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Successful digital transformation involves all areas which bring new impacts and challenges to the leadership of the enterprise. From the perspective of organizational identification, the authors construct a theoretical model of digital leadership–digital strategic consensus–digital transformation and explore the different moderated mediation effects of diversity types.

This paper obtains data from 351 Chinese science and technology enterprises and uses regression analysis and bootstrap analysis to test the research hypotheses.

The results demonstrate that digital leadership has a positive impact on digital transformation. Digital strategic consensus partially mediates the linkage between digital leadership and digital transformation. Disparity diversity and variety diversity positively moderate the mediating role of digital strategic consensus between digital leadership and digital transformation, respectively; and separation diversity negatively moderates the mediating role of digital strategic consensus between digital leadership and digital transformation.

The research innovatively measures digital leadership and digital transformation. It expands the application of leadership, strategic consensus, diversity and other related theories in a digital context and provides a decision-making basis for enterprises' digital transformation.

Although virtual anchors have emerged as potent marketing tools, their acceptance by consumers is controversial. Specifically, the relative efficacy of selecting an all-human-like or animal-human-like virtual anchor is not well-defined. However, anthropomorphic visual cues are vital in enhancing live streaming. This study aims to analyze the disparate effects of an animal-human-like or all-human-like virtual anchor on purchase intention as well as evaluate the possible underlying influential mechanisms and boundary conditions.

In this research, three different studies were carried out to elucidate the impact of virtual anchors on purchase intention. Study 1 evaluated the core impact of an animal-human-like and all-human-like virtual anchor on purchase intention, as well as the mediating role of perceived warmth and competence. Studies 2 and 3 were then performed to investigate the moderating impacts of product type and certainty of consumer needs, respectively. Furthermore, research data for these studies was collected using the Credamo tool and analyzed via SPSS, using PROCESS for moderation and mediation analyses.

The research findings indicate that virtual anchors can trigger purchase intention, with perceived warmth and competence acting as mediating factors. Based on the utilitarian products and high certainty of consumer needs, the influence of perceived competence on purchase intention is augmented. Therefore, an all-human-like virtual anchor increases purchase intention. In contrast, the impact of perceived warmth on purchase intention is supplemented for hedonic products and low certainty of consumer needs. Thus, an animal-human-like virtual anchor increases purchase intention.

This research study evaluated consumer reactions to all-human-like and animal-human-like virtual anchors for different product types and the certainty of consumer needs to optimize the comprehension of a virtual anchor. Furthermore, the assessment of the mediating roles of perceived warmth and competence provided valuable insights into the influential mechanisms by which virtual anchors affect purchase intention. Moreover, this study provided managerial implications to guide retailers and brands on the strategic adoption of virtual anchors to enhance purchase intention based on the product type and the certainty of consumer needs.

The purpose of this paper is to develop a more comprehensive understanding of the consequences of retaliations and our evidence indicates that retaliations are beneficial for firms with supranormal earnings by making their higher earnings more persistent, but harmful for firms with subnormal earnings by slowing the recovery of their earnings.

This paper use annual Compustat files based on Fama-French 48 industry. The time-varying competitive reactions (CRs) for each firm are captured using quarterly rolling-window estimation across 41 windows with five years (i.e. 20 observations) in each window. This paper measure earnings persistence as the slope coefficient (ß1) from regressing future earnings on current earnings. The result remains qualitatively similar to the main findings when alternative measures of earnings persistence.

Abnormal earnings are expected to dissipate in the long run owing to competitive forces, but this paper show that more retaliatory CRs increase earnings persistence. This is good news for supranormal firms as they can sustain high profitability. However, it will be harder to revert subnormal earnings to the industry mean if such firms conduct more retaliatory CRs. This paper also show that these associations are stronger for less competitive industries.

First, high earnings persistence per se would not be a major consideration in the firm’s strategic decisions but a natural by-product of such decisions spanning an extended period of operations. Second, though this paper focus on the period of 2004–2018 that includes the rebound after financial crisis in 2008, an extension of the observation period over a longer economic cycle would verify our results.

