Search results

The businesses at the global level are surfacing precipitously, and its ecosystem is illustrated by the factors of volatility, uncertainty, complexity and ambiguity. The rapidly changing business landscape calls for incorporating virtual exertion and the adoption of various digital tools. The process of virtual onboarding which has gained prominence at the global level at the onset of the pandemic necessitates encompassing recruits using virtual podiums and remote processes. The current chapter insinuates a holistic model for a suitable virtual onboarding programme, delineating a comprehensive methodology that incorporates a range of onboarding process elements and syndicates business best exercises from several theoretical backgrounds. It intends to offer a robust framework that suitably guides business organisations in developing and implementing effective virtual onboarding programmes. The Virtual Onboarding Model outlined in the present study elucidates the five integral phases, each serving a specific purpose and strategically integrating them from the outcomes derived from various theoretical underpinnings. The outcomes of this chapter provide detailed assistance for businesses operating in the volatility, uncertainty, complexity and ambiguity (VUCA) world to establish comprehensive remote onboarding programmes. It aims to endow human resource (HR) managers with the indispensable intuitions to create and execute virtual onboarding programmes that support successful learning, cultural integration and employee engagement, ultimately benefiting both the recruits and the businesses in contemporary HR practices.

This review explores the challenges and opportunities of implementing telemedicine in rural Tanzania. The study examines how telemedicine can address healthcare gaps in underserved areas and identifies key barriers that hinder its full adoption. It also provides insights into strategies for improving healthcare delivery through telemedicine in rural settings.

A systematic literature review methodology was employed following the PRISMA guidelines. The study collected and analysed relevant academic and grey literature from PubMed, Google Scholar and Scopus databases. The search focused on telemedicine in rural Tanzania, with a thematic analysis used to categorise the challenges and opportunities identified in the literature.

The review found that the major challenges to telemedicine implementation in rural Tanzania include inadequate telecommunications infrastructure, limited digital literacy among healthcare providers, insufficient financial investment and weak regulatory frameworks. Despite these barriers, telemedicine presents significant opportunities to improve healthcare access, particularly by connecting rural patients with specialists, reducing travel times and integrating telemedicine with national health insurance systems. Innovations such as drone-based medical deliveries and SMS-based health interventions also show promise.

This paper comprehensively overviews telemedicine’s potential to transform healthcare delivery in rural Tanzania. It synthesises existing literature, highlights critical gaps in telemedicine adoption and proposes actionable strategies for overcoming these barriers. The study contributes valuable insights for policymakers, healthcare providers and stakeholders interested in leveraging telemedicine to enhance healthcare outcomes in underserved regions.

The IT services industry faces ongoing disruptions due to rapid technological changes and corresponding shifts in customer expectations and competitor actions. Successfully addressing these disruptions entails IT firms to channelize their intellectual capital toward enhancing their ability to adapt. In this study, we propose a mediation model to examine the influence of a project team’s intellectual capital on project performance.

Data were collected from 215 project leaders across different business units within an Indian IT services organization with large operational teams. Mediation analysis was conducted to test the model.

Our findings provide evidence for enhanced team performance through the indirect benefits of adaptive capability accruing from the teams’ intellectual capital. Superior performance is achieved when the intellectual capital steers the adaptive capability of the firm.

Project leaders within IT organizations should constantly enhance their knowledge base and intellectual capital, enabling them to exploit the available knowledge to gain a competitive advantage. This intellectual capital created within the project team can be tapped to foster an adaptive capability, eventually leading to better performance.

Our findings provide unique insights regarding the importance of investing in the intellectual capital of the teams, which results in the enhancement of adaptive capability and thereby the project performance. Data collected from a non-western setting also add to the existing body of knowledge on intellectual capital.

The purpose of this study is to revisit the conventional wisdom about a key contribution [i.e. strengths, weaknesses, opportunities, threats (SWOT) analysis] in the field of strategic management. The societal context and the role of academics, consultants and executives is taken into account in the emergence of SWOT analysis during the 1960–1980 period as a pivotal development within the broader context of the satisfactory, opportunities, faults, threats (SOFT) approach. The authors report on both the content and the approach, so that other scholars seeking to invigorate indigenous theories and/or underreported strategy practices will thrive.

Applying a historiographic approach, the authors introduce an evidence-based methodology for interpreting historical sources. This methodology incorporates source criticism, triangulation and hermeneutical interpretation, drawing upon insights from robust evidence through three iterative stages.

