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The composition of author teams is a significant factor affecting the novelty of academic papers. Existing research lacks studies focusing on institutional types and measures of novelty remained at a general level, making it difficult to analyse the types of novelty in papers and to provide a detailed explanation of novelty. This study aims to take the field of natural language processing (NLP) as an example to analyse the relationship between team institutional composition and the fine-grained novelty of academic papers.

Firstly, author teams are categorized into three types: academic institutions, industrial institutions and mixed academic and industrial institutions. Next, the authors extract four types of entities from the full paper: methods, data sets, tools and metric. The novelty of papers is evaluated using entity combination measurement methods. Additionally, pairwise combinations of different types of fine-grained entities are analysed to assess their contributions to novel papers.

The results of the study found that in the field of NLP, for industrial institutions, collaboration with academic institutions has a higher probability of producing novel papers. From the contribution rate of different types of fine-grained knowledge entities, the mixed academic and industrial institutions pay more attention to the novelty of the combination of method indicators, and the industrial institutions pay more attention to the novelty of the combination of method tools.

This paper explores the relationship between the team institutional composition and the novelty of academic papers and reveals the importance of cooperation between industry and academia through fine-grained novelty measurement, which provides key guidance for improving the quality of papers and promoting industry–university–research cooperation.

The current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer’s purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI.

The proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.

This study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems’ findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI.

Developers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer’s page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI.

This research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information.

Criticality cognitions regarding the same workplace event often differ between leaders and employees. Nevertheless, its consequences on employee work outcomes remain unknown. In this study, we draw on cognitive dissonance theory to examine how and why leader–employee differences in cognitions of workplace event criticality impact employee job-related outcomes.

Wu used multilevel polynomial regression analyses from a time-lagged, multi-source field study with 145 leader–employee dyads to test our proposed model.

Leader–employee differences in cognitions of workplace event criticality can bring both benefits and perils to employees. Specifically, such differences can cause employee rumination, which in turn leads to an increase in both employee voice and fatigue.

This study contributes to the event and cognitive discrepancy literature in four ways. First, prior event studies largely adopted a singular employee perspective for investigation (e.g. Chen et al., 2021; Lin et al., 2021). By examining the impacts of event criticality from the dual perspective of leaders and employees, we attain a more comprehensive understanding of the implications of workplace events in organizational life. Second, extant studies have predominantly focused on the dark side of cognitive discrepancy (e.g. Bashshur et al., 2011; Erdogan et al., 2004; Grandey et al., 2013). Our study reveals that leader–employee differences in criticality cognitions can have both a bright and a dark side on employee outcomes, offering a more balanced and dialectical view of the consequences of cognitive discrepancy. Third, drawing on cognitive dissonance theory, we introduce employee rumination as an underlying mechanism to explain the impacts of leader–employee differences in criticality cognitions on employee voice and fatigue. Finally, while prior cognitive dissonance research has primarily employed an intrapersonal perspective (e.g. Sivanathan et al., 2008; Pugh et al., 2011; Grandey et al., 2013), our study adopts an interpersonal lens and underscores that interpersonal differences in cognitions can also serve as an example of cognitive discrepancy to instigate internal dissonance processes. By doing so, we enrich our understanding of cognitive dissonance theory.

Digital product and service innovations (DPSI) has attracted widespread attention from both management scholars and practitioners. Previous studies have documented that information technology (IT) capability and digital orientation positively influence DPSI performance. However, the question of whether and how digitalization capability can facilitate DPSI performance remains unresolved. This paper fills these gaps by investigating the mediating role of improvisation capability and the moderating role of technological turbulence.

This study used two-wave data from 240 matched digital transformation department leaders and senior managers from Chinese firms and examined the hypotheses deploying hierarchical regression and bootstrapping.

Our analyses reveal positive, significant links between digitalization capability and improvisation capability and between improvisation capability and DPSI performance. The findings further show that the effect of digitalization capability on DPSI performance is partially mediated by improvisation capability and that technological turbulence strengthens the indirect relationship between digitalization capability and DPSI performance through improvisation capability.

Integrating resource-based view, this research provides evidence that the extent to which improvisation capability mediates the relationship between digitalization capability and DPSI performance depends on technological turbulence. It provides a new direction for digitalization capability and DPSI performance.

This study aims to analyze the distribution of novelty among scholarly papers in the field of library and information science (LIS) in China. Specifically, this study explores the distribution of novelty of papers in various journals, research topics and different periods. It is possible to understand the characteristics of LIS research in China and what factors have influenced it.

This paper collects articles published in Chinese library science journals indexed by the Chinese Social Sciences Citation Index from 2000 to 2022. The BERTopic model is used based on abstracts of the papers and to obtain the topic of each paper. Based on the combination innovation theory of reference pairs cited by focal papers, novelty scores of all papers are calculated. Next, this paper analyzes the novelty of papers under different topics. Finally, this paper analyzes the differences in author collaboration patterns across various topics, aiming to explain how these differences relate to the novelty of papers from a collaborative perspective.

This study shows that archival research topics have lower novelty than papers on journal evaluation and patent technology in Chinese LIS. Research papers in this field are gradually becoming more novel over time. Papers on different topics and with varying degrees of novelty exhibit distinct author collaboration patterns, with low-novelty topics more frequently featuring solo authorship, while high-novelty topics tend to involve a higher percentage of inter-institutional collaboration.

This study investigates the novelty characteristics of research papers on different topics in the field of LIS in China. The authors’ contribution includes visualizing research hotspots and trends in the field and analyzing authors’ collaboration patterns at the level of research topics, thereby providing new perspectives on the factors affecting the novelty of these papers.

