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A growing number of studies have investigated the effect of ethical leadership on behavioral outcome of employees. However, considering the important role of ethics in IS security, the security literature lacks a theoretical and empirical investigation of the relationship between ethical leadership and employees' security behavior, such as information security policy (ISP) violation. Drawing on social learning and social exchange theories, this paper empirically tests the impact of ethical leadership on employees' ISP violation intention through both information security climate (i.e. from a moral manager's perspective) and affective commitment (i.e. from a moral person's perspective).

The research was developed based on social learning theory and social exchange theory. To measure the variables in the model, the authors used and adapted measurement items from previous studies. The authors conducted a scenario-based survey with 339 valid responses to test and validate the research model.

Results indicated that information security climate fully mediates the relationship between ethical leadership and ISP violation intention. The authors also found that information security climate enhances the negative effect of affective commitment on ISP violation intention.

This research contributes to the literature of information security by introducing the role of ethical leadership and integrating two theories into our research model. This study also calls attention to how information security climate and affective commitment mediate the relationship between ethical leadership and employees' ISP violation intention. The theory-driven study provides important pragmatic guidance for enhancing the understanding of the importance of ethical leadership in information systems security research.

The purpose of this study is to demonstrate that Strategic Enterprise Management (SEM) and Business Intelligence (BI) have the potential to integrate management decisions vertically through an organization’s hierarchy. This study also aims to present a design theory framework and build a model dimension using eight principles serving as mid-range theories.

This study uses a design science perspective to posit how organizations can successfully implement SEMBI (a union of SEM and BI). This study then completes the design theory by building the method dimension using two principles. Finally, the study presents testable hypotheses for the theory and an evaluation using stakeholder attitudes and judgments as proxies for objective measures.

In the search for a prescription for SEMBI success, this study finds that the notion of the Capability Maturity Model (CMM) is a good artifact with which to organize the principles the authors are seeking. CMM has since been adapted to suit different contexts by incorporating relevant principles from those domains. Hereafter, this study refers to SEMBI–CMM as the adapted solution for SEMBI's success.

This study coins and uses the term SEMBI to represent the union of SEM and BI. This term retains its distinct identities and principles and forms a holistic and integrated view of SEM and BI implementation strategies. In an effort to advance this line of research, this study employs a design science perspective to address the question of how an organization can successfully implement SEMBI.

The purpose of our study is to investigate the effects of politically-connected boards (PCBs) on over-(under-)investment in labor. We also examine the impacts of the supervisory board (SB)’s optimal tenure on the association between PCBs and over-investment in labor.

We constructed the proxy for PCBs using a dummy variable set to 1 (one) if a firm has politically-connected boards and zero (0) otherwise. For the robustness check, we used the number of politically-connected members on the boards as the proxy for PCBs.

We find that the presence of PCBs reduces over-investment in labor. Consistent with our prediction, we found no significant association between PCBs and under-investment in labor. We also find that the SB with optimal tenure strengthens the negative association between PCBs and over-investment in labor. In our channel analysis, we find that the presence of PCB mitigates over-investment in labor through a higher dividend payout ratio.

Due to the unavailability of data in firms’ annual reports regarding the number of poorly-skilled and highly skilled employees, we were not able to examine the effect of low-skilled and high-skilled employees on over-investment in labor. Also, we were not able to examine over-(under-)investment in labor by drawing a distinction between general (generalist) and firm-specific human capital (specialist) as suggested by Sevcenko, Wu, and Kacperczyk (2022). Generally, it is more difficult for managers to hire highly-skilled employees, specialists in particular, thereby driving the choice of either over- or under-investing in the labor forces. In addition, in the firms’ annual reports, there is no information regarding temporary employees. Therefore, if and when such data become available, this would provide another avenue for future research.

Our study offers several practical implications and insights to stakeholders (e.g. insiders or management, shareholders, investors, analysts and creditors) in the following ways. First, our study highlights significant differences between capital investment and labor investment. For instance, labor investment is considered an expense rather than an asset (Wyatt, 2008) because, although such investment is human capital and is not recognized on the firm’s balance sheet (Boon et al., 2017). In addition, labor investment is characterized by: its flexibility which enables firms to make frequent adjustments (Hamermesh, 1995; Dixit & Pindyck, 2012; Aksin et al., 2015), its non-homogeneity since every employee is unique (Luo et al., 2020), its direct impact on morale and productivity of a firm (Azadegan et al., 2013; Mishina et al., 2004; Tatikonda et al., 2013), and its financial outlay which affects the ongoing cash flows of a firm (Sualihu et al., 2021; Khedmati et al., 2020; Merz & Yashiv, 2007). Second, our findings reveal that the presence of PCBs could help to reduce over-investment in labor. However, if managers of a firm choose to under-invest in labor in order to obtain better profit in the short-term through cost saving, they should be aware of the potential consequences of facing a financial loss when a new business opportunity suddenly arises which requires a larger labor force. Third, our findings help stakeholders to re-focus on the labor investment. This is crucial due to the fact that labor investment is often neglected by those stakeholders because the expenditure of labor investment is not recognized on the firm’s balance sheet as an asset. Instead, it is written off as an expense in the firm’s income statement. Fourth, our findings also provide insightful information to stakeholders, suggesting that an SB with optimal tenure is more committed to a firm, and this factor plays an important role in strengthening the negative association between PCBs and over-investment in labor.

