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The purpose of this study is to investigate the socioeconomic determinants of property crimes on convicted offenders in the Chilga district correctional institution (CDCI).

This study conducted a socioeconomic determinant of property crimes on convicted offenders using quantitative research. Respondents consisted of a random sample of 170 convicted offenders in CDCI. This study used descriptive statistics, logistic regression and Pearson correlations for analyzing the quantitative data in CDCI.

The results of the study showed that the age at first engagement, educational status, offender’s immediate economic situation and previous experience of the offender were the perceived reasons in one’s major property crime offending. However, average monthly income, peer effect and family structure (grown up with) were found to be non-perceived reasons. Youths who are unmarried, illiterate and unemployed offenders had over three times more probabilities of committing theft than robbery and burglary in the winter season, especially in February, because of the determinants of illiteracy and unemployment such as negligence for the future life. Furthermore, the convicted offenders who were illiterate, unemployed and raised by single parents have engaged in theft for the first time, but burglary and robbery by employed and literate offenders in more probable.

This paper takes a fresh perspective on knowledge about property crime and economic as well as criminological theories using various bodies of academic research. This paper’s insight will be helpful to fill the literature gaps; there are lot research studies with little investigations addressing to the issue of the determinants of property crime. It will also be useful for policymakers to mitigate the determinant of property crime.

This paper aims to assess the performance of achieving the targets of Sustainable Development Goals (SDGs) by aligning with the recent initiatives taken by different ministries and divisions in Bangladesh. It also examines the institutional challenges associated with the implementation of SDGs.

Both primary and secondary data were analysed by applying different techniques. The initiatives taken by various ministries and divisions of Bangladesh resonated partially with 84 targets of SDGs.

Ambiguous mandates of different ministries and divisions have caused overlapping jurisdiction, followed by conflicts of interest and lack of cohesion. Inaccurate stakeholder analysis, data unavailability, lack of competency and accountability, and a top-down policy approach can be considered as the major institutional bottlenecks in achieving SDGs.

This paper advocates revising the map to incorporate the right stakeholders to avoid erroneous monitoring, evaluation, and finally, inaccurate reporting. The reallocation of business for ministries and divisions is warranted to abolish jurisdictional overlapping and conflict of interests and make SDGs friendly. Despite the COVID-19 pandemic recession, Bangladesh may be able to multiply its achievement with the same resources and efforts by addressing those drawbacks.

The purpose of this research is the exploration of online complainants' revenge based on their consumer-brand relationship strength and received webcare. The authors introduce inter-failures (i.e. the perceived number of earlier independent service failures that a customer has experienced with the same brand involved in the current service failure) as the central frame condition.

To test our hypotheses, both a scenario-based online experiment (n = 316) and an online survey (n = 492) were conducted.

With an increasing number of inter-failures, online complainants with a high-relationship strength move from the “love is blind” effect (no inter-failures) to the “love becomes hate” effect (multiple inter-failures), when they ultimately become more revengeful than their low-relationship strength counterparts. In addition, the authors show that in the case of no or few inter-failures, accommodative webcare has a lasting positive effect over no/defensive webcare for both low- and high-relationship complainants. More importantly, however, when consumers have experienced multiple inter-failures, accommodative webcare becomes ineffective (for low-relationship complainants) or boomerangs by cultivating revenge towards the brand (among high-relationship complainants), but not strategic avoidance.

The findings have pronounced implications for the literature on customer–brand relationships following service failures and the literature, which predominantly emphasizes the unconditionally positive effects of accommodative webcare.

This study is the first that simultaneously considers the prior customer–brand relationship, inter-failures and webcare to explain online complainants' revenge.

The study of shame has a long tradition in intra- and inter-personal psychology. This paper aims to investigate whether consumers can experience brand shame after self-relevant consumption incidents. Specifically, this research proposes that consumers follow a complex shame-inducing process in the aftermath of unpleasant experiences involving their favorite brand. The moderating role of relational tie strength between consumers and their favorite brand existing prior to symbolic failures is examined.

A scenario-based, online survey (n = 660) among consumers who have recently experienced a self-relevant failure with their favorite brand was conducted. Confirmatory factor analysis ensured the reliability and validity of the measurement model. For testing the conceptual model, data was analyzed by means of a moderated mediation analysis. The proposed model was tested against, among others, common method bias and alternative models. The findings were cross-validated with a scenario-based online experiment (n = 1,616).

Results show that brand shame is a key mediator between customer dissatisfaction and brand anger when self-relevant, symbolic failures happen. Moreover, strong consumer-brand identification triggers brand-detrimental effects. It is shown to influence the connection between consumers’ inward- (i.e. brand shame) and resulting outward-directed (i.e. brand anger) negative emotions on brands, which lead to consumer vengeance.

To the best of the authors’ knowledge, this research is the first to introduce the concept of situational brand shame to the literature on favorite brands. Furthermore, it shows that consumer-brand identification moderates the direct and indirect (via brand shame) unfavorable effects of failure-induced dissatisfaction on brand anger. This research adds insights to the investigation of the “love-becomes-hate” effect arising after self-relevant failures involving consumers’ most preferred brand.

In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.

The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.

The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.

To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.