The underreporting of the SOFT approach/SWOT analysis can be attributed to several factors, including strategy tools being integrated into planning frameworks rather than being published as standalone materials; restricted circulation of crucial long-range planning service/theory and practice of planning reports due to copyright limitations; restricted access to the Stanford Research Institute Planning Library in California; and the enduring popularity of SOFT and SWOT variations, driven in part by their memorable acronyms.

In the spirit of a renaissance in strategic planning research, the authors unveil novel theoretical and social connections in the emergence of SWOT analysis by combining evidence from both theory and practice and delving into previously unexplored areas.

Caution is advised for scholars who examine the discrete time frame of 1960–1980 through mere bibliometric techniques. This study underscores the risks associated with gathering incomplete and/or inaccurate data, emphasizing the importance of triangulating evidence beyond scholarly databases. The paradigm shift of strategic management research due to the advent of large language models poses new challenges and the risk of conserving and perpetuating academic urban legends, myths and lies if training data is not adequately curated.

Although the interest in firm media sentiment has been increasing, the impact of news media sentiments on consumers’ perception of firms’ offerings and, subsequently, their sales remain unknown. This study aims to address this research question in this study. Furthermore, the authors consider the role of two boundary conditions, i.e., offerings’ similarity and offerings’ service ratio, that moderate the main relationship.

Using a comprehensive and novel data set of over 900 firms between 2009 and 2019 from multiple sources, this study addresses the research questions. The authors use a fixed effects panel regression model to estimate the model.

A firm’s news media sentiments can influence consumers’ perception of the corporate brand, thereby driving sales growth. This study finds that when a firm’s offerings are not differentiated from its competitors, news media sentiments become more important and so does when a firm offers more services than a product.

To the best of the authors’ knowledge, this study is the first to assess customers’ responses as manifested in the sales growth of a firm’s offerings, using both primary and secondary data and analysis.

The findings provide actionable insights to managers by identifying specific offerings-related attributes – similarity and service ratio – where media sentiments play a critical role in influencing sales growth.

While existing studies in marketing have primarily considered user-generated social media sentiments, this study departs from this literature by investigating earned media sentiments through traditional media outlets such as newspapers and business magazines, which have rarely been studied in marketing.

The utilization of virtual agents, particularly chatbots, within healthcare and medical contexts is witnessing exponential growth owing to their capacity to provide comprehensive support to patients throughout their healthcare journey, by reshaping the healthcare business processes. Such transformation in healthcare service delivery processes is enabled by those digital entities able to offer a preliminary screening and consultation platform, facilitating patients’ interactions with real medical professionals. However, when redesigning processes through the integration of new technologies, particular attention to the reactions of end users cannot be neglected. Thus, the purpose of this paper is investigating how both chatbots' features and patients' individual differences may shape a redesigned/renewed service in the healthcare sector.

Through two experimental studies (Study 1 and Study 2), we examined the impact of chatbot tone of voice (formal vs unformal) on patients’ behavioral responses, particularly their intention to use.

Our investigation shed light on chatbots’ characteristics in terms of perceived warmth, denoting the friendliness and empathy conveyed by the chatbot, and competence, reflecting its effectiveness in addressing user queries or tasks, when used to reshape the service delivery process; Moreover, we also shed light on the moderating role of emotional receptivity seeking, indicating that the emotionality and non-verbal communication between doctor and patient, cannot be overlooked even in innovative digital environments.

Managers and marketers could leverage insights from this study to tailor chatbot interactions, optimizing tone of voice to enhance patient engagement and satisfaction. By focusing on perceived warmth and competence, they can design more effective digital health solutions. Additionally, recognizing the role of emotional receptivity can guide strategies for integrating chatbots in a way that maintains a human touch in patient communications.

Findings importantly underscore the relevance of chatbot in improving patient care, making digital interactions more empathetic and responsive. This relevance extends to business process management by demonstrating how integrating emotionally intelligent chatbots may contribute to better service delivery on the basis of personalized and effective healthcare experiences.

The originality of this paper is about the relevance of considering chatbots’ and final users’ characteristics to strategically plan healthcare services process redesign. Indeed, it examines chatbots' perceived warmth and competence in reshaping service delivery processes. Additionally, it highlights the moderating role of emotional receptivity seeking, emphasizing the importance of emotional communication in digital healthcare environments.

Generative Artificial Intelligence (Gen-AI) has provided new opportunities and challenges in using educational environments for students’ interaction and knowledge acquisition. Based on the expectation–confirmation theory, this paper aims to investigate the effect of different constructs associated with Gen-AI on engagement, satisfaction and word-of-mouth.

We collected data from 508 students in the UK using Qualtrics, a prominent online data collection platform. The conceptual framework was analysed through structural equation modelling.

