteriously affected the performance of the eatery industry to a great extent. Many food outlets failed to cope up with crisis and opted to move out, and many still vie to survive through pandemic. It becomes vital for researchers to understand what factors influence the performance and survival of eateries during the pandemic? The study makes an attempt to fabricate new factors which affect the performance and contribute significantly towards the survival of eateries in this new COVID-19-prone world.

The present study is a cross-sectional analysis with the sample of 150 eateries from the walled city of Punjab (India), i.e. Amritsar. Factor analysis is employed to scrutinise the factors which influence the performance of eateries during the pandemic, and to analyse factors which contribute significantly for the survival of eateries, logistic regression is performed.

The empirical analysis reveals that at prior psychological factor, followed by turnover factor, external factor, financial factor and marketing factor influence the performance of eateries during the pandemic. Only three factors, namely turnover factor, external factor and financial factor, turned up to be significant towards the survival rate of an eatery. The marketing factor which is a crucial contributor for survival of business in literature has turned out to be insignificant during the times of pandemic.

With the arrival of pandemic, the preference of people has changed, and the business environment in which entities operate has turned more complex. The present study is one of the pioneer attempts to evaluate whether the factors responsible for performance or survival of an eatery during normal times is relevant during the pandemic as well. The study contributes to the literature of eatery industry by adding a new variable namely psychological factor, i.e. changes witnessed in customers’ preference to visit an eatery.

While the disruptive potential of artificial intelligence (AI) has been receiving growing consensus with regards to its positive influence on entrepreneurship, there is a clear lack of systematization in academic literature pertaining to this correlation. The current research seeks to explore the impact of AI on entrepreneurship as an enabler for entrepreneurs, taking into account the crucial application of AI within all Industry 4.0 technological paradigms, such as smart factory, the Internet of things (IoT), augmented reality (AR) and blockchain.

A systematic literature review was used to analyze all relevant studies forging connections between AI and entrepreneurship. The cluster interpretation follows a structure that we called the “AI-enabled entrepreneurial process.”

This study proves that AI has profound implications when it comes to entrepreneurship and, in particular, positively impacts entrepreneurs in four ways: through opportunity, decision-making, performance, and education and research.

The framework's practical value is linked to its applications for researchers, entrepreneurs and aspiring entrepreneurs (as well as those acting entrepreneurially within established organizations) who want to unleash the power of AI in an entrepreneurial setting.

This research offers a model through which to interpret the impact of AI on entrepreneurship, systematizing disconnected studies on the topic and arranging contributions into paradigms of entrepreneurial and managerial literature.