Search results

This study examines whether and how the legal origin of foreign institutional investors (FIIs) impacts corporate investment efficiency.

The study employs a large panel dataset of firms from 32 non-USA countries from 2005 to 2018. Financial and institutional ownership data are obtained from the COMPUSTAT Global and Public Ownership databases in S&P Capital IQ, respectively. The study employed ordinary least squares (OLS) regression with year and firm fixed effects. In addition, two-stage least squares with instrumental variable regression (2SLS-IV) and propensity score matching (PSM) approaches were employed to address the potential endogeneity.

The findings of this study suggest that common- and civil-law FIIs differ in their monitoring capabilities to promote investment efficiency. The authors find evidence that increased equity ownership by common-law FIIs, not civil-law investors, strengthens the investment-Q sensitivity, resulting in higher investment efficiency. Consistent with the monitoring and information channel, the results further indicate that the positive impact of common-law FIIs on investment efficiency is stronger in host environments susceptible to agency conflicts and information asymmetry.

This study offers novel evidence on the heterogeneous monitoring role of FIIs with regard to their home countries' legal origins and their impact on investment efficiency in an international context.

The numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental risks.

Based on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and K-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed method.

The results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive factors.

This paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

This study aimed to identify subgroups of esports players based on their gaming behavior patterns across game genres and compare self-efficacy, social efficacy, loneliness and three dimensions of quality of life between these subgroups.

324 participants were recruited from prolific academic to complete an online survey. We employed latent profile analysis (LPA) to identify subgroups of esports players based on their behavioral patterns across genres. Additionally, a one-way multivariate analysis of covariance (MANCOVA) was conducted to test the association between cluster memberships and development and well-being outcomes, controlling for age and gender as covariates.

LPA analysis identified five clusters (two single-genre gamer groups, two multigenre gamer groups and one all-genre gamer group). Univariate analyses indicated the significant effect of the clusters on social efficacy, psychological health and social health. Pairwise comparisons highlighted the salience of the physical enactment-plus-sport simulation genre group in these outcomes.

This study contributes to the understanding of the development and well-being benefits experienced by various esports consumers, as well as the role of specific gameplay in facilitating targeted outcomes among these consumer groups.

The purpose of this paper is to offer an accessible and interdisciplinary research strategy in organisational ethnography, called action ethnography, that acknowledges key concepts from action research and engaged and immersive ethnography. It aims to encourage methodological innovation and an impact turn in ethnographic practice.

A working definition of “action ethnography” is provided first. Then, to illustrate how an action ethnography can be designed by considering impact from the outset, the author draws on a study she is undertaking with a grassroots human rights monitoring group, based in England, and then discusses advantages and limitations to the approach.

The author suggests three main tenets to action ethnography that embrace synergies between action research and ethnography: researcher immersion, intervention leading to change and knowledge contributions that are useful to both practitioners and researchers.

This paper provides researchers who align with aspects of both action research and ethnography with an accessible research strategy to employ, and a better understanding of the interplay between the two approaches when justifying their research designs. It also offers an example of designing an action ethnography in practice.

Whereas “traditional” ethnography has emphasised a contribution to theoretical knowledge, less attention has been on a contribution to practice and to those who ethnographers engage with in the field. Action ethnography challenges researchers to consider the impact of their research from the outset during the research design, rather upon reflection after a study is completed.

This study explores how businesses can effectively market functional and emotional benefits through virtual corporate social responsibility co-creation (VCSRC) initiatives. The aim is to enhance customer engagement through these initiatives.

The initial phase of this study involved recruiting 185 Chinese university students as participants. These individuals were randomly distributed into four distinct experimental groups, each designed to investigate the influence of varied marketing appeals and common in-group identity (CIGI) on consumer engagement willingness. This phase also sought to elucidate the mediating role of CSR associations in these dynamics. Following this, the second study engaged 570 individuals, recruited through “Credamo,” for a group-based experiment. This subsequent phase was intended to validate the findings of the preliminary study and explore the varying intensities of interaction between different marketing appeals and CIGI, with a particular focus on the dichotomy of independent and interdependent self-construals.

The study delineates a detailed relationship between consumers' willingness to participate in VCSRC, marketing appeals, and common in-group identity, revealing that strong common in-group identity correlates with a preference for functional appeals, while a weaker in-group identity inclines towards emotional appeals. Notably, interdependent self-construal significantly influences consumer responses, intensifying the interaction between in-group identity and marketing appeal and thereby influencing participation willingness. Moreover, CSR associations emerge as key mediators in this interaction, underscoring their role in enhancing the effectiveness of VCSRC strategies. These insights provide a new understanding of the crucial impact of consumer identity traits on marketing strategy efficacy.

This research stands as a trailblazing endeavor in evaluating the effects of varied advertising appeals under the VCSR paradigm. It probes into the foundational mechanisms, leveraging insights from interaction alignment theory and persuasion theory to elucidate the processes involved.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

The lack of progress toward equity in the U.S. is evident across many spheres of society, academia notwithstanding. Women academicians, in particular, face many barriers that prevent them from advancing–including a continued unsupportive climate, competing work and family demands, and interpersonal discrimination. This paper reflects on a collaborative research effort in the United States to enhance allyship for women in academia.

The authors partnered with a major university to hold ally training for department chairs during a university-wide department chair meeting. The authors developed a methodology for creating and implementing training content using a focus-group-based training needs analysis and a diversity science grounded approach to allyship training. The authors followed this up with surveys to assess impact.

