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E-commerce companies use different types of dark patterns to manipulate choices and earn higher revenues. This study aims to evaluate and prioritize dark patterns used by e-commerce companies to determine which dark patterns are the most profitable and risky.

The analytic hierarchy process (AHP) prioritizes the observed categories of dark patterns based on the literature. Several corporate and academic specialists were consulted to create a comparison matrix to assess the elements of the detected dark pattern types.

Economic indicators are the most significant aspect of every business. Consequently, many companies use manipulative methods such as dark patterns to boost their revenue. The study revealed that the revenue generated by the types of dark patterns varies greatly. It was found that exigency, social proof, forced action and sneaking generate the highest revenues, whereas obstruction and misdirection create only marginal revenues for an e-commerce company.

The limitation of the AHP study is that the rating scale used in the analysis is conceptual. Consequentially, pairwise comparisons may induce bias in the results.

This paper suggests methodical and operational techniques to choose the priority of dark patterns to drive profits with minimum tradeoffs. The dark pattern ranking technique might be carried out by companies once a year to understand the implications of any new dark patterns used.

The advantages of understanding the trade-offs of implementing dark patterns are massive. E-commerce companies can optimize their spent time and resources by implementing the most beneficial dark patterns and avoiding the ones that drive marginal profits and annoy consumers.

This study attempts to understand the research clusters and thematic evolution of the topic generational diversity at workplace, over the period of 2001–2009 and 2010–2018. Furthermore, it attempts to identify the key shifts (and convergence) that have taken place in the value system across generational cohorts.

In this context, the first stage of the study involved an in-depth systematic analysis of extant literature on multigenerational workforce between 2001–2009 and 2010–2018 by applying bibliometric analysis. Following an explanatory mix-method approach, the second stage of the study comprised of 32 interviews conducted across generations, exploring the role of ethics at the workplace.

It was revealed that during the period 2001–2009, communication and identification of generational characteristics emerged as the major themes. The 2010–2018 period unraveled four themes of research – retaining and engaging millennials through leadership, generational differences in work values, impact of generational differences on organizational-level variables and generational diversity in education and nursing. The outcome from the second stage showed that work values differ across generations with an emphasis on intrinsic work values, and work values have rather deteriorated, with baby boomers possessing stronger work ethics in comparison to the millennials. Finally, an integrated model for multigenerational workforce has been proposed.

This paper provides significant inputs to the expanding research in the area of work values, as it delves into the principal mechanisms leading to differences in work values among generations.

Bibliometric analysis, which is a quantitative approach to understanding the intellectual structure of a research topic, has been applied to generational diversity at the workplace. This constitutes a novel attempt that can be bracketed as a pertinent contribution to the field.

E-commerce companies employ deceptive techniques known as dark patterns (DPs) to manipulate customer decisions on online platforms. DPs are known to annoy and generate negative emotions in the consumers, which might be detrimental to a brand. Consequently, this study examines the influence of DPs on brand experience (BE) and consumer-based brand equity (CBBE) from the lens of stimulus-organism-response theory in an e-commerce organization.

Survey data was obtained from 419 respondents who frequently make transactions on e-commerce platforms. Subsequently, the authors used partial least squares-based structural equation modeling (PLS-SEM) via the R programming language to analyze the relationships.

It was found that DPs negatively impact the BE and CBBE of an e-commerce firm. Furthermore, BE positively impacts CBBE as well as mediates the relationship between DPs and CBBE. Additionally, no significant moderating influence of shopping frequency was discovered for the relation between DPs and BE.

The study investigates the connection between DPs, BE and CBBE in a general e-commerce context. Results may differ across specialized e-commerce sectors.

The study’s insights could motivate e-commerce managers to reevaluate their use of DPs. The study could help online platforms reorganize their business strategies and adopt a customer-centric approach, which might create a long-term positive relationship with customers. The results might spark discussions over redressing customer relations after previous exploitation via DPs.

To the best of the authors’ knowledge, this study is the first to empirically assess the impact of DPs on crucial brand parameters like BE and CBBE.

