Search results

Recent studies have observed rise in consumer’s ethical concerns about the online retailers while making a purchase decision. The impetus for businesses to use corporate social responsibility (CSR) is evident, but the effects of CSR motives on corresponding processes underlying cause-related marketing (CRM) patronage intention have not been thoroughly examined. This study, anchored on attribution theory, established a research model that better explains the influence of CSR motives on patronage intentions toward CRM-oriented online retailers. Additionally, this study aims to examine the moderating role of spirituality (SPT) on CSR motives and CRM patronage intention (CPI).

Primary data has been collected from 722 respondents and analyzed by using deep neural-network architecture by using the innovative PLS-SEM-ANN method to predict/rank the factors impacting CPI.

The results revealed the normalized importance of the predictors of CPI and found that value-driven motive was the strongest predictor, followed by strategic motive, SPT, age and stakeholder-driven motive. In contrast, egoistic motive, education and income were found insignificant.

The pandemic has transformed the way consumers shop and fortified the online economy, thereby resulting in a paradigm shift toward usage of e-commerce platforms. The results offer valuable insights to online retailers and practitioners for predicting patronage intentions by CSR motives and, thus, effectively engage CRM consumers by designing promotions in a way that would deeply resonate with them. This study assessed and predicted the factors influencing the CPI s, thereby guiding the online retailers to design CSR strategies and manage crucial CRM decisions.

Managing mega infrastructure projects (MIPs) is more complex because of time, size, social, environmental and financial implications. This study aims to address the management approaches, complexity and risk factors involved in MIPs. The study focuses on project success criteria and their individual effects on the success of MIPs.

To address the challenges and identify the most influencing factor for the success of MIPs, the study deployed a cross-sectional survey approach. Six hundred eighty-two usable samples were collected from the respondents to understand the impact of predetermined factors on the success of MIPs. The structural equation model and artificial neural network approach were used to derive the importance of factors affecting the success of MIPs.

The study's outcome confirms that all three influencing factors: feasibility studies, community engagements and contract selection, have a significant positive impact on the success of MIPs. Community engagement amongst all three has the most influential predictor for the success of MIPs.

The developed model will enable practitioners and policymakers from Indian construction companies and other emerging nations to concentrate on recognized risk reduction variables to enhance project success criteria and project management success, especially for MIPs.

This study examines the influence of consumer skepticism on cause-related marketing (CRM) campaign participation intentions of Generation Z consumers from emerging market perspective. This study was undertaken due to the paucity of relevant literature in the public domain to directly investigate whether and how consumers' skepticism affects CRM participation intentions, specifically in the luxury retailing context.

A survey was conducted among 552 Generation Z consumers and path analysis was used to examine the direct and indirect effects of skepticism. The mediation and moderation analysis was used to explore and test the authors' hypotheses via partial least squares structural equation modeling (PLS SEM).

The authors' findings provide empirical evidence that corporate social responsibility, religiosity and cause involvement positively affects consumer participation intentions, and this link is also established indirectly through skepticism toward the CRM campaign. These findings provide novel theoretical contributions by establishing skepticism's complex role in determining the CRM participation intention in the Generation Z consumers' context. This study further demonstrates the moderating effect of gender and luxury store format on consumer skepticism and CRM participation intentions.

The Generation z group will represent a quarter of the Asia–Pacific region's population by 2025. However, little is known about Generation z consumers' CRM participation intentions. This research would help practitioners, including luxury retailers, CRM managers and advertising professionals, to effectively design CRM campaigns. The paper contributes by highlighting the theoretical implications and managerial implications based on the current findings in the emerging market context.

The overarching goal of the study was to formulate an integrated research model to empirically demonstrate the complex interplay between heuristics, project characteristics, information system usage quality, empathy, and mindfulness in predicting users'/donors' donation behaviour and well-being in the context of donation-based crowdfunding (DBC) mobile apps.

The data were collected from 786 respondents and analysed using the multi-stage SEM-ANN-NCA (Structural equation modelling-artificial neural network-necessary condition analysis) method.

Increased perceived aesthetics, narrative structure, self-referencing, project popularity, project content quality, and initiator reputation would foster empathy. Empathy and mindfulness lead to donation behaviour, and, ultimately emotional well-being.

This study offers a clear framework by ranking the key contextual predictors and assessing the model’s necessity logic to facilitate crowdfunders' donation behaviour and well-being on DBC platforms. This research provides practical insights for bank marketers and further aids financial service providers in formulating an optimal DBC mobile app strategy.

Businesses embark on cause-related marketing (CRM) initiatives as a marketing strategy to fortify consumers' behavioural intentions. Prior research indicates that human values could be tapped to understand the consumers' responses to perceived organizational motives behind undertaking social cause initiatives. This research employs Schwartz's theory of human values to examine consumers' patronage intentions towards CRM-linked fashion products. Moreover, fashion leaders play a crucial role in the diffusion of the latest fashion and fashion trends. This research investigates by integrating human values and fashion leadership, offering insights into CRM-linked fashion consumption motives.

The overarching goal was to investigate the complex interplay between human values and female fashion leadership to predict CRM patronage intention (CPI). Hence, a large-scale research study on 2,050 samples was undertaken by adopting threefold partial least squares–multigroup analysis–artificial neural network (PLS-MGA-ANN) to establish and empirically test a comprehensive model.

