Search results

This study aims to examine the role of different forms of corporate social advocacy (CSA) in shaping individuals’ attitudinal and behavioral intentions towards companies taking their public stand on controversial socio-political issues. With an online experiment as the research method, this study tests whether depicting nonpolitical or political behaviors in CSA messages increases individuals’ positive behavioral intentions.

This study uses a single factor between subject online experiment. A total of 135 US young adults were recruited through a Qualtrics online panel. Three social media mockups were created to manipulate three levels of actions in CSA messages (no action, nonpolitical action and political action). Participants viewed one of those social media posts depicting presented actions to counter anti-LGBTQ + legislation in the USA and answered questions about values-driven motives behind CSA, brand preference and positive word-of-mouth (WOM) intention.

Participants displayed higher levels of brand preference when they viewed CSA messages depicting the company’s political action intended to repel anti-LGBTQ + legislation. Participants showed more positive WOM intentions towards the company when they perceived its political actions as more values-driven.

The findings of this study offer practical insights to companies when designing CSA messages and strategies. The results of this study indicate that the presence of political actions in CSA communication increases individuals’ positive behaviors towards companies. The results also suggest that depicting altruistic motives behind CSA leads individuals to talk about companies more in positive ways.

This study is one of the early studies investigating the impact of various forms of CSA on individuals’ attitudinal and behavioral intentions to companies practicing CSA. This study provides practical implications on how to effectively appeal individuals’ favorable attitudes and behaviors towards CSA. In particular, this research presents the importance of action aspects in individuals’ attitudes toward corporations’ CSA messages.

The surge of artificial intelligence (AI) applications and subsequent adoption by consumers and marketers has ignited substantial research exploring the benefits and opportunities of AI. Despite this, little attention has been given to its unintended negative consequences. In this paper, the authors examine both the practitioner and academic sides of ethical AI. In doing so, the authors conduct an extensive review of the AI literature to identify potential issues pertaining to three areas: individual consumers, societal and legal. The authors identify gaps and offer questions to drive future research.

The authors review recent academic literature on AI in marketing journals, and top ethical principles from three top technology developers (Google, IBM and Meta) in conjunction with media reports of negative AI incents. They also identify gaps and opportunities for future research based on this review.

The bibliographic review reveals a small number of academic papers in marketing that focus on ethical considerations for AI adoption. The authors highlight concerns for academic researchers, marketing practitioners and AI developers across three main areas and highlight important issues relating to interactive marketing.

This paper highlights the under-researched negative outcomes of AI adoption. Through an extensive literature review, coupled with current responsible AI principles adopted by major technology companies, this research provides a framework for examining the dark side of AI.

This paper aims to examine and compare the associations between financial capability and financial anxiety (FA) before and during the coronavirus disease 2019 (COVID-19) pandemic. Specifically, financial capability is measured by three indicators: financial knowledge, financial behavior and financial confidence. This study also examines and compares the association among different income groups before and during the pandemic.

Data are from 2018 to 2021 National Financial Capability Study (NFCS). Structural equation modeling (SEM) is employed to examine the direct and indirect associations between financial capability factors and FA. Furthermore, this paper also conducts multi-group SEM for three income groups to examine the heterogeneous effects of household income.

Both before and during the pandemic, financial knowledge is directly positively and financial behavior is directly negatively associated with FA. In addition, both financial knowledge and financial behavior are positively associated with financial confidence, which in turn is negatively associated with FA. However, when taking the indirect effects into consideration, the total effects of financial capability factors on FA are all negative. Furthermore, the pandemic has intensified the negative association between financial behavior and FA rather than financial knowledge or financial confidence. Multi-group SEM shows that the positive direct effects of financial knowledge are only significant in the low-income group, while the negative direct effects of financial behavior are only significant in the low- and middle-income groups before the pandemic. However, direct effects of financial knowledge and financial behavior are significant in all income groups during the pandemic.

First, this study specifies a construct, financial confidence, to proxy perceived financial capability. Second, it examines the mediating role of financial confidence in the association between the other two financial capability factors (financial knowledge and financial behaviors) and FA. Third, it also compares the associations between financial capability factors and FA before and during the COVID-19 pandemic.

This study aims to investigate the contribution of blockchain technology to supply chain risk management and its impact on performance among Indian manufacturing companies.

Drawing on a resource-based view, dynamic capability and system of systems theory, this study examines the direct relationships between blockchain, supply chain risk management and supply chain performance. The authors validate the mediating effects of three supply chain risk management components, namely supply risk management, demand risk management and cyber security management, on financial transaction reliability and information reliability. Data were collected from 204 Indian manufacturing companies that have adopted blockchain technology.

