Search results

This study introduces the Harmonized Information-Technology and Organizational Performance Model (HI-TOP), which addresses the need for a holistic framework that integrates technology and human dynamics within organizational settings. This approach aims to enhance organizational productivity and employee well-being by aligning technological advancements with human factors in the context of digital transformation.

Employing a two-phased methodology, the HI-TOP model is developed through a literature review and text mining of industry reports. This approach identifies and integrates critical themes related to ICT integration challenges and opportunities within organizations.

This research indicates that successful ICT integration requires balancing technological advancements with human-centric considerations, including addressing technostress and promoting skills development. The HI-TOP model’s four components – Workforce Empowerment and Resource Strategy (WERS), Technology-Enhanced Information Architecture (TEIA), Organizational Information Processing Strategy (OIPS) and Knowledge Sharing Platform (KSP) – demonstrate operational and strategic synergy required to achieve enhanced organizational performance and adaptability.

The HI-TOP model contributes to the body of knowledge by providing a structured framework for understanding the interplay between technology and organizational dynamics, with an emphasis on employee well-being and overall organizational performance. Its originality lies in the integrative approach to model development, combining theory with empirical insights from industry data, thus offering actionable guidance for organizations navigating the complexities of digital transformation.

This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature.

This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations.

The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service.

The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties.

The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly.

The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains.

Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.

This case was developed from secondary sources such as industry reports, articles, news reports and social media sites.

This study offers a fresh perspective on leadership by exploring the nontraditional journey from human resources (HRs) positions to the esteemed chief executive officer (CEO) role. It highlights the transformation of HR from an administrative function to a strategic one. The study emphasizes the pivotal role of chief human resources officers (CHROs) in shaping company culture and ensuring employee satisfaction. It also delves into HR professionals’ unique skills and attributes to the CEO position, drawing examples from successful transitions such as Mary Barra at General Motors, Leena Nair at Chanel and Nigel Travis at Dunkin Donuts. The study addresses the growing trend in contemporary business discussions: the potential reshaping of the traditional CEO role by HR directors. It offers valuable insights for organizations looking to adapt to a rapidly evolving economic landscape by highlighting the synergy between CEO responsibilities and HR expertise.

This case is designed for undergraduates in various courses such as fundamentals of HR management, human capital management, strategic management, leadership development and career planning and management. It is appropriate for sections of the course focusing on managerial decisions and the changing role of HR managers. The case discussion is suitable for classes of 12–40 students and can be easily adapted for online courses using interactive discussion tools. Students are expected to read the case before participating in the discussion.

The study aims at investigating the impact of real earnings management (REM) on the cross-sectional stock return after considering the moderating role of market effect, size effect, value effect and momentum effect.

The study uses weekly and monthly data of 3,085 Bombay Stock Exchange listed stocks spanning over twenty years, from January 2000 to December 2019. REM is measured through metrics developed by Roychowdhury (2006), namely, abnormal levels of operating cash flows, production costs and discretionary expenditure. The study employs univariate and bivariate portfolio-level analysis.

The findings deduced from the empirical results demonstrate that investors perceive downward REM as an element of risk; hence, they discount the stock prices at a higher rate. On the contrary, results show that investors positively perceive upward REM; hence, they hold the stocks even at a lower rate of return. This anomaly is found to be robust for all kinds of considered moderations.

The findings have important managerial implications as investors are found to assign different weights to different forms of REM, depending upon the perception regarding the magnitude of risk involved in different forms. Managers can accommodate this information during their short- and long-term corporate planning.

First, the study is among the earlier attempts to examine the association between REM and stock returns by considering the moderating role of cross-sectional effects. Second, the study considers the direction and endogenous nature of REM while investigating the issue.

Task conflict and relationship conflict are common in organizations. This paper aims to present a unique case of the use of the targeted conflict-resolution technique. The revival of positive group dynamics is aptly shown.

This descriptive case study is developed as a practice insight to showcase how a peculiar case of misunderstanding is resolved in the most unconventional way through the intervention of a mediator who unearths the real cause of contention. The mediator works through logic and emotion to remove negativity. Narration, a necessary component of the case study approach, peeps into the research subject involving flashbacks, flash forward, backstories and foreshadowing. The mediator uses reframing as a tool very efficiently, encouraging the people in conflict to understand the nothingness in their cold war and eventually prompting them to collaborate and compromise.

The shifts in communication dynamics post-mediator’s intervention are subtle and full of wisdom, encouraging introspection and constructive interaction, eventually bridging the differences. The possibility of achieving a state of homeostasis in the future magnifies. The belief in the power of affirmation and manifestation is validated. The heavy, difficult, hardened negativity loses ground and gets transformed.

Conversation/prayers at the deepest level in several meetings are the communication tools that have immense social relevance in the Indian context.

A unique combination of intermediation encompassing written communication and energy transformation is adopted to resolve ongoing conflict by stroking the positive psychology of the partakers. To some, the method may appear to have a spiritual connotation.

This study aims to examine how career commitment (CC) affects hospitality employees perceived subjective career success (SCS) using a serial mediation system that uses career resilience (CR) behaviour and self-efficacy (SE) as serial mediators.

