Search results

This study aims to revisit the properties of 24-item version of mindfulness scale proposed by Bohlmeijer et al. (2011) in an Indian context to add to the existing global knowledge base on mindfulness.

A questionnaire was administered to 531 adult employees working in the IT/ITES sector in India. Their responses were analysed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and validity statistics to validate the Indian adaptation of the five-facet mindfulness questionnaire (FFMQ).

The findings confirmed that the Indian version of the 24-item short form of the FFMQ (denoted as FFMQ-SF) matches the findings of Bohlmeijer et al. (2011). It can, therefore, provide valuable insights to both employees and management on the benefits of mindfulness in the workplace.

This paper also presents the limitations of this work along with scholarly and practical implications. It enhances the global understanding of mindfulness, with applications in education, health and well-being, workplaces, social justice, spirituality and personal growth.

This study justifies and presents a unique instrument for assessing employee mindfulness and is beneficial for both management and employees in navigating the evolving hybrid work environment. It promotes present-moment awareness in a non-judgemental manner, facilitating perspective shifts, improved self-regulation and experiential acceptance. Additionally, the study affirms the five-dimensional structure underlying the mindfulness construct.

This study aims to investigate which configurations of organizational-level and group-level management controls support an identity fit for management accountants in the management accounting profession. It aims to complement recent qualitative management accounting research. This stream just begun to use role and identity theory to investigate role expectations, conflicts and coping strategies of management accountants when they struggle with their work identity.

Based on configuration theory, this study uses a fuzzy-set qualitative comparative analysis to indicate all possible configurations of formal and informal management controls that improve management accountants’ sense of their identity in an organization. The analyses are based on the results of a cross-sectional survey of 277 management accountants from Germany, Austria, Switzerland and Liechtenstein.

The results show that a strong group culture and high psychological safety at the group level are relevant conditions for a high identity fit. Further, the configurations differ regarding the career stages of management accountants.

This study contributes to work identity research of management accountants and to research on formal and informal control configurations as a control package. It is of particular importance for various professions that are affected by role change, as from the findings on management accountants’ identity fit, implications can also be made for other organizational functions that need to engage in identity work.

Organizations increasingly use knowledge arbitrage to stimulate innovation and achieve competitive advantage. However, in knowledge management its use in startups is yet unexplored. This study aims to examine the utilization of knowledge arbitrage by startups, specifically during COVID-19.

This study employed an open-ended essay methodology to explore the drivers and barriers that startups face in utilizing knowledge arbitrage. We collected data from 40 participants to understand the role of knowledge arbitrage in startups’ knowledge management practices.

This study’s findings highlight the significance of knowledge arbitrage for startups. The benefits identified include organizational benefits such as building networks, innovating new products and achieving competitive advantage and financial benefits such as cost reduction and sales growth. The study also identifies several technological and organizational drivers and barriers that startups confront during knowledge arbitrage.

This study contributes to the existing literature on knowledge management by extending our understanding of knowledge arbitrage’s role in startups. Additionally, it sheds light on the importance of knowledge arbitrage for startups and the challenges they face, particularly in a disrupted environment reared by COVID-19. The study provides insights for the scholars and practitioners interested in effective knowledge management in startups.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

The purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.

This study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.

The findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.

Process mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.

In a dynamic and complex environment, employees’ creative performance (CP) can be essential in developing a distinguished and competitive strategy for an organization. Using the lens of competency management, this study aims to examine how employees perceived environmental uncertainty (PEU) and competency formula relate to employee CP, with a focus on the hospitality industry.

The data was collected from employees in the hospitality sector. Both symmetrical (PLS-SEM) and asymmetrical (fuzzy-set qualitative comparative analysis [fsQCA]) tests were performed to gain in-depth knowledge of how individual, organizational and environmental factors can be configured to explain employees’ CP.

The symmetrical analysis shows that the competency formula mediates the negative impacts of PEU on two dimensions of creativity – that is, novelty and utility. The fsQCA testing generated contrasting findings and revealed that uncertainty, along with the formula elements, is a unique antecedent condition and opportunity for employees’ CP. The inconsistent findings indicate asymmetrical and complex relationships between the proposed antecedents and outcomes in the case of employee creativity.

A combination of symmetrical and asymmetrical approaches is necessary to uncover the complex relationships among employees, organizations and the environment. This study shows that organizational agility, competency strategies and comprehensive strategic management processes can be configured to explain positive outcomes for organizations during uncertain circumstances. The findings can be used by human resource practitioners to maximize employee creativity and enhance organizational performance.

To the best of the authors’ knowledge, this study is the first to use symmetrical and asymmetrical testing to address the inadequacy of explaining employee CP in complex and uncertain environments, and highlight the crucial role of the competency formula in enhancing novelty and utility dimensions of CP. This research examines the impact of various internal and external factors (i.e. individual, organizational and contextual) on employee creativity within the hospitality industry.

This paper aims to investigate the potential mediating effect of environmental disclosure on the relationship between corporate governance and the disclosure of social information by disaggregating Bloomberg ESG (Environmental-Social-Governance) scores. The polluting level of a company is examined for its potential moderating effect.

The focus is on the S&P 500. A structural equation model (SEM) is proposed that considers the effects of governance board constructs on the voluntary disclosure of social information (S-score) mediated by the voluntary disclosure of environmental information (E-score). The model is fit separately for two groups of companies (high-polluting and low-polluting), and the path coefficients are compared.

Consistent with prior research, board independence, gender diversity, and size positively impact voluntary environmental disclosure; board age is found to have a significant but negative effect. The estimated path coefficient from E-score to S-score is strong, positive, and significant; environmental disclosure fully mediates the relationship between corporate governance and social disclosure. This path coefficient is significantly greater for those companies in the high-polluting group.

The findings indicate that high-polluting companies may engage in increased voluntary disclosure of social information as reputation insurance. E-score fully mediates the relationship between corporate governance and S-score more strongly for high-polluting companies, suggesting this group is more likely to engage in and report on socially responsible behaviors to deflect attention away from environmental performance (i.e. greendeflecting).

Using social and moral identity theory, this study aims to investigate the influence of perceived greenwashing on employee work attitudes by using social and moral identity theory. By examining the relationships between perceived greenwashing, employee cynicism, work alienation and turnover intention, this study unveils essential mechanisms that shed light on the complex relationship between these variables.

The study gathered data from a sample of 267 employees in the service industry and used variance-based structuring equation modeling to test the hypothesized associations. The results of the study indicated a positive relationship between perceived greenwashing and turnover intention.

Employee cynicism and work alienation emerged as crucial mediating factors, revealing the underlying psychological dynamics linking perceived greenwashing to turnover intention. Moreover, the study identified organizational pride as a powerful moderator that mitigates the adverse effects of greenwashing on employee attitudes.

Genuine and transparent environmental practices are crucial in the service industry to avoid misleading claims, safeguard reputation and establish trust. Leaders should exemplify genuine commitment to environmental practices, serving as role models. Regular and honest feedback mechanisms should be established to gauge employee perceptions of the organization’s environmental initiatives. Educating employees about the signs of deceptive practices can empower them to make informed judgments, reducing the likelihood of falling victim to misrepresentations and mitigating associated negative outcomes.

The current research seeks to shed light on the profound impact of greenwashing on employees, an area that has been surprisingly overlooked. The study responds to the call of the antecedents that influence employees’ intentions to leave their organizations. The study explored the vital relationship between perceived greenwashing and employee attitudes, thereby contributing valuable insights to the existing literature on the sustainable practices of organizations, particularly those in the service industry.