Search results

Drawing on prospect theory, terror management theory, and social influence theories, this study explores the phenomenon of panic buying amid the COVID-19 pandemic, namely its situational antecedents such as fear of COVID-19, increased media exposure to COVID-19-related news, and context-specific paranoia. It offers insight into the situational nature of panic buying, contrary to the purely dispositional/trait conceptualization of irrational spending, usually depicted through the phenomenon of compulsive buying.

This is a cross-sectional study. An online questionnaire was used for data collection from 621 Croatian citizens. The questionnaire features a series of validated instruments designed to measure compulsive buying, fear of COVID-19, and context-specific paranoia. The media exposure scale (MES) was also specifically developed and empirically tested for the purpose of this research.

The results suggest that individuals who exhibited greater fear of COVID-19 while also experiencing increased exposure to COVID-19-related news were more likely to engage in panic buying. This connection has remained significant even after controlling for compulsive buying tendencies, suggesting that panic buying witnessed during the coronavirus pandemic was a situational phenomenon, not strictly dispositional. This establishes the fear of COVID-19 and increased exposure to pandemic-related news content as situational antecedents to panic buying. After controlling for compulsive buying, this paper does not demonstrate a significant connection between context-specific paranoia and panic buying. Furthermore, context-specific paranoia does not mediate the relationship between media exposure to pandemic-related content and panic buying, whereas the fear of COVID-19 significantly mediates the same relationship.

This study recognizes people's panic behavior amid the COVID-19 pandemic as a byproduct of a situational, reactive process – not a psychopathological one. Furthermore, it recognizes media sensationalism and the audience's impaired capacity for rational spending as major risk factors preceding the event of panic buying.

This study proposes a novel conceptual framework of irrational spending amid crises such as COVID-19 pandemic, introducing the differentiation between the situational nature of the phenomenon (panic buying), thereby separating it from its previous dispositional operationalizations (hoarding, compulsive buying).

This study aims to examine conditions in both inter- and intra-alliance contexts within an oligopolistic alliance industry operating across multiple markets. It focuses on how a focal firm’s optimal performance depends on nuanced evaluations of the trade-offs associated with coopetitive synergy, and on decisions about whether to collaborate or compete with its members.

The authors analyze the six leading global container shipping firms within two major alliances (The Grand Alliance and the New World Alliance) from 2003 to 2010, gathering 7,825 news articles from the Cyber Shipping Guide, a comprehensive global container shipping business database in Japan.

The findings reveal the following: (1) the focal firm cooperating with members of a rival alliance decreases the level of inter-alliance competition. (2) The focal firm cooperating with members of a rival alliance increases the level of intra-alliance competition. (3) Increased inter-alliance competition negatively impacts the performance of the focal firm. (4) Increased intra-alliance competition negatively impacts the performance of the focal firm.

Global container shipping firms should make optimal decisions about which firms to cooperate with, focusing on those that contribute to the focal firm’s overall synergies and thus performance.

This study contributes to the literature on coopetition in strategic alliances by extending the concept of dynamic coopetition to include strategic alliance groupings, and by examining how focal firm members cooperate in both inter- and intra-alliance contexts.

The societal, economic and personal costs of aggression are indisputable. Impulsivity and childhood trauma (CT) play a role in aggression but less is known about the potential mechanisms underlying these associations. This study aimed to investigate the influence of CTs and impulsivity on aggression in the general population.

A total of 178 participants (aged 18–86, M = 30.93, SD = 14.50) including 65 men (36.5%), 110 women (61.8%), 3 participants self-identified (1.7%)(n = 2 nonbinary, n = 1 gender fluid) of the UK adult population completed an online survey. Questionnaires measured impulsivity (Short UPPS-P), adverse childhood experiences (CT Questionnaire) and aggression (Buss and Perry Aggression Questionnaire).