To examine the proposed relationships, a comprehensive theoretical serial mediation model was constructed. The proposed hypotheses were tested on a sample of 316 hospitality employees from India using regression analysis (Process Macro Model 6) with 5,000 bootstrapping at 95% confidence interval.

The regression analysis confirmed a strong, positive relationship between CC and SCS. It also highlighted a significant indirect effect, involving a serial mediation through CR and SE, demonstrating how CC influences SCS.

Drawing from three prominent theories – career self-determination theory (CSDT), career construction theory (CCT) and the contextual action theory of career development (CATCD), this research underscores the importance of fostering career-committed behaviour among employees and advocating for strategic career development initiatives. Understanding CC elements may help firms retain and encourage individuals, which can boost job satisfaction and performance. Importantly, the results advocate for the implementation of targeted career development strategies and the promotion of career-committed behaviour within hospitality organisations. This, in turn, fosters resilience and competence, ultimately contributing to individual career success and organisational excellence.

A serial mediation model with CR and SE introduces a fresh perspective that, to the best of the authors’ knowledge, has not been extensively explored in previous research examining the relationship between CC and SCS.

The growing demands of work and life have shifted the concept of work-life balance to work-life integration (WLI). The success of integration depends upon the flexibility to perform the duties. This paper aims to explore the factors that affect WLI and the role of flexible work arrangements (FWAs) in the process of WLI.

Systematic literature review was used to explore the concept of WLI and FWAs. A bibliometric analysis was carried out with Bibexcel and VoSviewer.

This paper explained the organizational and personal factors that create the demand for WLI. The FWAs, perceived flexibility, technology and self-efficacy have important roles in WLI. The result of WLI can be enrichment or strain, depends upon how effectively the work-life domains are integrated.

This paper explores the work-life from both personal and organizational views. The findings of this paper will be useful to design the organizational policies and work arrangements that match the requirements of employees and organizations. This paper helps to develop the future research agenda of investigating the relations of WLI to performance, organizational policies and personal factors.

The advent of e-retailing has created multiple options for customers. Hence, most important concern is to identify the experience which entices customers for repurchase from the one particular e-retailer. This paper tries to identify the customer experience with reference to activities happening post-purchase. The purpose of this paper is to develop and validate online post-purchase customer experience (OPPCE).

The methodology of this scale development study starts with skimming of relevant literature to identify the knowledge gap and prepare a theoretical background for the study. Then scientific method is applied for scale creation. First, items of the scale been identified through interviews of online shoppers and marketing experts. Then the major dimensions were identified through exploratory factor analysis applied on the data collected from active shoppers, who transacted online in last six months, with the help of a structured questionnaire survey. These data are analyzed through structural equation modeling to validate the scale.

The study yields that the scale for measuring OPPCE is multi-dimensional. It has six dimensions, i.e. delivery, product-in-hand, return and exchange, customer support, benefits and feel-good factors. The proper focus on the items of these dimensions can help e-retailers improve customer experience and increase repeat purchase.

Post-purchase activities have a significant impact on customer and their repeat purchase intensions. But it has not received its due attention particularly in the online context. Hence, this paper fills this knowledge gap and gives e-retailers a tool to enhance their customers’ experience.

The purpose of this paper is to study the influence of inflated energy prices on the capital structure of Indian manufacturing corporations and to investigate whether the capital structure of Indian firms is driven by demand shocks or supply shocks during the study period.

After conducting a thorough review of the capital structure and inflation-based research studies, panel data-based regression model and correlation matrix have been used as statistical tools for Indian manufacturing sector available with the Centre for Monitoring Indian Economy Prowess database.

The results suggest that variables like the presence of inflated energy prices had adversely influenced the capital structure of Indian corporations. Not only this, the study also highlights that factors pertaining to the demand shock had induced Indian corporations to have higher debt levels in the capital structure.

This study has laid some ground work to explore the influence of inflation on capital structure of Indian firms upon which a more detailed evaluation could be based.

To the best of the authors’ knowledge, this study is the first that explores the influence of inflated energy prices on the capital structure of manufacturing firms in India by using the most recent data.

This study aims to integrate the social cognitive theory and flow theory to examine how e-skills self-efficacy (ESSE) and trust-building self-efficacy (TBSE) impact work-related flow in remote work. It also explores how work-related flow (WOLF) mediates the connection between these factors and employee resilience (ER) in the context of the growing shift toward remote work.

The study tested an empirical mediation model with 535 remote workers from Indian organizations that adopted remote work either before or during the pandemic. Hypotheses were tested using Hayes’ PROCESS macro.

The study confirmed the direct influence of ESSE and TBSE on WOLF. Moreover, a mediation analysis demonstrated that WOLF significantly mediates the association linking ESSE, TBSE and ER.

This study, evaluating experienced remote employees in the quaternary sector, offers data-driven insights for informed organizational decisions. These findings aims to guide HR and training departments in formulating recruitment and training strategies, emphasizing digital skill development to enhance knowledge and skills effectively.

This study highlights the need for remote workers and organizations to be aware of the challenges of remote work. It is among the first to explore the link between ESSE, TBSE and ER, addressing psychological factor, i.e. WOLF, often overlooked in organizational contexts.