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This study aims to capture how the association between a multichannel relational communication strategy (MRCS) and customer performance is contingent upon such customer performance (low- versus high-performance customers) and to reconcile past contradictory results in this marketing-related topic. To this end, the authors propose and validate the method of quantile regression as an unconventional, yet effective, means to proceed to that reconciliation.

This study collected data from 4,934 customers of a private pension fund firm and accounted for both firm- and customer-initiated relational communication channels (RCCs) and for customer lifetime value (CLV). This study estimated a generalized linear model and then a quantile regression model was used to account for customer performance heterogeneity.

This study finds that specific RCCs present different levels of association with performance for low- versus high-performance customers, where outcome customer performance is the dependent variable. For example, the relation between firm-initiated communication (FIC) and performance is stronger for low-CLV customers, whereas the relation between customer-initiated communication (CIC) and performance is increasingly stronger for high-CLV customers but not for low-CLV ones. This study also finds that combining different forms of FIC can result in a negative association with customer performance, especially for low-CLV customers.

The authors tested the conceptual model in one single firm in the specific context of financial services and with cross-sectional data, so there should be caution when extrapolating this study’s findings.

This study offers nuanced and precise managerial insights on recommended resource allocation along with relational communication efforts, showing how managers can benefit from adopting a differentiated-customer performance approach when designing their MRCS.

This study provides an overview of the state of the art of MRCS, proposes a contingency analysis of the relationship between MRCS and performance based on customer performance heterogeneity and suggests the quantile method to perform such analysis and help reconcile past contradictory findings. This study shows how the association between RCCs and CLV varies across the conditional quantiles of the distribution of customer performance. This study also addresses a recent call for a more holistic perspective on the relationships between independent and dependent variables.

This study aims to provide a framework for assessing the technical condition of a house to determine its market value, including the identification of other price-setting factors and their statistical significance. Time on market (TOM) in relation to the technical condition of a house is also addressed.

The primary database contains 631 houses, and the initial asking price and selling price are examined. All the houses are located in the Brno–venkov district in the Czech Republic. Regression analysis was used to test the influence of price-setting factors. The standard ordinary least squares estimator and the maximum likelihood estimator were used in the frame of generalized linear models.

Using envelope components of houses separately, such as the façade condition, windows, roof, condition of interior and year of construction, brings better results than using a single factor for the technical condition. TOM was found to be 67 days lower for houses intended for demolition – as compared to new houses – and 18 days lower for houses to refurbishment.

To the best of the authors’ knowledge, this paper is original in the substitution of specific price-setting factors for factors relating to the technical condition of houses as well as in proposing the framework for professionals in the Czech Republic.

This paper aims to analyse the influence of workforce diversity on the firm’s likelihood to develop organisational innovations. Operationalising human resources diversity is not straightforward, and its effect has been rather overlooked in the context of non-technological innovations. This study analyses the impact of task-related diversity among research and development (R&D) unit workers and women R&D workers, in particular.

To estimate the impact of task-related diversity on firm propensity to undertake organisational innovation, this study uses a generalised linear model (GLM) – with a binomial family and log–log extension. GLMs are used to control problems of over-dispersion, which, in models with binary response variables, could generate inaccurate standard error estimates and provide inconsistent results.

This paper provides three important results. Firstly, employee diversity increases the firm’s propensity to engage in organisational innovations. Secondly, the influence of each facet of task-related diversity varies depending on the type of organisational innovation considered. Thirdly, gender has an effect on the innovation process; this study shows that women play a different role in the production of non-technological innovations.

This paper makes several contributions to the literature. Firstly, it makes a theoretical contribution to research on innovation management by considering the influence of human resources diversity on the development of non-technological innovations. Secondly, this study analyses the role of workforce diversity in an R&D department context to clarify the contribution made by women R&D workers.

This study aims to examine the effect of gender equality on financial stability and financial inclusion for 14 developing countries using yearly data from 2005 to 2021.