The results demonstrate that companies adopting blockchain technology have experienced positive outcomes in managing supply chain-related risks, financial transaction reliability and information reliability. These findings provide valuable guidance to managers, highlighting blockchain as a competitive advantage for supply chain management.

To the best of the authors’ knowledge, no previous research on blockchain-based risk management capabilities has been conducted.

The authors study the effect of an exogenous shock in the form of Coronavirus lockdowns on individual default and on default contagion within the microfinance (MF) sector in India. The authors rely on proprietary data obtained from an MF institution for the period from Nov 2019 to Dec 2020. The authors show that default increased to 95.29% in the month of April 2020, when Covid lockdowns were fully in place. However, borrowers bounced back thereafter, either making full or partial payments, so that defaults had fallen to 5.92% by December 2020. Static features of the group lending model like peer monitoring and joint liability help explain 90% of the monthly deficit during Covid lockdowns among uneducated borrowers. Dynamic features such as contingent renewal help explain why defaults were cured quickly through timely repayments. Finally, there is an absence of default contagion at the district level. Indeed, lagged own default explains 96.6% of variation in individual default, rather than contagion through group, village or district-level defaults. The authors conclude that the MF sector is resilient to exogenous shocks like the pandemic.

The authors use time series panel regressions, as well as cross-sectional regressions.

The authors find that borrower defaults increased significantly to 95.29% during the month of April 2020, when Covid lockdowns were fully in place. However, borrowers bounced back almost immediately, either making full or partial payments, such that defaults had fallen to 5.92% by December 2020. The group lending model does remarkably well in explaining defaults even during Covid lockdowns. Among the majority (92%) of borrowers who are residents of rural districts, the group lending model appears to blunt the impact of the exogenous shock on rates of default. Indeed, panel regressions demonstrate that the group lending model helps explain 90% of the monthly deficit among uneducated borrowers. Logistic regressions indicate that the group lending model is less persuasive among relatively affluent borrowers residing in semi-urban or urban areas who have some formal schooling. Contingent renewal is shown to be an effective disciplining mechanism when a group does default due to the Covid lockdowns. The authors find that groups who defaulted in April 2020 but repaid the outstanding balance within the next two months were more likely to receive subsequent loans from the lender. On the other hand, groups who defaulted in April 2020 and did not repay the outstanding balance until December 2020 did not receive follow-on financing. Finally, the authors find that lagged individual default is the primary source of individual default, rather than contagion through group, village or district-level defaults.

The limitation of the study is that it is confined to a single MF institution in India.

The authors conclude that the social capital that is the foundation of the group lending model succeeds in limiting both the risk and contagion of default from an exogenous shock, such as the Covid pandemic.

To the best of the authors’ knowledge, the authors are the first to examine defaults in the Indian MF sector during the Covid lockdowns in April 2020.

This paper summarizes and synthesizes existing research while critically assessing findings for future studies to advance the scholarship of maritime logistics and digital transformation with big data.

A bibliometric analysis was conducted on 159 journal articles from the Scopus database with search keywords “maritime*” and “big data.” This analysis helps identify research gaps by identifying themes via keyword co-occurrence, co-citation and bibliographic coupling analysis. The Theory-Context-Characteristics-Methodology (TCCM) framework was applied to understand the findings of bibliometric analysis and provide a research agenda.

The analyses identified emerging themes of the scholarship of maritime logistics and digital transformation with big data and their relationships to identify research clusters. Future research directions were provided by examining existing research's theory, context, characteristics and method.

This research is grounded in bibliometric analysis and the TCCM framework to understand the scholarly evolution, giving managers and academics retrospective and prospective insights.

The purpose of this study is to investigate how proximity to metropolitan areas and local creative talent impact a company’s access to venture capital (VC). We analyze the interplay between these factors and test our hypotheses using USA county data.

This empirical study uses multivariate regression analyses to analyze VC investment distribution across the USA at the county level between the years 1990–2011.

Our findings suggest that an increase in the local creative workforce correlates with higher levels of VC funding, regardless of metro location, but has a more significant impact in metro areas, indicating the complementary nature of these factors. Furthermore, the tech industry benefits more from the local creative workforce and is less sensitive to geographic location. Our results suggest that non-metro locations with a rich local creative culture can be as effective in attracting VC as metro locations with a mediocre local creative culture. This study contributes to our understanding of the optimal geographic location for companies seeking VC.