Today, big data (BD) is considered as a crucial investment for firms to stay competitive. However, the human resource (HR) function within small- and medium-sized enterprises (SMEs) has been slow to adopt this innovation. Drawing on the organizational learning theory (OLT), this study aims to propose that BD can improve HR functions, especially of SMEs, thereby yielding them a competitive edge.

This study analyzed unstructured data from 41 journal papers, based on which, a conceptual framework was developed. Further, this framework was validated with responses collected from 148 SMEs in India.

Bibliometric analysis and results of partial least squares techniques revealed that better BD quality is needed to improve HR practices, human resource service quality (HRSQ) and innovation competency of SMEs.

This paper contributes to the extant literature by considering strategic management theories such as resource-based view and OLT to evaluate BDA’s effect on organizational functional practices such as HR and HRSQ.

In Indian SMEs, BD quality has a substantial effect on BD HR practices and HRSQ. However, these factors influence can constructively impact SMEs, if SMEs are open to organizational change, whereby they need to develop technical skills and competencies of the HR professionals.

Though BD research works have shown exponential growth in recent times, scholarly empirical research investigating BD’s impact upon human resource management (HRM) is scarce. The present study appraises extant literature on BD in HRM.

The sudden onset of COVID-19 has brought about a watershed moment in the current research across all disciplines. As it has impacted almost all aspects of human existence, academicians are aggressively trying to understand the phenomenon from multidisciplinary perspectives. In this regard, the present study attempts to provide an in-depth understanding of academia's response pattern in the field of social sciences using a grounded theory literature review and bibliometric analysis.

The present study analyzed 395 research articles on the pandemic phenomenon, yielding five main themes and 11 sub-themes.

The emergent research themes are global impact on public health, the influence of COVID-19 on workplace functioning, global governance in COVID-19, research ethics in scholarly works and the influence of COVID-19 on demography.

Drawing from these themes, the authors provide propositions, policy implications and future research directions.

In this study, we investigated the positive and negative effects of stress that is driven by responsible artificial intelligence (RAI) principles on employee job outcomes by adapting the challenge–hindrance stressors model.

The study design involved empirically validating the proposed model on 299 respondents who use AI for work-related tasks.

The results revealed several RAI-driven challenge and hindrance stressors related to employees’ positive and negative psychological responses and task performance in a digital workplace. Practitioners could use the RAI characteristics to improve employees’ RAI-driven task performance.

This study contributes to the ongoing discussion on technostress and awareness in the context of RAI in the AI literature. By extending the C-HS model to the RAI context, it complements the context-specific technostress literature by conceptualizing different characteristics of RAI as RAI-driven stressors.

Adoption and use of technologies like RAI are not automatically translated into expected job outcomes. Instead, practitioners and academicians also need to know whether the RAI characteristics actually help employees show positive or negative behavior. Furthermore, relying on the challenge–hindrance stressor (C-HS) model, we try to reveal the beneficial and detrimental effects of different RAI characteristics on employees’ job outcomes.

Due to the extreme competition in the e-commerce landscape, online companies resort to driving extra profits by manipulating user decisions via manipulative user interfaces and tricks, known as dark patterns. Although many online users are aware of such manipulative tactics, they struggle to combat dark patterns due to certain challenges. Therefore, this study identifies and ranks the most and least significant user challenges in resisting dark patterns in e-commerce.

Initially, the authors identified 11 user challenges against dark patterns in the existing literature. After collecting expert opinions about the challenges from 17 industry professionals, the authors used grey influence analysis (GINA) to identify the most significant challenges.

The study reveals that lack of user awareness, partial control over cognitive biases and user preference for short-term benefits are the most influential challenges. Conversely, the normalization of aggressive marketing, lack of collective user action and legal challenges were found to have minimal influence.

The study’s findings apply to the generic e-commerce industry and may differ in specialized sectors.

The study’s findings could encourage specialized consumer education regarding dark patterns to assist users in minimizing cognitive biases. Based on the insights, e-commerce companies might differentiate themselves by embracing ethical design methods and supporting consumer trust and transparency above manipulative techniques.