This paper aims to demonstrate that online complainants’ reactions to a company’s service recovery attempts (webcare) can significantly vary across two different types of dissatisfied customers (“vindictives” vs “constructives”), who have dramatically diverging complaint goal orientations.

Online multi-country survey among 812 adult consumers who recently had a dissatisfying brand experience and turned to a marketer-generated social media site to voice an online complaint for achieving their ultimate complaining goals. Scenario-based online experiment for cross-validating the survey findings.

Results suggest that “vindictive complainants” – driven dominantly by brand-adverse motives – are immune to any form of webcare, while “constructive complainants” – interested in restoring the customer-brand relationship – react more sensitively. For the latter, “no-responses” often trigger detrimental brand-related reactions (e.g. unfavorable brand image), whereas “defensive responses” are likely to stimulate post-webcare negative word-of-mouth.

This research identifies the gains and harms of (un-)desired webcare. By doing so, it not only sheds light on the circumstances when marketers have to fear negative effects (e.g. negative word-of-mouth) but also provides insights into the conditions when such effects are unlikely. While the findings of the cross-sectional survey are validated with an online experiment, findings should be interpreted with care as other complaining contexts should be further investigated.

Marketers have to expect a serious “backfiring effect” from an unexpected source, namely, consumers who were initially benevolent toward the involved brand but who received an inappropriate response.

This research is one of the first research studies that enables marketers to identify situations when webcare is likely to backfire on the brand after a service failure.

This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients.

A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification.

The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection.

This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

This study aims to address customer focus as an important component of total quality management (TQM) and explore the key drivers of member satisfaction in tennis clubs via a novel theory-based member satisfaction index (MSI) model with high explanatory and predictive power. Furthermore, the study aims to investigate the relationship between satisfaction and behavioral intentions (willingness to stay; WTS) with consideration of the mediating effect of identification with the club.

This study uses variance-based partial least squares structural equation modeling (PLS-SEM) to estimate the MSI model, which was tested in a leading tennis club in Germany (n = 185).

The results reveal that club atmosphere, club facilities and the price/quality ratio of the membership fee are the most important drivers of member satisfaction in tennis clubs. Member satisfaction has a large influence on the WTS of tennis club members. Identification with the club, when included as a mediator in the model, increases the variance explained in WTS considerably.

The small sample limits the generalizability of findings, and further research is recommended.

The MSI model is a useful benchmark tool for club managers who want to quantify the satisfaction and WTS of their club members. In addition, because of the integrated formative measurement models, the PLS-SEM results show which indicators can be used to positively impact satisfaction with each of the service quality dimensions, overall member satisfaction and WTS. The most important of these results are discussed in an importance-performance map analysis.

The MSI model is a multi-attribute index model through which members' evaluations of various dimensions of service and value are derived through multivariable linear function with each dimension weighted according to its importance in one holistic model. The model shows the strong impact of satisfaction on WTS of sports club members and reveals that findings of previous research on the relationship between fan and spectator identification and loyalty are transferable to sports club members. The MSI represents a new contribution to the literature; it was applied here to tennis clubs but is also suitable for application to other sports clubs.

The effects of board composition on performance have long been discussed in management research using fractionalization measures. In this study, we propose an alternative measurement approach based on board polarization.

Using an exploratory analysis and applying the polarization measure to German Deutscher Aktienindex (DAX)-, Midcap-DAX (MDAX)- and Small Cap-Index (SDAX)-listed companies, this paper applies the polarization index to examine the relationship between board diversity and performance.

The results show that the polarization concept is well suited to measure principal-agent problems between the members of the management and supervisory boards. We reveal that board polarization is negatively associated with firm performance, as measured by return on investment (ROI).

This exploratory study shows that the measurement of board polarization can be linked to performance differences between companies, which offers promising starting points for further research.

The transition to omnichannel retail is the recognized future of retail, which uses digital technologies (e.g. augmented reality shopping assistants) to enhance the customer shopping experience. However, retailers struggle with the implementation of such technologies in brick-and-mortar stores. Against this background, the present study investigates the impact of a smartphone-based augmented reality shopping assistant application, which uses personalized recommendations and explainable artificial intelligence features on customer shopping experiences.

The authors follow a design science research approach to develop a shopping assistant application artifact, evaluated by means of an online experiment (n = 252), providing both qualitative and quantitative data.

Results indicate a positive impact of the augmented reality shopping assistant application on customers' perception of brick-and-mortar shopping experiences. Based on the empirical insights this study also identifies possible improvements of the artifact.

This study's assessment is limited to an online evaluation approach. Therefore, future studies should test actual usage of the technology in brick-and-mortar stores. Contrary to the suggestions of established theories (i.e. technology acceptance model, uses and gratification theory), this study shows that an increase of shopping experience does not always convert into an increase in the intention to purchase or to visit a brick-and-mortar store. Additionally, this study provides novel design principles and ideas for crafting augmented reality shopping assistant applications that can be used by future researchers to create advanced versions of such applications.

This paper demonstrates that a shopping assistant artifact provides a good opportunity to enhance users' shopping experience on their path-to-purchase, as it can support customers by providing rich information (e.g. explainable recommendations) for decision-making along the customer shopping journey.

This paper shows that smartphone-based augmented reality shopping assistant applications have the potential to increase the competitive power of brick-and-mortar retailers.