CRs are regarded as an evolving portfolio of dynamic marketing decisions and tools for strategic decisions in our study. It helps how firms manage competition over time to lengthen the superior performance. Also it helps the low-profitability firms attempting to improve profitability by showing nonretaliation may be a more appropriate strategy than retaliation.

Firms in financial distress suffer from illiquidity, survival of firms is contingent on meeting their financial obligations, thus need for turnaround decisions. However, retaliations under financial distress can mitigate the effect of such turnaround decisions and thereby aggravate the situation.

Greater persistence extends the benefits of superior earnings, thus increasing the opportunities for value exploitation, but it may also restrict earnings recovery. This paper finds that the way that firms react within the competition explain the differences in earnings persistence. Although a large body of research has examined the static drivers (e.g. firm size and diversification) of the differential persistence of earnings, there has been little research on dynamic drivers that explicitly recognize the erosion process for earnings.

Loitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.

To address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).

The proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.

The proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

This study examines the impact of digital corporate social responsibility (CSR) on social entrepreneurship, organizational resilience and competitive intelligence during the coronavirus disease 2019 (COVID-19) crisis. It also examines the impact of competitive intelligence on social entrepreneurship and organizational resilience.

Data were collected from telecommunication companies in Jordan with a sample of 223 managers, using Smart-PLS for analysis and testing the research model and hypotheses.

The results reveal a significant impact of digital CSR on social entrepreneurship. They show that digital CSR significantly impacts organizational resilience. The findings also indicate a significant role of digital CSR in competitive intelligence. This study shows that social entrepreneurship significantly impacts organizational resilience. The results also confirm the impact of competitive intelligence on social entrepreneurship. Finally, the results confirm that competitive intelligence significantly impacts organizational resilience.

This study provides valuable academic and practical insights into digital CSR practices, social entrepreneurship and how to support organizational resilience during crises.

The study aims to investigate how artificial intelligence (AI)-powered virtual streamers can supercharge brands in live-streaming virtual commerce (v-commerce). Built upon social identity theory (SIT) and experiential value theory, we developed a framework to investigate the impact of AI-powered virtual streamers’ personalization and human-like personalities and live-streaming v-commerce’s system quality and content quality on brand image, mediated by parasocial interaction and experiential value.

A survey was designed and distributed to the target respondents via social media channels. SmartPLS version 4.0.9.4 was used to analyze a total of 354 responses after the data were obtained via purposive sampling.

The results show that personalization, human-like personality, system quality and content quality are positively associated with parasocial interaction and experiential value, which subsequently impact brand image.

This study addresses the gap of relatively sparse academic literature on the implications of AI-powered virtual streamers in live-streaming v-commerce on brand image.

Digitally driven virtual streamers are increasingly utilized in live-streaming commerce, possessing distinct advantages compared to human streamers. However, the applicable scenarios of virtual streamers are still unclear. Focusing on product attribute variances, this paper compares the livestreaming effects of virtual and human streamers to clarify the applicable scenarios for each and assist companies in strategically choosing suitable streamers.

We conducted four experiments utilizing both images and video as stimulus materials, and each experiment employed different products. To test the proposed model, a total of 1,068 valid participants were recruited, encompassing a diverse group of individuals, including undergraduates and employed workers.

The results indicate no significant difference between virtual and human streamers in increasing consumers’ purchase intention for utilitarian products. In contrast, human streamers are more effective in enhancing consumer purchase intention for hedonic products, with a mediating role of mental imagery quality. Consumers’ implicit personality variances also influence their willingness to accept virtual streamers.

This paper is the first to compare the effects of virtual and human streamers in promoting different products to enhance our comprehension of virtual streamers. Given the potential risks associated with human streamers, a comprehensive understanding of the role of virtual streamers is imperative for brands when deploying live-streaming commerce activities.

To exploit collaboration-driven innovation, in recent years, many government-sponsored innovation programs and mentor services have emerged. These services support an effective exchange of knowledge among innovation actors, including innovation mentors and enable mentor connectedness as an important factor to develop and sustain effective innovation mentors’ community of practice (CoP). The purpose of this paper is to examine the degree of connectedness in an innovation mentor CoP.

