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This paper aims to estimate the determinants of the intensity of use of financial inclusion by households in Ghana.

Due to the reality of a household using one or more financial products or services, this study uses the generalised Poisson model applied to GLSS6 and GLSS7 data collected in 2012/2013 and 2016/2017 respectively, to estimate the determinants of the intensity of use of financial inclusion. To deepen the analysis, a multinomial probit model is also applied.

Results show that infrastructural variables such as roads, public transport and banks stimulate the intensity of financial inclusion. In addition, agricultural development characteristics such as markets and cooperatives are essential for the intensity of inclusion.

There is a need to incorporate how many services or depth of services that people use as part of the conceptualisation of financial inclusion, as this can provide more policy-relevant evidence to enhance priority setting in financial inclusion policies. Also, micro-level financial inclusion studies in agrarian economies should consider exploring agricultural development and infrastructure variables in the modelling framework. As lead to further studies, count models of financial inclusion should consider exploring cross-country analysis, the use of panel data, or other methodological approaches to provide more robust evidence.

Previous studies have not modelled financial inclusion based on a count model as a means of measuring intensity though conceptualisations highlight the fact that people use varied financial products or services. Following from this angle, to the best of the authors’ knowledge, this study provides the first attempt at analysing the underlying determinants of the number of financial products or services used by households.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

As public health professionals strive to promote vaccines for inoculation efforts, fervent anti-vaccination movements are marshaling against it. This study is motived by a need to better understand the online discussion around vaccination. The authors identified the sentiments, emotions and topics of pro- and anti-vaxxers’ tweets, investigated their change since the pandemic started and further examined the associations between these content features and audiences’ engagement.

Utilizing a snowball sampling method, data were collected from the Twitter accounts of 100 pro-vaxxers (266,680 tweets) and 100 anti-vaxxers (248,425 tweets). The authors are adopting a zero-shot machine learning algorithm with a pre-trained transformer-based model for sentiment analysis and structural topic modeling to extract the topics. And the authors use the hurdle negative binomial model to test the relationships among sentiment/emotion, topics and engagement.

In general, pro-vaxxers used more positive tones and more emotions of joy in their tweets, while anti-vaxxers utilized more negative terms. The cues of sadness predominantly encourage retweets across the pro- and anti-vaccine corpus, while tweets amplifying the emotion of surprise are more attention-grabbing and getting more likes. Topic modeling of tweets yields the top 15 topics for pro- and anti-vaxxers separately. Among the pro-vaxxers’ tweets, the topics of “Child protection” and “COVID-19 situation” are positively predicting audiences’ engagement. For anti-vaxxers, the topics of “Supporting Trump,” “Injured children,” “COVID-19 situation,” “Media propaganda” and “Community building” are more appealing to audiences.

This study utilizes social media data and a state-of-art machine learning algorithm to generate insights into the development of emotionally appealing content and effective vaccine promotion strategies while combating coronavirus disease 2019 and moving toward a global recovery.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-03-2022-0186

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

The study examines the relationship between news intensity, media sentiment and market microstructure invariance-implied measures of trading activity and liquidity of Chinese property developer stocks during the 2020–2022 Chinese property sector crisis.

The authors adopt the extension of the news article invariance hypothesis, which is a generalization of the market microstructure invariance conjecture, from January 2020 to January 2022 to test specific quantitative relationships between the arrival rate of public information, trading activity and a nonlinear function of a proxy for the probability of informed trading. Empirical tests are based on a dataset of 22,412 firm-day observations and two count-data models to correct for overdispersion and the excess number of zeros. Seventy-five stocks of Chinese companies from the property development industry (including the China Evergrande Group) were included in the sample.

The authors reject the news article invariance hypothesis but document a positive and significant relationship between the flow of public information and risk liquidity. Additionally, the authors find that the proxy for informed trading activity is positively related to the arrival rates of public information from October 2021 to January 2022.

The findings support the hypothesis that negative (positive) media sentiment induces significant deterioration (insignificant improvement) in stock liquidity. The authors find that an increase in the number of news articles about a company corresponds to a higher liquidity of Chinese property developers' stocks after controlling for media sentiment.

The purpose of this paper is to examine the role of short sellers in foreign direct investment (FDI) decisions. Drawing on threat rigidity theory, the authors argue that short sellers pose a threat to chief executive officers (CEOs) by exerting downward pressure to target firms’ stock prices. That threat will evoke rigid managerial responses that hinder new FDI activities. The authors also posit that CEOs will be less reactive to short sellers’ threats when they are generalist CEOs who have extensive general work experience or when they serve as the board chair.

The authors collect data from S&P 1,500 firms, and the final sample consists of 717 firms and 6,930 firm-year observations from 1998 to 2016. The authors use an Arellano and Bond generalized method of moments static linear probability panel data model and an instrumental panel count data model to test the hypotheses.

The findings support the hypotheses and suggest that CEOs who are under more pressure from short sellers engage in fewer new FDI activities. The negative impact of short sellers on FDI decisions is less salient when CEOs have general work experience or are the chairperson of the board.

This study contributes to the international business research by stressing the need to consider the role of short sellers in firm internationalization decisions.