The two-stage least squares regression estimation and the generalized linear model regression estimation were used to investigate the effect of gender equality on financial stability and financial inclusion.

Gender equality has a significant positive effect on financial stability and financial inclusion in developing countries. Gender equality has a significant positive effect on financial stability and financial inclusion in African countries. Gender equality has a significant positive effect on financial stability but not on financial inclusion in non-African countries.

Little attention has been paid to the role of gender equality in promoting financial stability and financial inclusion. The authors address this issue in this study.

The objective of this paper is to analyze the relationship between innovation and the development of artificial intelligence (AI) and digital technologies in Europe. The use of digital technologies among European companies is studied through a composite index, while the relationship between innovation and AI is studied through a log-linear regression model. The results of the model have made possible to develop interesting indications for economic and industrial policy.

The use of digital technologies among European companies is studied through a composite index of AI and information technology (ICT) (using the Fair and Sustainable Welfare methodology) with the aim of measuring territorial gaps and to know which European countries are more or less inclined to its use, while the relationship between innovation and AI is studied through a log-linear regression model.

In the paper, two different methodologies were used to analyze the relationship between innovation and the development of digital technologies in Europe. The synthetic indicator made possible to develop a taxonomy between the different countries, the log-linear model made possible to identify and explain the determinants of innovation.

The description of the biunivocal relationship between innovation and AI is a topical and relevant issue that is treated in the paper in an original way using a synthetic indicator and a log-linear model.

本文旨在探討在歐洲、創新與人工智能和數字技術的發展之間的關係。研究人員透過一個綜合指數、去探討歐洲公司之間數字技術的使用狀況。至於創新與人工智能之間的關係, 則以對數線性回歸模型來進行研究。從模型所得的結果, 為我們提供了建議、去訂定適切的經濟和產業政策。

研究人員透過一個人工智能和資訊科技的綜合指數, 去探討歐洲企業之間數字技術的使用狀況 (研究人員使用了公平和可持續福利方法論), 其目標為測量領土差距, 以及確定哪些歐洲國家、大體上傾向於使用數字技術;至於創新與人工智能之間的關係, 則以對數性回歸模型來進行研究。

本文使用了兩個不同的方法、去探討在歐洲、創新與數字技術發展之間的關係。有關的合成指標, 使研究人員可製定一個不同國家間的分類法;而有關的對數線性模型, 則讓研究人員可確立並說明創新的決定因素。

本文使用了合成指標和對數線性模型、去探討創新與人工智能之間的一對一的關係, 這是時下受到關注和適宜的課題;就研究法而言, 本研究確是新穎獨創的。

The current research aimed to examine the prevalence of Instagram posts featuring ultra-processed products targeted at adolescents in Uruguay and hence investigate the frequency of such posts among a vulnerable consumer segment in a country that cannot be classified as WEIRD (i.e. Western, educated, industrialized, rich and democratic).

The study relied on a cross-sectional content analysis. A total of 2,014 Instagram posts promoting ultraprocessed products or brands commercializing such products, generated by 118 Instagram accounts between August 15th, 2020, and February 15th, 2021, were analyzed. Nine indicators of food marketing targeted at adolescents were selected to identify posts targeted at this age segment. Inductive coding was used to describe the content of the posts. Descriptive statistics and generalized linear models were used to analyze the data.

In total, 17.6% of the posts were identified as targeted at adolescents. Graphic design and adolescent language were the most prevalent indicators of marketing targeted at adolescents, followed by explicit references to adolescents or young adults and memes. Posts identified as targeted at adolescents mainly promoted snacks and discretionary foods. Differences in the content of posts identified as targeted and not targeted at adolescents were observed.

The analysis was restricted to one social media platform in one country during a limited period of time, which limits the generalizability of the findings to other media platforms, samples and settings.

Results stress the need to implement digital food marketing regulations to reduce exposure of adolescents to the deleterious effects of stemming from marketing of unhealthy foods and provide empirical evidence to inform their development.