In this study, the innovation mentors CoP as part of a national innovation program is considered a network. The connectedness and assortative mixing of this CoP and the effects of these two on each other were examined by using social network measures, including component analysis, the giant component (GC) and assortativity.

The authors provide the analytical interconnectedness results for both the GC and the whole network with network analysis and assortativity measurements of three attributes of mentors (institution, title and degrees). The degree of correlation of community for the GC shows preferential attachment between high-ranking and low-ranking mentors, while preferential attachment was not observed for the whole network. The correlation coefficient for the institution attribute has the highest value for GC, while the title has the highest value for the whole network.

The study is one of the early attempts to apply social network analysis for an innovation mentor CoP. This study reveals the criticality of evaluating the GC and the whole network separately and provides a number of research and practical directions that will contribute to the development of the innovation mentor CoP.

Positive effect of knowledge sharing (KS) on innovation has come to the fore and government-supported innovation and mentoring communities or mentor networks have become widespread. This article aims to examine the community connectedness and mentors' preferences for professional competency-based KS of such innovation community of practice networks (CoPNs).

The paper constructs a directed weighted CoPN model with a node-attribute-based novel fingerprint edge weights. Based on the CoPN, Social Network Analysis (SNA) metrics and measures including Giant Component (GC) were proposed and analyzed to identify mentors' connectedness preferences. The fingerprint was proposed as a novel binarized node attribute of competence. Jaccard similarity of fingerprints was proposed as edge weights to reveal correlations between competences and preferences for KS.

The work opted to conduct a survey of 28 innovation mentors to measure a CoPN. Both a name generator question and a second set of questions were employed to invite respondents to name their collaborators and indicate their professional competence. SNA metrics result in differing values for GC and the rest, which lead us to focus on GC to reveal salient metrics of connectedness. Jaccard similarity analysis results on GC demonstrate that mentors collaborate in an interdisciplinary manner.

Based on the CoPN, the methods proposed may be effective in predicting preferred relationships for interdisciplinary collaborations, providing the managers with an analytical decision support tool for KS in practice.

Asynchronous Video Interviews (AVIs) incorporating Artificial Intelligence (AI)-assisted assessment has become popular as a pre-employment screening method. The extent to which applicants engage in deceptive impression management (IM) behaviors during these interviews remains uncertain. Furthermore, the accuracy of human detection in identifying such deceptive IM behaviors is limited. This study seeks to explore differences in deceptive IM behaviors by applicants across video interview modes (AVIs vs Synchronous Video Interviews (SVIs)) and the use of AI-assisted assessment (AI vs non-AI). The study also investigates if video interview modes affect human interviewers' ability to detect deceptive IM behaviors.

The authors conducted a field study with four conditions based on two critical factors: the synchrony of video interviews (AVI vs SVI) and the presence of AI-assisted assessment (AI vs Non-AI): Non-AI-assisted AVIs, AI-assisted AVIs, Non-AI-assisted SVIs and AI-assisted SVIs. The study involved 144 pairs of interviewees and interviewers/assessors. To assess applicants' deceptive IM behaviors, the authors employed a combination of interviewee self-reports and interviewer perceptions.

The results indicate that AVIs elicited fewer instances of deceptive IM behaviors across all dimensions when compared to SVIs. Furthermore, using AI-assisted assessment in both video interview modes resulted in less extensive image creation than non-AI settings. However, the study revealed that human interviewers had difficulties detecting deceptive IM behaviors regardless of the mode used, except for extensive faking in AVIs.

The study is the first to address the call for research on the impact of video interview modes and AI on interviewee faking and interviewer accuracy. This research enhances the authors’ understanding of the practical implications associated with the use of different video interview modes and AI algorithms in the pre-employment screening process. The study contributes to the existing literature by refining the theoretical model of faking likelihood in employment interviews according to media richness theory and the model of volitional rating behavior based on expectancy theory in the context of AVIs and AI-assisted assessment.