This study investigates the effect of user fees on access and waiting time in Nigeria. For access, the effect of user fees on both preventive and curative care; and the effect of user fees on waiting time at public healthcare facilities were examined. User fees are vital for the fiscal sustainability of healthcare provision for most African economies. Its imposition could debar healthcare access by the poor while its removal can reduce quality of care and induce longer waiting time.

The wave 3 of the Nigerian General Household Survey (2015/16) data was used for users of public health facilities. Access to healthcare was modelled using utilization data in a logistic regression model while waiting time was through the Negative Binomial Regression Model (NBRM).

The analyses showed significant effects of user fees on access to both preventive and curative care and on time spent waiting to make use of healthcare services. Individuals were able to access healthcare services regardless of amounts paid. Also, there was a non-negative effect of user fee imposition on waiting time.

Nigeria should improve healthcare facilities to address the enormous demand for healthcare services when designing policy for health sector.

This paper shows that even with the imposition of user fees, healthcare facilities could still not cater for the rising healthcare needs of the populace but cautioned that its abolition may not be a preferred option.

While the value of human capital for technological innovation is well acknowledged, literature on the role of vocational training in corporate innovation is notably scarce. The purpose of this study is to assess the effect of government support for small and medium-sized enterprise (SME) competencies on Korean firms’ innovation. The author investigates SMEs’ patent applications (supported by the government to varying degrees) while accounting for firms’ market position, ownership and management structure, as well as prior changes in firms’ technologies, products, processes and other characteristics. Alternative hypotheses about management motivation – the “lazy manager”, “career concerns” and “special East Asian institutional constraints” hypotheses – are also evaluated.

Censored and count data analysis methods are used on a panel of 595 Korean firms covering 2005–2015 from the Korean Human Capital Corporate Survey, Intellectual Property Office and National Investment Commission. A regression discontinuity estimator accounts for potential endogeneity because of support for vocational training at firms.

Firms receiving training support are more innovative than firms without support, but latent effects may play a role. The regression-discontinuity model suggests that firms that succeeded only marginally in obtaining support had higher innovative output than non-recipients near the eligibility threshold.

The findings of this study establish that government support had the intended effect on SMEs’ technological capacity. This cannot be discounted as a simple crowding-out effect. The author also establishes that management–ownership separation within firms was conducive to innovation, that product competition had an inverse U-shaped effect and that management–ownership separation had a substitutable relationship with competition in overcoming managers’ effort avoidance. The findings support the “lazy manager” hypothesis over the “career concerns” and the “special East Asian institutional constraints” hypotheses.

Disruptions and shortages of drugs have become severe problems in recent years, which has triggered strong media and public interest in the topic. However, little is known about the factors that can be associated with the increased frequency of shortages. In this paper, the authors analyze the drivers of drug shortages using empirical data for Germany, the fourth largest pharmaceutical market.

The authors use a dataset provided by the German Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte [BfArM]) with 425 reported shortages for drug substances (DSs) in the 24-month period between May 2017 and April 2019 and enrich the data with information from additional sources. Using logistic and negative binomial regression models, the authors analyze the impact of (1) market characteristics, (2) drug substance characteristics and (3) regulatory characteristics on the likelihood of a shortage.

The authors find that factors like market concentration, patent situation, manufacturing processes or dosage form are significantly associated with the odds of a shortage. The authors discuss the implications of these findings to reduce the frequency and severity of shortages.

The authors contribute to the empirical research on drug shortages by analyzing the impact of market characteristics, DS characteristics and regulatory characteristics on the reported shortages. The authors’ analysis provides a starting point for better prioritizing efforts to strengthen drug supply as it is currently intensely discussed healthcare authorities.

Although being fired up about changes such as firm expansion, chief executive officers (CEOs) have a hard time with changes that involve divesting businesses or downsizing operations. This study aims to examine how a particular psychological process – regulatory focus – serves as a managerial exit barrier in the context of store closings in the US retail industry. This study also examines how a particular corporate governance mechanism, the board of directors, moderates the relationship between CEO regulatory focus and divestment activity.

This study content-analyzed letters to shareholders to measure the regulatory focus of retail CEOs and used negative binomial regression to test the effect of the CEO’s regulatory focus and board independence on store closure activity.

The two motivation orientations – promotion and prevention – focuses have distinct effects on store closure decisions. As predicted, promotion-focused CEOs, who value attainment and growth, resist “pulling the plug.” Conversely, prevention-focused CEOs, who are more sensitive to losses, are more inclined to close stores. Independent boards decrease the CEOs’ resistance to “pull the plug” only when necessary, which is the case when CEOs have less vigilant tendencies.

This study contributes to the strategy and marketing literature. It examines an individual-level antecedent of store closure decisions and responds to the call for research on the effect of regulatory focus on divestment decisions.

Leaders themselves can be a source of resistance to change. The findings suggest the importance of boards hiring CEOs psychologically aligned with the firms’ strategic priorities. Promotion-focused CEOs may be a better fit for companies engaged in growth and acquisition. By contrast, prevention-focused CEOs may be a better fit for firms involved in retrenchment and restructuring. Independent boards still have the power to influence CEO decisions in the case of a misfit, as the findings suggest.

This study examines divestment decisions during the “retail apocalypse” and provides empirical evidence for the existence of managerial exit barriers, first introduced by Michael Porter.