The study breaks new ground by analyzing the prevalence and exploring the characteristics and content of Instagram posts promoting ultra-processed products to adolescents in an under-researched geographic area of the world.

This study aims to examine a second-order latent variable of family functioning built from two established protective factors for American Indian (AI) youth, i.e. family cohesion and parental monitoring. This study then examines if family functioning is related to alcohol use frequency or age of initiation for AI youth. Additionally, this study examines if family functioning served as a moderator for the risk factor of peer alcohol use.

Data came from the 2021 Our Youth, Our Future survey. Participants were 4,373 AI adolescents from Grades 6–12 across the contiguous USA. Structural equation modeling (SEM) was used to test the latent variable of family functioning. Structural paths and interaction terms between peer use and family functioning were added to the SEM to explore direct and moderating effects.

Family cohesion and parental monitoring were best represented by a second-order latent variable of family functioning, which was related to later initiation and lower alcohol use frequency.

The findings regarding the initiation of alcohol use may be applicable to prevention programs, with family functioning serving as a protective factor for the initiation of alcohol use. Programs working toward alcohol prevention may be best served by focusing on family-based programs.

The latent variable of family functioning is appropriate for use in AI samples. Family functioning, which is an inherent resilience factor in AI communities, was shown to be protective against harmful alcohol use behaviors.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

This paper aims to investigate the donor characteristics of Muslim donors and fills the gap by empirically surveying Muslim donors from Kuwait. The authors believe their sample choice to be of importance due to the stark contrast between the Kuwaiti and Asian environment, of which much of the literature’s findings on Muslim donor behavior was based on.

The characteristics studied include demographics, socioeconomics, individual attitudes, trust perceived generosity among others identified in the literature. Data was gathered by disseminating 320 surveys to better understand which variables have significant influence on an individual’s charity behavior. Statistical analysis using regression method was used to analyze the data.

The findings report that fundraising campaigns, perceived financial security are significant and there is also a significant association between certain charity activities and gender. The findings have implications on market segmentation and promotional strategies aimed toward similar donor profiles and for the charities soliciting Zakat who are based in the Gulf Cooperation Council region.

The contributions of this manuscript further the knowledge of donor behavior and thus enrich the body of work within research that explores the role of marketing in philanthropic and non-profit organizations. This study provides deeper insights into the Muslim’s donor behavior and from a managerial standpoint, facilitates on how to target them effectively when soliciting donations or raising funds for campaigns within Muslim communities, an area that has received little attention from research investigating marketing for nonprofit organizations.

This intensified case finding study aimed to evaluate the prevalence of tuberculosis (TB) disease among people with HIV entering the largest prison in Malaysia.

The study was conducted in Kajang prison, starting in July 2013 in the men’s prison and June 2015 in the women’s prison. Individuals tested positive for HIV infection, during the mandatory HIV testing at the prison entry, were consecutively recruited over five months at each prison. Consented participants were interviewed using a structured questionnaire and asked to submit two sputum samples that were assessed using GeneXpert MTB/RIF (Xpert) and culture, irrespective of clinical presentation. Factors associated with active TB (defined as a positive result on either Xpert or culture) were assessed using regression analyses.

Overall, 214 incarcerated people with HIV were recruited. Most were men (84.6%), Malaysians (84.1%) and people who inject drugs (67.8%). The mean age was 37.5 (SD 8.2) years, and median CD4 lymphocyte count was 376 cells/mL (IQR 232–526). Overall, 27 (12.6%) TB cases were identified, which was independently associated with scores of five or more on the World Health Organization clinical scoring system for prisons (ARR 2.90 [95% CI 1.48–5.68]).

Limited data exists about the prevalence of TB disease at prison entry, globally and none from Malaysia. The reported high prevalence of TB disease in the study adds an important and highly needed information to design comprehensive TB control programmes in prisons.