The purpose of this study is to solve the problem that existing researches ignore the long-term and staged nature of digital transformation, failing to conduct specific discussions for different stages. It responded the call by constructing a three-stage evolutionary model to analyze the impact of digital transformation at different stages on the sustainable performance of manufacturing enterprises. The moderating effect of core technology capabilities is also explored, guided by the theory of assimilation innovation.

Based on the panel data of Chinese listed manufacturing companies from 2012 to 2020, this study empirically investigate the impact of digital transformation (digital process, digital operation and digital ecology) on sustainability performance (economic performance and environmental performance).

The findings indicate that digital operations and digital ecology significantly improve economic performance and environmental performance. Furthermore, the core technological capacity of the enterprise serves to modify the positive correlation between digital transformation at each stage and sustainable performance to some extent. In other words, when an enterprise is equipped with the requisite technological capacity, the digital transformation at each stage accelerates both economic performance and environmental performance, which in turn is conducive to an improvement in the enterprise’s sustainable development performance.

The findings contribute to the theoretical framework of digital transformation and sustainable development in all stages of enterprises. Furthermore, they provide guidance for achieving sustainable development through the implementation of digital transformation and the enhancement of core technological capacity.

Introduction: The global financial crisis has affected the financial markets and has had social consequences in addition to economic ones. The concept of ‘sustainability’ concerns firms reaching their main corporate goals. So, to maximise corporate financial performance (FINP), firms pay attention to non-financial data, such as elements of governance, social, and environmental concerns (henceforth referred to as ESG). Therefore, non-financial information provided by EGS factors measured by the ESG score has a crucial role in incorporating strategy and firm performance.

Purpose: This chapter looks at how ESG scores affect the performance of firms. The term ‘ESG’ describes how corporate operations include ESG principles. The ESG score is a novel way to gauge a company’s sustainability.

Methodology: ESG practices are a current phenomenon that has taken the attention of researchers in the last decades. Besides the amount of research conducted, researchers still need consensus regarding its impact. This chapter implements a systematic literature review to compile the research on ESG performance (ESGP) and how it affects business performance.

Findings: Businesses can incorporate sustainability practices into their operations with the help of ESG reports. ESG reports and scores provide non-financial information, which is crucial for businesses to achieve sustainability in their activities and attract more investors. The chapter contributes to the literature by creating value through a comprehensive and theoretical literature review.

Leader high performance expectation (LHPE) as a performance management practice is becoming more common in today’s business environment, with managers setting ambitious goals to motivate employees to excel at their jobs. This study aims to critically examine LHPE as a performance management practice within contemporary organizations, focusing on whether LHPE has opposing effects on employee performance and health, as well as how servant leadership can improve the benefits of LHPE while lowering its costs.

A path analysis of data from a three-wave survey of 416 full-time employees was used to test our hypotheses.

LHPE has opposing effects on employees via two distinct pathways: motivational effects on employee performance via harmonious work passion and strain effects on employee health via work strain. In the face of LHPE, servant leadership can assist in achieving a mutual gain between employee performance and health.

This study contributes uniquely to the field of employee relations by offering a comprehensive analysis of LHPE’s dual effects. It moves beyond traditional views that focus on singular outcomes, providing a deeper understanding of how LHPE can both motivate and strain employees. Highlighting servant leadership’s role signifies a novel approach to managing LHPE’s complexities, presenting valuable insights for HR practitioners and organizational leaders. This research underlines the importance of balancing performance expectations with employee well-being, aligning with modern perspectives on positive employment relationships.

Recently, both researchers and practitioners have been very interested in the impact of leadership on employee engagement. Thus, I aimed to examine the relationship between spiritual leadership and work engagement through the mediating role of spiritual well-being at work.

I assessed spiritual leadership, engagement, and well-being in an empirical study based on a sample of 223 employees. I collected data through a survey-based method and analyzed them using structural equation modeling (SEM).

The present study contributes to the existing knowledge in the leadership field, especially spiritual leadership. The results revealed that spiritual leadership impacts employees’ work engagement by indirectly influencing employees’ spiritual well-being.

Theoretically, the findings imply that spiritual well-being can be one of the factors considered in enhancing work engagement through spiritual leadership.

Finding evidence that spiritual leadership, like other leadership styles, can foster employee engagement. Therefore, leaders should take care of employees’ spiritual needs.

Many researchers have indicated that well-being is associated with employee engagement. However, they overlooked employees’ spiritual well-being in the research. The study confirmed the unexplored mediating role of spiritual well-being between spiritual leadership and employee engagement.

Existing labor estimation models typically consider only certain construction project types or specific influencing factors. These models are focused on quantifying the total labor hours required, while the utilization rate of the labor during the project is not usually accounted for. This study aims to develop a novel machine learning model to predict the time series of labor resource utilization rate at the work package level.

More than 250 construction work packages collected over a two-year period are used to identify the main contributing factors affecting labor resource requirements. Also, a novel machine learning algorithm – Recurrent Neural Network (RNN) – is adopted to develop a forecasting model that can predict the utilization of labor resources over time.

This paper presents a robust machine learning approach for predicting labor resources’ utilization rates in construction projects based on the identified contributing factors. The machine learning approach is found to result in a reliable time series forecasting model that uses the RNN algorithm. The proposed model indicates the capability of machine learning algorithms in facilitating the traditional challenges in construction industry.

The findings point to the suitability of state-of-the-art machine learning techniques for developing predictive models to forecast the utilization rate of labor resources in construction projects, as well as for supporting project managers by providing forecasting tool for labor estimations at the work package level before detailed activity schedules have been generated. Accordingly, the proposed approach facilitates resource allocation and enables prioritization of available resources to enhance the overall performance of projects.