The findings show that Gen-AI expectation formation and Gen-AI quality help to boost Gen-AI engagement. Further, we found that active engagement positively affects Gen-AI satisfaction and positive word of mouth. The mediating role of Gen-AI expectation confirmation between engagement and the two outcomes, satisfaction and positive word of mouth, was also confirmed. The moderating role of cognitive processing in the relationship between Gen-AI quality and engagement was found.

This paper extends the Expectation-Confirmation Theory on how Gen-AI can enhance students’ engagement and satisfaction. Suggestions for future research are derived to advance beyond the confines of the current study and to capture the development in the use of AI in education.

Mobile edge computing (MEC) services have long been used by private enterprises in Saudi Arabia with considerable success; however, there has been a stark lack of insight into how these services can be used to improve mobile government (M-Government) services for KSA citizens. This study aims to bridge this gap by integrating MEC with an enhanced version of the technology acceptance model (TAM) and examining its effects on user behavior and acceptance.

A closed-ended survey was administered to 1,500 people, and the responses were analyzed using sophisticated advanced statistical techniques to test an expanded TAM, using a quantitative method that uses structural equation modeling to validate the proposed model and hypotheses.

This study reveals that MEC significantly influences users’ intentions about using M-Government services and their tolerance for new technology adoption. Specifically, service cost and social influence are positively linked with end users’ intention to adopt M-Government services.

The novelty and contribution of this paper to existing literature are in highlighting the pivotal role of MEC in transforming public sector service delivery through technology. This study not only supports the adoption of M-Government services to enhance social welfare but also demonstrates and concludes some practical and theoretical ramifications of MEC service adoption.

This study aims to explore how firm-level and country-level sustainability governance can shape corporate sustainability performance.

This study uses an international sample of 2,460 observations from 2010 to 2019 for firms in environmentally-sensitive industries (i.e. energy). Various measures have been used to measure corporate sustainability performance, firm-level and country-level sustainability governance. This study uses a range of statistical models, including fixed effects, random effects, a two-step generalized method of moments, along multiple sensitivity checks to provide accurate empirical evidence.

A specialized sustainability committee and environmental management team enhance corporate sustainability performance. Likewise, a country’s regulatory quality and its environmental performance positively affect the sustainability performance of firms operating in this country. Besides, companies operating in a country with a higher gender gap have a lower corporate sustainability performance.

Energy firms should prioritize establishing sustainability committees or environmental management teams to enhance sustainability practices. Likewise, policymakers should develop robust regulatory frameworks that promote sustainability. Besides, countries should enforce policies promoting gender equality to enhance corporate sustainability.

This study contributes to the literature on environmentally-sensitive industries by examining both firm-level and country-level sustainability governance attributes using three proxies (environmental, social and governance, sustainability strategy and environmental performance) to measure sustainability performance, providing a holistic perspective of how governance attributes influence sustainability outcomes in energy industries.

The aim of the current study is to recommend and compare the estimates of finite element model (FEM), analytical model, and artificial neural networks (ANN) model for capturing the LCC of FCSC members. A database comprising 325 FCSC columns was constructed from previous studies to propose FEM and ANN models while the analytical model was proposed based on a database of 712 samples and encasing mechanics of steel tube and FRP wraps. The concrete damage plastic model was used for concrete along with bilinear and linear elastic models for steel tube and FRP wraps, respectively. Analytical and ANN models effectively considered the lateral encasing mechanism of FCSC columns for accurate predictions.

The study aimed to compare the prediction accuracy of finite element (FEM), analytical, and artificial neural network (ANN) models for the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC). A database of 325 FCSC columns was developed for FEM and ANN models, while the analytical model was based on 712 samples, utilizing encasing mechanics of steel tube and FRP wraps. FEM used a concrete damage plastic model, bilinear steel tube, and linear elastic FRP models. Statistical accuracy was evaluated using MAE, MAPE, R², RMSE, and a 20-index across all models.

Based on the experimental database, the FEM presented the accuracies in the form of statistical parameters MAE = 223.76, MAPE = 285.32, R² = 0.94, RMSE = 210.43 and a20-index = 0.83. The analytical model showed the statistics of MAE = 427.229, MAPE = 283.649, R2 = 0.8149, RMSE = 275.428 and a20-index = 0.73 while ANN models portrayed the predictions with MAE = 195, MAPE = 229.67, R² = 0.981, RMSE = 174 and a20-index = 0.89 for the LCC of FCSC columns.

Although various investigations have already been performed on the prediction of the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC) using small and noisy data, none of them compared the accuracy of prediction of different modeling techniques based on a refined large database.