Emotional neglect and abuse were the most endorsed CTs (abuse and neglect). As predicted, results showed the impulsivity facet “negative urgency” was associated with the behaviour, emotions and cognitions of aggression. Findings showed a distinct effect of both impulsivity and emotional abuse on physical aggression, which may reflect a pathway in which impulsivity influences adverse childhood experiences and future violence. Types of aggression may have potentially distinct pathways. This study discusses the reasons for these observed results and future research.

The originality/value of the paper lies in the acknowledgement of the role of negative and positive urgency in behaviours related to emotional dysregulation. It also highlighted the importance of examining different types of aggression. There was a distinct effect of both impulsivity and CTs on physical aggression and hostility. Further research in larger samples should examine pathways in which impulsivity mediates the effects of adverse childhood experiences and adulthood aggression. These collective insights can help further our understanding of the role adverse and traumatic events in childhood and impulsivity has on aggression and may be relevant to tailored support and intervention strategies for individuals expressing aggressive behaviours.

This paper aims to map the antecedents and precursory contexts regarding the four principles of management. Moreover, a description of its codification and coalescence as a unified teaching framework is provided, critically reviewing key theoretical underpinnings of management principles in academic research and management textbooks.

A historiographic approach reviewed seminal works for theory origins of the four principles of management, by analyzing 260 management textbooks from 1935 to 2013 to document their adoption in management education. This study used critical hermeneutics (Prasad, 2002) to explore the framework’s progression by providing the context of cultural, political and economic influences.

This research study tracked and mapped the creation of the four principles of management, as it became the commonly accepted teaching framework in management education. Today, every predominant management principles textbook uses the four principles of management – plan, lead, organize and control – as the basis for teaching students.

There is limited research on the application of the four principles of management in contemporary management, despite its ubiquity in management education. The study’s historical account of its formation provides insights into its adoption and utilization in modern education context. The study’s primary limitation stems from the generalization of the representative sample of textbooks used in the study (1917–2013). However, data saturation was achieved for the scale of textbooks and writings which was reviewed.

Through a critical analysis into the formation of the four principles of management, this research not only provides a historical account of its construction but, as importantly, the influencing factors that led to its development. This research fills a gap in critical literature, as a post mortem exegesis has never been conducted on the four principles of management in the afteryears of its amalgamation.

This paper examines the effect of restrictions on executive pay and high CEOs’ compensation on bank performance following the “2008 UK bank rescue policy”.

Using the difference-in-difference estimation technique we assess the relationship between executive compensation and financial performance of rescued banks relative to non-rescued banks over the period 1999–2019.

Our main finding indicates that the relationship between executive compensation and financial performance declines in rescued banks relative to non-rescued banks. Further, we document that performance continues to deteriorate in rescued banks relative to non-rescued banks. Our results are robust to different estimation techniques.

This study contributes to the literature that examines the efficacy of government bailouts during the 2008 crisis. To the best of the author’s knowledge, this study is among the first to examine the long-term implications of bank rescue and pay restrictions on executive compensation and performance post–rescue.

The purpose of the paper is to examine the trade structure of India’s pharmaceutical sector with a focus on intra-industry trade (IIT).

This paper starts with analysing export destinations and import sources using significant trade shares; the study calculates IIT between India and its consistent trade partners at an aggregate level and considers the problem of categorical aggregation at a disaggregate level. To determine the determinants of IIT at different levels, the Vector Error Correction model used production-related data to identify the drivers of IIT. Also, the Granger causality test was used for short-run causality.

This study examining India’s consistent trade partners from 1993 to 2023, finds long-run association and short-run causality. The results show a significant long-run association between total IIT and factors like unskilled labour share, invested capital, fuel consumption, total input and net value added. The key low-vertical IIT (LVIIT) drivers are invested capital, unskilled labour, fixed capital and total inputs. The negative long-run association between the total input and LVIIT obtained implies a rising level of total input cost, leading to a fall in IIT and LVIIT. Also, a negative association is obtained for unskilled labour and total IIT, while a positive association is obtained for LVIIT. In the short run, causality indicates that total IIT is influenced by invested capital and fuel consumption, while unskilled labour shares and total inputs drive LVIIT. Both IIT types impact invested capital, highlighting the need for policy intervention in input markets. It provides insights for improving quality trade expansion and correcting production-related factors.

