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Based on the team conflict theory and organizational learning theory, this study aims to discuss the two different types of conflicts of the top management team (TMT) on the different mechanisms of exploratory learning behavior of firms, and, based on the perspective of CEO-TMT (CEO – chief executive officer) interface, the different moderating effects caused by different CEO leadership styles are clarified.

Using the sample of 193 firms’ samples with multi-source data, the authors take an empirical test of the theoretical framework.

The effect of task conflict on exploratory learning behavior was insignificant, and relationship conflict had a positive effect on exploratory learning behavior. However, when CEO’s transformational leadership level was high, or transactional leadership level was low, there existed “bathtub curve” relationship between task conflict and exploratory learning behavior, and the relationship conflict under these conditions strengthened exploratory learning behavior. When CEO’s transactional leadership level was high, or transformational leadership level was low, there existed the inverted U-shaped relationship between task conflict and exploratory learning behavior, and the relationship conflict under such conditions weakened exploratory learning behavior.

First, the authors challenge the assumption of linear mechanism of task conflict, trying to build the mechanism of curve hypothesis, and the nonlinear explanation might be able to integrate the inconsistent results in the existing literature. Second, according to the inconsistent results of relationship conflict in existing literature, this study takes perspective of the CEO-TMT and introduces CEO leadership behavior as a moderating variable to test the moderating effect of CEO leadership and clarifies the boundary conditions of TMT conflicts.

Based on social information processing theory, this paper aims to explore how and when leader self-deprecating humor may spark subordinate learning from failure. The authors cast perspective taking as a novel explanatory mechanism for this indirect effect, and further consider leader–member exchange as a boundary condition of the relationship.

The authors tested the hypotheses by conducting a multiwave and multisource survey of 604 members from 152 teams in a Chinese high-technology company.

Results of multilevel path analyses demonstrate that leader self-deprecating humor positively influences subordinate learning from failure via perspective taking. Further, this mediation effect is stronger at higher levels of leader–member exchange.

This study contributes to the theoretical understanding of the relationship between leader self-deprecating humor and subordinate learning from failure. However, the research design was not longitudinal or experimental, and thus the authors were unable to make strong inferences about absolute causality.

The work yields useful insights for practitioners aiming to encourage subordinates to learn from failure.

This study provides evidence that leader self-deprecating humor can stimulate subordinate learning from failure via perspective taking, and the indirect effect is further strengthened by leader–member exchange. The findings offer new directions for research on leader self-deprecating humor and learning from failure.

The purpose of this paper is to examine how different conflict‐handling approaches moderate the relationship between conflicts and TMT as well as firm performance by adopting the Thomas terminology.

CEOs, TMT members, and lower‐level employees of 200 firms in the telecommunication industry in Mainland China were surveyed. Survey data collected from 123 CEOs, 442 TMT members, and 894 employees and archival data of firm performance were matched for hierarchical regression modeling (HRM) analyses.

Results show relationship conflict reduces team cohesiveness and both relationship and task conflicts are negatively related to firm performance. With relationship conflict, the results show using compromising approach could help reduce its negative effects on TMT cohesiveness and firm performance; but avoiding either type of conflict will undermine both team and firm outcomes. Accommodating approach does not have significant moderating effect on conflict‐outcome relationships.

The study reveals some interesting insights, but it does not include any contextual variables such as firm culture or team norms or climate which are shown to relate to both conflict types and conflict management approaches.

By examining the interplay between conflict types and conflict management approaches, the paper offers interesting insights into the connection between nature of conflicts, conflict management, employee‐rated TMT effectiveness, and objective firm performance.

The purpose of this paper is to examine team external learning, particularly focusing on: how leader external learning behavior affects team external learning; how team external learning influences employee creativity; and whether team internal learning is a moderator between the cross‐level relationship of external learning and employee creativity in Chinese R&D teams.

Survey questionnaire were administered to a sample of 80 R&D teams comprising 331 employees from three medium‐sized Chinese high‐tech companies. HLM was used to test cross‐level relationships.

The paper hypothesizes that: leader external learning positively influences team external learning; team external learning leads to employee creativity; and team internal learning moderates the relationship between team external learning and employee creativity. Results support hypotheses 2 and 3, but not hypothesis 1. In addition, leader external learning is found to positively influence employee creativity.

This study has implications for both team leaders and top management. Although the context of this study is in Chinese R&D teams, all those teams facing similar environment to R&D teams can also learn from this study. There are several interesting implications for scholars who are studying teams, creativity and innovation.

Combining fields of team learning, boundary spanning, creativity and leadership, the paper addresses the following four questions, and provides further insights to the area of team external learning: First, what is the cross level relationship between team external learning and employee creativity? Second, is internal learning a moderator on the relationship between team external learning and employee creativity in R&D project teams? Third, is leader external learning behavior one of antecedents of team external learning? Fourth, is there anything unique about team external learning in Chinese culture? The paper provides research evidence for each of these questions.

Based on natural-resource-based view (NRBV), the purpose of this paper is to clarify the dimensions of green procurement and the mechanisms involved in the relationship between green procurement and firm performance.