Participants indicated that they learned from the training, but participation in follow-up data collection was limited, hampering the ability to conduct rigorous quantitative analyses around intervention impact.

Although the sample size may have been too limited to detect effects, the current study provides an approach that furthers the way in which researchers and practitioners can better assess the impact of allyship to women academicians.

Published research on allies is very limited. The current research examines allies in the context of helping women in academia.

Despite widespread recognition of the importance of first-line supervisors in support of diversity, limited intervention designs are available. The authors add to the extant literature on diversity interventions, while highlighting barriers to rigorous intervention evaluation.

This study examined the differential effects of printed advertisements with luxury and nonluxury brands on consumer brand attitude persuasion using a qualitative experimental approach.

The authors adopted a qualitative experimental approach and the authors conducted two experiments over six months. In the first experiment, participants were asked to view five print advertisements related to five different luxury brands. In the second experiment, the same participants were asked to view another five print advertisements on non-luxury brands. The qualitative thematic differences for each brand were analyzed using NVivo software, employing the theoretical assumptions of Petty and Cacioppo's (1981) elaboration likelihood model (ELM).

In experiments 1 and 2, it was identified that brand experience, personalized brand experience, product quality, product quantity, personal image-conscious, nonpersonal image-conscious, affordability and unaffordability as the main thematic findings leading to consumer attitude persuasion.

The two main contributions are as follows: theoretically, applying a social psychology theory to the advertising industry offers an understanding of the social cognition stages of a human mindset. As a practical implication, this study's findings guide advertising agencies, marketers and salespeople regarding how to design effective print advertisements in a way that persuades consumer attitudes.

Through the theoretical assumptions of Petty and Cacioppo's (1981) ELM, this paper can be considered one of the first studies to combine social psychology and advertising to investigate the differential effects on consumer brand attitude persuasion for luxury and nonluxury brands.

This study aims to identify, analyze and rank the critical success factors (CSFs) of Lean Six Sigma (LSS) implementation in Indian manufacturing sector based micro, small and medium enterprises (MSMEs). This study provides critical insight for managers and researchers aspiring for successful implementation of LSS in Indian manufacturing MSMEs.

The CSFs were extracted from literature followed by a questionnaire-based survey from 120 industry professionals with extensive knowledge and experience about LSS working in Indian manufacturing MSMEs. Further, the CSFs were grouped based on their fundamental relevance and ranked using best worst method (BWM) approach using inputs from LSS experts.

This study provides insights on success factors that have helped Indian manufacturing MSMEs to implement LSS. The findings signify that “Strategy based CSFs” were ranked as the top most important factors, followed by two other category factors namely “Bottom-Line CSFs” and “Supplier based and other category-based CSFs”.

The proposed research is specifically relevant to the context of MSMEs in the Indian manufacturing sector. In the future, the same approach can be extended to a global context, encompassing service sector-based MSMEs in healthcare and finance.

This study provides valuable inputs for managers, decision-makers, industrial practitioners and researchers about Indian manufacturing MSMEs. The identified CSFs and their prioritization offer a roadmap for successful adoption of LSS. Managers can allocate resources, and make strategic decisions based on the prioritized CSFs. Decision-makers can align their initiatives with the identified CSFs. Industrial practitioners gain insights to enhance their LSS initiatives, and researchers can focus their efforts on areas critical to LSS implementation in Indian MSMEs. Furthermore, the structured approach employed in this study can be adopted by various MSME sectors globally, thereby broadening the comprehension of LSS implementation.

This study contributes to the existing body of knowledge by addressing the gaps in literature on CSFs related to LSS adoption within Indian manufacturing MSMEs. While LSS has been widely studied, there is limited focus on its adoption in the context of Indian MSMEs. The combination of extensive literature review, questionnaire-based survey and the application of the BWM approach for prioritizing CSFs adds originality to the research.

The objective of this study is to explore the structural attributes of the pharmaceutical industry before the onset of the COVID-19 pandemic by examining the relationship between inventory and firm performance and developing a taxonomy of pharmaceutical firms based on the earns-turns matrix.

This study examines the inventory–firm performance linkage, considering both total inventory and its discrete inventory components in pharmaceutical firms. In addition, this research develops a new taxonomy of pharmaceutical firms based on the earns-turns matrix. A large panel dataset of firms in the US pharmaceutical industry was collected for the period 2000–2019.

The results reveal that strategic groups identified based on this taxonomy show different levels of profitability and inventory turns in the earns-turns matrix. Most pharmaceutical firms moved from the low-right to the top-left section in the earns-turns matrix, indicating that these firms have generally pursued profitability rather than effective inventory management.

This study explores the structural attributes of the pharmaceutical industry using the earns-turns matrix. This two-dimensional analysis may not, however, capture the full complexity of inventory–firm performance dynamics.

The mapping of strategic groups on the earns-turns matrix provides a useful tool for visual representations of the dynamics of strategic groups in terms of financial performance and inventory management performance. Practitioners can use the earns-turns matrix to benchmark their firm's position against their competitors.

This study broadens the scope of operations management research by introducing the earns-turns matrix as an empirical validation tool for operational and strategic management theories. This study emphasizes the effectiveness of the earns-turns matrix in analyzing strategic groups of pharmaceutical firms.