Unlike other studies on the pharmaceutical trade in India, this study analyses India’s pharmaceutical trade for a longer time period, focusing on destination-wise analysis and calculating the intra-industry trade index while taking care of the problem of categorical aggregation. Further, the study attempted to find the long-run association with production-related drivers.

In addition to financial performance, firms are increasingly trying to obtain a social reputation from their corporate social responsibility (CSR) engagement within society due to reputational benefits. Thus, the authors seek to highlight two facilitators of social reputation which may help firms realize their targets. Hence, drawing on the signaling, stewardship and legitimacy theories, this study aims to investigate whether chief executive officer (CEO) power and firm visibility help translate CSR engagement into greater social reputation, proxied by CSR awarding.

Adopting a cross-country and cross-industry sample of 52,549 observations between 2002 and 2021, the authors run a fixed effects regression analysis.

The authors found that greater CSR engagement leads to better social reputation. Furthermore, CEO power and greater firm visibility foster a positive association between CSR engagement and social reputation. The results are robust to endogeneity concerns, which were addressed by propensity score matching, entropy balancing, instrumental variable regression analysis, alternative samples and regulatory changes.

Although the CEOs’ power is severely criticized in the corporate governance literature due to its weakening effect on board monitoring ability, the authors found that it is beneficial for firms seeking to improve their social reputation. This outcome may help firms shape their upper management structure for greater social reputation gains from CSR engagement. Furthermore, more visible firms achieve greater social reputation through their CSR engagement, which could help managers co-consider firms’ advertising–CSR awarding engagements and budget their financial resources accordingly.

Increasing the CSR engagement of firms has prompted investigations into how firms may better benefit from this investment. However, despite considerable research interest in the financial return of CSR engagement, the social reputation that firms derive from CSR engagement has not been sufficiently addressed. Thus, the authors examine whether two corporate mechanisms, CEO power and firm visibility, could help firms translate CSR engagement into improved social reputation, proxied by CSR awarding.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

The ability to create and sustain competitive advantages depends on cultivating employee’s capabilities. Entrepreneurial orientation (EO) can foster an organisation-wide culture of exploring new opportunities and creating new learnings. Sustainable competitive advantages should be based on long-term behavioural changes rather than ad hoc adjustments made for short-term gains, enabling employees to become a source of unique and inimitable advantages. This study aims to explore how each external environment impacts the dimensions of EO. The study also introduces environmental jolts as a dimension of the external environment.

The authors considered a sample of 39 organisations in IT companies from India to study the external environment’s effect on their EO. Using covariance-based structural equation modelling, the authors measured the impact of external environment variables on EO. A total of 250 responses were found suitable for analysis.

Certain crucial factors were identified through an extensive analysis of the relationships between individual factors of the external environment and EO. Technological opportunities showed a strong positive association with all factors of EO, whereas dynamism of the environment had a positive relationship with innovativeness and proactiveness. Environmental jolts showed a negative impact on innovativeness and risk-taking propensity.

EO has been developed and researched extensively in the Western context as a unidimensional construct. In the present study, the relationship between the external environment factors and each dimension of EO has been analysed individually, thus following a multidimensional approach. Moreover, environment jolts as a factor of the external environment have been introduced, and their effect on the dimension of EO has been studied. Finally, the implications of encouraging entrepreneurial behaviours to develop sustainable competitive advantages have been discussed.

The research explores the multidimensionality of the EO construct and also introduces environmental jolts as a dimension of the external environment.

Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.