One of the limitations of our research is the research timeline. Since “creative class” was not measured by the U.S. Department of Agriculture (USDA) after 2011, we cannot analyze the recent effects of creative class on VC. However, given the fact that technology-related industries increasingly dominated the VC industry in recent years, our results on tech-related industries can shed light on the future expectations of the creative class in the VC industry moving forward.

Some companies might find it advantageous to locate outside metro areas where the creative workforce is more abundant and accessible. Our results support this trend by demonstrating that companies must consider the tradeoff between these two factors and recognize that locating in metro areas may not always be the optimal choice for every company. A tradeoff may exist between location and the cost of accessing creative talent.

Our results suggest that non-metro locations with a rich local creative culture can be as effective in attracting VC as metro locations with a mediocre local creative culture.

The existing literature emphasizes the importance of studying various factors that can help distribute VC and entrepreneurial activities across the country instead of just being concentrated in specific areas like metro regions. Although previous studies have examined broader institutional and country-level factors, local creative culture has not been considered in the context of its impact on the geographical distribution of VC. Our research highlights creative culture as a new local factor that affects VC distribution among USA counties.

The purpose of this paper is to estimate the implications of illicit market use for the value of Bitcoin in an event studies framework.

This study uses a data set of 58 state-level marijuana decriminalisation and legalisation bills and referenda in the USA in 2010–2022.

Decriminalisation is associated with a strong and consistent positive Bitcoin price response around the event, recreational legalisation induces a more ambiguous reaction and medical legalisation is found to have a negative albeit small impact on Bitcoin value. This suggests decriminalisation enhances shadow economy use value of Bitcoin, whereas recreational and medical legalisation are not consistently reducing illicit drug cryptomarket activity. The effects are robust to various estimation windows, in subsamples, and also when outliers, heavy tails, conditional heteroskedasticity and state size are accounted for.

New to the literature, the choice of US marijuana bills, specifically as sample events, is based on both theoretical and empirical grounds.

Medicine distribution logistics pattern in pharmaceutical supply chains is a hot topic, which aims to predict applicable and efficient medicine distribution patterns so that the medicine can be distributed effectively. This research aims to propose a new method, named density-distance method, that works based on Kth proximity using patient features (including age, gender, education, inherent diseases, systemic diseases and disorders); geographical features (city, state, population, density, land area) and supply chain features (destination and transportation system).

The proposed method of this research consists of two main phases where in the first phase, quantitative data analytics will be carried out to find out the significant factors and their impacts on medicine distribution. Then, in the next phase, a new Kth-proximity density-distance-based method is proposed to determine the best locations for the wholesalers while designing a supply chain.

The findings show that the proposed method can effectively design a supply chain network using realistic features. In addition, it is found that while the distance-density aggregate index is applied, the wholesalers' locations will be different compared to classic supply chain designs. The results show that age, public hygiene level and density are the most influential during designing new supply chains.

The outcomes of this research can be used in the medicine supply chains to predict appropriate medicine distribution logistics patterns.

In this research, the machine learning method based on the nearest neighbor has been used for the first time in the design of the supply chain network.

Research has examined how new ventures strengthen local economic outcomes; however, limited research examines health-oriented ventures and their impact on social outcomes, including health outcomes. Increased VC investment in healthcare service start-ups signals more activity toward this end, and the need for further academic inquiry. We examine the relationship between these start-ups and county-level health outcomes, health factors, and hospital utilization.

Data on start-ups funded via institutional venture capital from PitchBook were merged with US county-level outcomes from the County Health Rankings and Area Health Resources Files for 2010 to 2019. We investigated how the number of VC-funded healthcare service start-ups, as well as a subset defined as innovative, were associated with county-level health measures. We used panel models with two-way fixed effects and Propensity Score Matched (PSM), controlling for demographics and socioeconomic factors.

Each additional VC-funded healthcare service start-up was related to a significant 0.01 percentage point decrease in diabetes prevalence (p < 0.01), a decrease of 1.54 HIV cases per 100,000 population (p < 0.1), a 0.02 percentage point decrease in obesity rates (p < 0.01), and a 0.03 percentage point decrease in binge drinking (p < 0.01). VC-funded healthcare service start-ups were not related to hospital utilization.

This work expands our understanding of how industry-specific start-ups, in this case healthcare start-ups, relate to positive social outcomes. The results underscore the importance of evidence-based evaluation, the need for expanded outcome measures for VC investment, and the possibilities for integration of healthcare services and entrepreneurship ecosystems.