The study pioneered the use of GINA to model user challenges in combating dark patterns, providing fresh insights into how these challenges interact and influence user behavior. It is one of the first to show the cascading effects of user challenges in a manipulative e-commerce environment.

The purpose of this study is to develop a model that explains a user’s attitude toward M-payments in India, based on the motivational model and awareness about demonetization policy. The study also investigates the validity and differential predictive power of four different M-payment usage models, by considering the effect of improved awareness regarding demonetization policy on the core construct of the motivational model.

The model was tested with survey data from 362 M-payment users using partial least squares. Respondents were M-payment users with significant usage experience.

This study empirically determined that the motivation model and awareness about demonetization policy in M-payment usage after demonetization are connected. As hypothesized, the study found: a positive relationship between extrinsic motivation and intention to use M-payment, positive relationship between awareness about demonetization policy and behavioral intention to use M-payment, positive relationship between intrinsic motivation and extrinsic motivation, positive relationship between awareness about demonetization policy and extrinsic motivation and positive moderation effect of awareness about demonetization policy on the extrinsic motivation-behavioral intention relationships. The hypothesis that awareness about demonetization policy would have a moderating effect on intrinsic motivation–behavioral intention relationship was not supported.

From a practitioner’s perspective, this study underscores the importance of raising sufficient awareness about the demonetization policy as a determinant of users’ willingness to use M-payment services. Mandatory regulations by the government and motivation toward M-payment use can be a good starting point for a cashless economy.

This study makes a needed contribution to the literature by validating the integrated motivation model, emphasizing the importance of raising awareness about the demonetization policy among M-payment users. The model may provide a useful foundation for future research in this area.

E-commerce companies use dark patterns to manipulate customer decisions to survive in the crowded online market and make profit. Although some online customers are aware of the dark patterns, they cannot overcome such manipulations. Therefore, the purpose of this study is to identify and model the barriers to overcoming dark patterns using total interpretive structural modeling (TISM).

Barriers to overcoming dark patterns were identified from the extant literature and were validated by a panel of 18 domain experts. In the modeling phase, TISM technique was used to identify the relationships between the barriers and assign priority to the barriers. Finally, the barriers were plotted and classified into three categories.

User unawareness, trust in brands and normalization of aggressive marketing were found to be the highest priority barriers. Whereas, designer bias, user fatigue, short-term user benefits and design complexity were identified as the most challenging barriers because they have least dependence over the other barriers.

Because TISM results are based on the opinion of domain experts, other statistical techniques could be applied for validation.

This study would educate online customers, while assisting online user communities and regulatory bodies to devise strategies to overcome dark patterns. Additionally, business managers could use the study’s findings to encourage designers to embrace ethical design methods as a competitive advantage.

This study contributes to the research as it is first of its kind to examine the link between dark pattern barriers.

E-commerce companies often manipulate customer decisions through dark patterns to meet their interests. Therefore, this study aims to identify, model and rank the enablers behind dark patterns usage in e-commerce companies.

Dark pattern enablers were identified from existing literature and validated by industry experts. Total interpretive structural modeling (TISM) was used to model the enablers. In addition, “matriced impacts croisés multiplication appliquée á un classement” (MICMAC) analysis categorized and ranked the enablers into four groups.

Partial human command over cognitive biases, fighting market competition and partial human command over emotional triggers were ranked as the most influential enablers of dark patterns in e-commerce companies. At the same time, meeting long-term economic goals was identified as the most challenging enabler of dark patterns, which has the lowest dependency and impact over the other enablers.

TISM results are reliant on the opinion of industry experts. Therefore, alternative statistical approaches could be used for validation.

The insights of this study could be used by business managers to eliminate dark patterns from their platforms and meet the motivations of the enablers of dark patterns with alternate strategies. Furthermore, this research would aid legal agencies and online communities in developing methods to combat dark patterns.

Although a few studies have developed taxonomies and classified dark patterns, to the best of the authors’ knowledge, no study has identified the enablers behind the use of dark patterns by e-commerce organizations. The study further models the enablers and explains the mutual relationships.