This study is unique as it establishes and validates the relative or normalized importance placed on human values by fashion leaders, thereby predicting CPIs. The results revealed that women with high-fashion leadership and specific value types (benevolence, universalism, self-direction) are more likely to patronize CRM-linked fashion retailers. In addition, the findings validated that women with low-fashion leadership and specific value types (tradition, security, conformity) are more likely to patronize CRM-linked fashion stores.

The findings provide a valuable rationale to non-profit marketers, fashion marketing experts and practitioners to design customer value-based profiling and manage crucial CRM decisions.

Every nation celebrates their Independence Day as a grand celebration by the whole country together. The purpose of this study is to identify the people’s emotions and sentiments about their country at the time of Independence Day celebration through the analysis of social media messages.

To collect data from Twitter now which is known as “X”, this study used python computer programming language. Total 119,151 tweets were collected, and sentiment analysis was done based on the polarity score and Latent Dirichlet Allocation to understand the sentiments of public. The topic modelling was also done in this study, where topics and categories were created based on the words found through the Latent Dirichlet Allocation.

The result of this study revealed that people have the respect towards their country and are always ready to come together for the nation. They also feel proud to be a citizen of an independent nation. Therefore, they celebrate their country’s Independent Day every year with joy. Every nation needs a dynamic leader to lead the nation, whom people can trust easily and follow blindly.

The result of this study can be generalised as everyone supports and respects their nation and the national flag. This study is practically useful to other researchers to conduct similar study of sentiment analysis and topic modelling on other counties’ patriotism or other campaigns at national or global level.

As a result, this study contributes to understand people’s respect towards the nation. This study also showed people’s positive response at their leader’s request. There are many studies done on topic modelling and content analysis, but this is the first and original work attempted on the special subject on national patriotism campaign to the best of the authors’ knowledge.

Global environmental and social threats challenge humans’ well-being and the survival of posterity. Industry 4.0 (I4.0) transformed the industry sector, enabling process automation and scalability, increasing manufacturers’ productivity, efficiency, and profitability, and supporting manufacturing innovation and firms’ competitive advantage. Despite this, the I4.0 paradigm, as currently conceived, does not fit for purpose in the context of the climate crisis and planetary emergency, nor does it address deep social tensions.

Therefore, a new revolution focusing on human and environmental needs is strongly required to address society’s economic and social problems. Combining digitalization and social purposes may address local, national, and international issues by involving a human-centered perspective in traditional business-oriented entrepreneurship.

Depending on the above, this chapter provides an in-depth understanding of Digital Entrepreneurship (DE), Digital Social Entrepreneurship (DSE), and how entrepreneurs may employ digital technologies to reach business and social aims.

Owing to the limitations of traditional infectious disease dynamic systems in accurately encapsulating the nuances of China’s dynamic epidemic prevention policies and considering the varying sensitivity of local governments to the unfolding of public health emergencies (PHEs), this paper introduces a novel infectious disease dynamic system.

This system, rooted in the distinct characteristics of infectious diseases and nuanced prevention and control measures, leverages a learning model for enhanced precision. It intricately incorporates factors such as the infectivity in sealed and controlled areas and the role of asymptomatic patients, thereby refining the dynamics of isolation, sealing, control and the transition from asymptomatic to confirmed cases. Employing the Markov Chain Monte Carlo (MCMC) parameter estimation approach significantly augments the accuracy in pinpointing the valid parameters of disease spread. Empirical analysis was meticulously carried out, using data from the Shanghai epidemic from 1 Mar 2022 to 1 Jul 2022.

This analysis not only illuminates the profound impact of control efforts on the trajectory of the epidemic but also underscores the pivotal role of social distancing in curbing the rapid transmission of infectious diseases. Furthermore, it reveals that an accelerated detection rate during the swift spread and peak of the epidemic paradoxically leads to a surge in confirmed cases and a consequent strain on medical resources, thereby impeding the pace of medical intervention.

A stage-wise dissection of the Shanghai epidemic and comparative analyses against the evolution profiles in ASEAN countries elucidates the five stages of PHE risk evolution in alignment with the crisis lifecycle theory. These stages encompass hidden transmission, multi-point dissemination, multi-chain parallelism, rapid spread, fluctuation rebound and multi-community spread, each presenting unique challenges and dynamics in the control and management of the epidemic.

The study aims to examine the conceptualization of online abuse (OA) and identifies theories, countries of research, top-cited articles, methodologies, antecedents, mediators, outcomes and moderators of OA and future research opportunities. Two research questions are addressed. How have the past studies on OA progressed regarding theories, context, characteristics and methodology? What future research opportunities can be done in this area?

This study systematically reviews, synthesizes and integrates OA literature using the well-recommended preferred reporting items for systematic reviews and meta-analyses (PRISMA) rules. The literature on OA was synthesized based on the Theory–Context–Characteristics–Methodologies (TCCM) framework given by Paul and Rosado-Serrano.

Through an examination of TCCM used in OA research, the review presents an all-inclusive and up-to-date overview of the research in this arena and sets a future research agenda to spur scholarly research. This systematic literature review has analyzed top-quality sample papers, published in the past decade. As a result, it contributes to a better understanding of this relationship by analyzing the different types of use and the value added to the shopping experience.

This study provides groundwork for researchers and promotes a deeper understanding of OA.