First, our findings provide a valuable understanding of the effects of PCBs on over-(under-)investment in labor. Stakeholders could use information disclosed in the financial statements of a publicly-listed firm to determine the extent of the firm’s investment in labor and PCBs, and compare this information with similar firms in the same industry sector. Second, our findings give a better understanding of the association between investment in labor and political connections , which are human and social capital that could determine the long-term survival and success of a firm. Third, for shareholders, the appointment of board members with political connections is an important strategic decision to build political capital, which is likely to have a long-term impact on the financial performance of a firm; therefore, it requires thoughtful consultation with firm insiders.

Our findings highlight the role of PCBs in reducing over-investment in labor. These findings are significant because both investment in labor and political connections as human and social capital can play an important role in determining the long-term survival and success of a firm.

The paper debunks Peter P. Li’s assertion that Yin-Yang is superior to any other cognitive frames or logical systems for paradox research. The purpose of this paper is to alert the Chinese indigenous management researchers to the danger of Chinese exceptionalism and over-confidence.

To show that Peter P. Li’s assertion is doubtful, the authors identify the flaws in his analysis.

The authors find that there are three serious flaws in Peter P. Li’s analysis. First, there are four defects in the typology of cognitive frames he built in order to compare Yin-Yang with the others. Second, his understanding of dialectics in general and Hegelian dialectics in particular is flawed. And finally, without resorting to Yin-Yang, many scholars can develop theories that are equivalent to those derived from Yin-Yang.

Due to the page limit, this paper only focuses on arguing that Yin-Yang is not superior to other cognitive frames or logical systems without going one step further to explain in which situations Yin-Yang are valuable and might be more suitable than others for helping us understand some research issues.

This paper implies that we should not blindly believe that the Chinese way of thinking and acting is superior to other people’s. Chinese people should be open-minded in the globalized era, not only promoting their own culture but also appreciating and learning from other cultures.

The reduction of cultural exceptionalism and ethnocentrism can make cross-cultural communication and interaction smoother.

This paper is a rigorous critique on the “Yin-Yang being superior” assertion of Peter P. Li.

The purpose of this paper is to comment on Professor Ming-Jer Chen’s recent publication titled “Competitive dynamics: Eastern roots, Western growth” and present an asymmetry reversing perspective on the competitive dynamics between two nonobvious, invisible or indirect competitors, namely, how emerging market resource-poor firms compete and outcompete advanced country resource-rich rivals.

The author first identifies an important neglect in Professor Chen’s scholarship on competitive dynamics, i.e., the neglect of the ubiquity of the less visible competition between two actors who initially would not be considered as competitors. Then, the author proposes an asymmetry reversing theory (ART) of competitive dynamics to redress this neglect. The theory is presented in two parts. The first part describes the competitive dynamics between the two actors as a three-stage process of reversing the asymmetry in resource possession and market position between the resource-poor firm and its resource-rich rivals. The second part explains the key success factors for the resource-poor firm to go through each of the three stages.

The growth process of the resource-poor firm can be broadly divided into three stages: surviving, catching-up, and outcompeting. For ambitious yet pragmatic resource-poor firms, in the surviving stage, they often (have to) accept the asymmetry between themselves and their resource-rich rivals in terms of resource possession and market position, and try to avoid any direct competition with the strong incumbents. They often tactically appear to pursue different paths of development from those of the strong incumbents by focusing on particular product categories and market segments. Doing so allows the resource-poor firms to win times and spaces for non-interrupted growth. Once they have accumulated sufficient resources and market experiences, they start to reduce the asymmetry between themselves and their better-endowed rivals by entering the similar or same product categories and market segments. To effectively catch up and outcompete the incumbents, they often differentiate themselves from their rivals by offering cheaper products or services, adding new features to their products, providing extra services to their customers, inventing new business models, etc.