Secondary data were to measure all the variables in 206 Chinese A-share companies’ annual reports, social responsibility reports, environmental reports, and sustainability reports, which were published by Guotai Junan Securities Co., Ltd and Wind Information Co., Ltd.

The results indicate that although both product-based and process-based green procurement have a positive effect on firm performance, these Chinese companies focus on the impact of product-based green procurement on their operational efficiency as well as the moderating role played by stakeholder satisfaction.

Although studies on green procurement have increased in recent years, issues such as how to measure green purchasing behaviors and how green procurement affects firms’ operational and financial performance are still unexplored. Based on previous literature, two dimensions have been defined for green procurement in this study to develop corresponding measurements, namely, product-based green procurement and process-based green procurement. Besides, this study considers operational efficiency as the mediator and stakeholder satisfaction as the moderator when constructing a new conceptual model to illustrate the relationship between green procurement and firm performance.

With the ever‐increasing volume of text data via the internet, it is important that documents are classified as manageable and easy to understand categories. This paper proposes the use of binary k‐nearest neighbour (BKNN) for text categorization.

The paper describes the traditional k‐nearest neighbor (KNN) classifier, introduces BKNN and outlines experiemental results.

The experimental results indicate that BKNN requires much less CPU time than KNN, without loss of classification performance.

The paper demonstrates how BKNN can be an efficient and effective algorithm for text categorization. Proposes the use of binary k‐nearest neighbor (BKNN ) for text categorization.

The purpose of this paper is to propose a conceptual model to investigate the determinants of information retweeting in microblogging based on Heuristic‐Systematic Model.

Microblogging data about emergency events from Sina microblogging (http://weibo.com) are collected and analyzed with text mining technology. The proposed hypotheses are tested with logistic and multiple linear regressions.

The results show that source trustworthiness, source expertise, source attractiveness, and the number of multimedia have significant effects on the information retweeting. In addition, source expertise moderates the effects of user trustworthiness and content objectivity on the information retweeting in microblogging.

This study provides an in‐depth understanding of what makes information about emergency events in microblogging diffuse so rapidly. Based on these findings the emergency management organizations in China can apply the microblogging to spread useful information, and these findings also provide practical implications for microblogging system designers.

The primary value of this paper lies in providing a better understanding of information retweeting in microblogging based on Heuristic‐Systematic Model. Organizations that would like to adopt the microblogging platform in emergency situations to improve the ability of emergency response can benefit from the findings of this study.

The purpose of this paper is to analyze the impact of logistics performance on the carbon (CO2) emissions of Balkan countries.

Fixed-effects panel regression analysis is used to estimate the causal relationship between CO2 emissions and logistic performances of Balkan countries. Logistics performance is measured by logistics performance index (LPI) which was published by the World Bank in 2007, 2010, 2012, 2014 and 2016 and used for ranking countries by means of their logistics performance. LPI is based on six main indicators: customs procedures, logistics costs and the quality of the infrastructure for overland and maritime transport. As a measure of carbon emissions of sampled countries, the natural logarithm of carbon dioxide emission per capita is used in this study.

The results obtained reveal that there is a positive and significant relationship between logistics performance and CO2 performances of the sampled Balkan countries.

This study is based on only 11 Balkan countries. In this sense, the data used in the analysis is limited.

Considering the important geostrategic position of the Balkan region, logistics sector has an important role for the development of the countries in that region. In this sense, the findings of this study may provide useful insights for policymakers to achieve sustainable economic development. Furthermore, as far as the authors know, this is the first study that focuses on the relationship between logistics performance and carbon emissions of Balkan countries.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

This study aims to propose and estimate the urban tourism performance index (UTPI), an index that can measure and track the month-by-month tourism performance on main tourism cities since the initial outbreak of the COVID-19 pandemic. The UTPI is estimated for the following urban destinations: Bangkok, Paris, London, Dubai, Singapore, Kuala Lumpur, New York, Istanbul, Tokyo, Seoul, Osaka, Phuket, Milan, Barcelona, Bali and Hong Kong.

Monthly based data measuring the tourism industry’s performance for these urban destinations has been collected. This data includes airlines’ and hotels’ performance, as well as potential tourists’ online searches. The obtained data has been combined using a principal component analysis, generating the UTPI.

The UTPI shows that all urban areas analyzed in this study experienced a devastating negative impact because of the COVID-19 pandemic outbreak. However, the tourism recovery evolution follows heterogeneous patterns.

The proposed index can be estimated using additional variables. Moreover, the index is only estimated for 16 tourism cities. Future studies can reproduce the methodology by incorporating further variables and amplifying the geographical coverage.

The UTPI might be useful for researchers and policymakers interested in using a measure of tourism performance during the COVID-19 pandemic in some of the most important urban destinations. Likewise, the UTPI index may serve as a suitable aggregated measure of tourism performance in a post-COVID-19 era or to monitor tourism during future crises.

This study analyzes the tourism performance during the COVID-19 pandemic from an urban perspective.