One limitation of this paper is that the ART framework has so far been built on anecdotal evidences. It needs to be tested by empirical studies and refined further in the future. Another limitation is that the proposed theory is based on competitive dynamics between emerging market resource-poor firms and advanced country resource-rich firms. It needs to be tested whether this theory has applicability to any other firms.

This paper fills an important research gap in the competitive dynamics literature by proposing an asymmetry reversing theory of competitive dynamics between a weak latecomer and a strong incumbent in a competitive field.

Firms have already begun integrating artificial intelligence (AI) as a replacement for conventional performance management systems owing to its technological superiority. This transition has sparked a growing interest in determining how employees perceive and respond to performance feedback provided by AI as opposed to human supervisors.

A 2 x 2 between-subject experimental design was employed that was manipulated into four experimental conditions: AI algorithms, AI data, highly experienced human supervisors and low-experience human supervisor conditions. A one-way ANOVA and Welch t-test were used to analyze data.

Our findings revealed that with a predefined fixed formula employed for performance feedback, employees exhibited higher levels of trust in AI algorithms, had greater performance expectations and showed stronger intentions to seek performance feedback from AI algorithms than highly experienced human supervisors. Conversely, when performance feedback was provided by human supervisors, even those with less experience, in a discretionary manner, employees' perceptions were higher compared to similar feedback provided by AI data. Moreover, additional analysis findings indicated that combined AI-human performance feedback led to higher levels of employees' perceptions compared to performance feedback solely by AI or humans.

The findings of our study advocate the incorporation of AI in performance management systems and the implementation of AI-human combined feedback approaches as a potential strategy to alleviate the negative perception of employees, thereby increasing firms' return on AI investment.

Our study represents one of the initial endeavors exploring the integration of AI in performance management systems and AI-human collaboration in providing performance feedback to employees.

This paper aims to study the effect of the establishment of bankruptcy courts on bond issuance market. This paper helps to predict that the introduction of bankruptcy courts in China can mitigate price distortions caused by the implicit government guarantees and promote the development of the high-risk bond market.

This paper exploits the staggered introduction of bankruptcy courts across cities to implement a differences-in-differences strategy on bond issuance data. Using bonds issued in China between 2018 and 2020, the impact of bankruptcy courts on the bond issuance market can be analyzed.

This paper reveals that bond issuance credit spreads increase and is more sensitive to firm size, profitability and downside risk of issuance entity after the introduction of bankruptcy courts. It also reveals a substantive increase in bond issuance quantity and a decrease in issuer credit ratings following the establishment of bankruptcy courts. In addition, the increase of credit spreads is more prominent for publicly traded bonds, those whose issuers located in provinces with lower judicial confidence, bonds issued by SOEs and bonds with stronger government guarantees. Finally, the role of bankruptcy courts is more pronounced in regions with higher marketization.

This paper relates to previous studies that investigate the impact of laws and institutions on external financing. It helps provide new evidence to this literature on how improvements of efficiency and quality in bankruptcy enforcements relate to the marketization of bond issuance. The results provide further evidence on legal institutions and bond financing.

Safety management is a key point and poses a challenge in joint testing. To detect and address potential accidents' hidden dangers early, this paper conducts research on the safety control technology for high-speed railway joint tests by incorporating the concept of hazardous events.

Aiming at ensuring the safety of high-speed railway combined inspections and trials, this paper starts from the dual prevention mechanism. It introduces the concept of threatening events, defines them and analyzes the differences between threatening events and railway accidents. The paper also proposes a cause model for threatening events in high-speed railway combined inspections and trials, based on three types of hazard sources. Furthermore, it conducts research on the control strategies for these threatening events.

The research on safety control technology for high-speed railway combined operation and testing, based on the analysis of threatened events, offers a new perspective for safety management in these operations. It also provides theoretical and practical support for the transition from passive prevention to active risk pre-control, which holds significant theoretical and practical value.

The innovation mainly includes the following three aspects: (1) Building on the traditional dual prevention mechanism, which includes risk hierarchical management and control as well as hidden danger investigation and management, a triple prevention mechanism is proposed. This new mechanism adds the management of threatening events as the third line of defense. The aim is to more comprehensively identify and address potential security risks, thereby enhancing the efficiency and effectiveness of security management. (2) In this paper, the definition of a railway threatening event is clarified, and the causative model of a high-speed railway threatening event based on three kinds of danger sources is proposed. (3) This paper puts forward the control strategy of the high-speed railway combined operation and trial, which includes five key links: identification, reporting, analysis, rectification and feedback, which provides a new perspective for the safety management of the high-speed railway combined operation and trial and has important theoretical and application value.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.