Search results

The purpose of this paper is to build a compact and accurate classifier for high-dimensional classification.

A classification approach based on class-dependent feature subspace (CFS) is proposed. CFS is a class-dependent integration of a support vector machine (SVM) classifier and associated discriminative features. For each class, our genetic algorithm (GA)-based approach evolves the best subset of discriminative features and SVM classifier simultaneously. To guarantee convergence and efficiency, the authors customize the GA in terms of encoding strategy, fitness evaluation, and genetic operators.

Experimental studies demonstrated that the proposed CFS-based approach is superior to other state-of-the-art classification algorithms on UCI data sets in terms of both concise interpretation and predictive power for high-dimensional data.

UCI data sets rather than real industrial data are used to evaluate the proposed approach. In addition, only single-label classification is addressed in the study.

The proposed method not only constructs an accurate classification model but also obtains a compact combination of discriminative features. It is helpful for business makers to get a concise understanding of the high-dimensional data.

The authors propose a compact and effective classification approach for high-dimensional data. Instead of the same feature subset for all the classes, the proposed CFS-based approach obtains the optimal subset of discriminative feature and SVM classifier for each class. The proposed approach enhances both interpretability and predictive power for high-dimensional data.

The purpose of this study is to present a systematic review of the online service failure (OSF) literature and conduct an exhaustive analysis of academic research on this emerging research area.

The current study has adopted a structured systematic literature review approach to synthesize and assess the OSF literature. Further, the study uses the Theory-Context-Characteristics-Methodology (TCCM) framework to propose future research directions in the OSF domain.

This systematic review shows that OSF research is still developing and remains mainly incoherent. Further, the study develops a conceptual framework integrating the frequently reported antecedents, mediators, moderator and consequences in the extant literature. This review also synthesizes the theoretical perspectives adopted for this domain.

The study followed specific inclusion and exclusion criteria to shortlist articles. Further, articles published only in the English language were considered. Hence, the findings of this review cannot be generalized to all OSF literature.

This systematic review has classified antecedents into customers' and service providers' roles which will enable online service providers to understand all sets of factors driving OSF. It also synthesizes and presents service recovery strategies and emphasizes the role of online customer support to fix OSF.

The OSF literature is still developing and remains highly incoherent, suggesting that a synthesized review is needed. This study has systematically reviewed and synthesized the OSF literature to study its development over time and proposes a framework which provides a comprehensive understanding of OSF.

A vast amount of educational literature has repeatedly documented the overrepresentation of African American male students in exclusionary school punishment. However, amid the wealth of data and statistics on the topic, a viable theoretical explanation, that helps to make sense of the disproportionately high number of suspensions for Black males, remains relatively absent. Drawing upon the Method of Theory Triangulation, this chapter uses three conceptual frames to develop a plausible, causal narrative for deconstructing how pose, perception, and threat converge create a perfect storm of conditions that perpetuate discriminatory discipline practices. Based on the theoretical considerations implicit in this account, practical recommendations are offered to educational stakeholders who might be interested in improving school discipline practices and reducing the number of Black males disproportionately targeted for disciplinary action.

This study aims to explore the human resource management (HRM) practices related to training and feedback in the app work industry, specifically in online food delivery service, and investigate the emotional and behavioral responses of gig workers.

This study adopts a qualitative approach by interviewing 19 gig workers from six food delivery firms operating in different countries.

The results show limited training and feedback opportunities are provided to app workers, although the complexity of training and delivery methods differ across platforms. To address this shortage, app workers developed response strategies relying on social interaction.

This study adds to the research on HRM practices in the gig economy by portraying the way in which training and feedback unfold in the food delivery app ecosystem and by disclosing the gig workers’ emotional and behavioral responses to it.

This study shows that the way training activities are currently designed may provide little value to the ecosystem and are likely to produce negative emotional responses in gig workers. Thus, platform providers may make use of these findings by introducing more transparent feedback and social learning opportunities.

To the best of the authors’ knowledge, this study is among the first empirical studies on online delivery gig workers addressing specific HRM practices. It reveals significant insights for training and feedback, suggesting app economy characteristics strongly affect training and feedback practices for app workers.

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.

Using a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).

The findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.

Based on the findings, the authors provided several implications for app stores, app developers and app marketers.

This study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.

This article attempts to contribute to medical dispute resolution by examining the adoption of medical judicial expertise opinions in determining medical malpractice responsibility and its coordination with the judge’s legal opinions.

This article examines the legal basis and empirical data to demonstrate the decisive effect of medical judicial experts’ opinions in allocating medical malpractice responsibility and corresponding dispute resolution effectiveness.

High reliance on medical judicial expertise in medical dispute litigation not only unifies the judicial standards but also limits judges’ discretion, which brings the risk of contradiction between factual and legal findings, which currently ends in judges’ compromise.

The current medical malpractice provisions neglect the divergence of medical judicial expertise and judges’ opinions in determining medical malpractice responsibility, which produces difficulties in harmonizing awarded compensations and parties’ expectations, leading to problematic medical dispute litigation in Mainland China.

While the popularity of online shopping has increased in recent years, surprisingly little research has examined the factors affecting consumers’ behavior in this context. Furthermore, though a widespread problem for companies, the phenomenon of online shopping cart abandonment has garnered even less attention. The purpose of this study is to examine the role of consumers’ mindsets in online shopping cart abandonment.

Three experimental studies were conducted to examine the effect of consumer mindsets (i.e. abstract vs concrete) on purchase intentions.

Results indicate that consumers who have an abstract (as opposed to concrete) mindset when shopping online rate the products they include in their shopping carts to be more important, and consequently are more likely to purchase them, reducing shopping cart abandonment.

The findings suggest that online retailers can reduce shopping cart abandonment by implementing strategies that allow consumers to think abstractly.

This research contributes to the literature by identifying an important underlying mechanism affecting online shopping cart abandonment.

This paper aims to examine the impact of health and other exogenous shocks on stock markets in Africa. Particularly, the authors examined the resilience of the major stock markets in 12 African economies during the recent global pandemic.

This paper uses the recent panel vector autoregressive model, which enables us to capture the response of stock markets to shocks in COVID-19, commodity markets and exchange rate. For robustness, the authors also analysed the panel Granger causality test. Data was obtained for the period ranging from 2 January 2020 to 31 December 2020.

The results show that the growth in COVID-19 cases and deaths do not have any substantial impact on the stock market returns of these economies. In terms of commodity markets, the authors find that gold price has a negative contemporaneous effect on stock returns, but the effect fizzles out around the fifth day while crude oil price, on the other hand, has a significant positive simult aneous impact on stock returns and also converges around the fifth day. The authors further find that the exchange rate has a contemporaneous and nonlinear effect on stock returns and seems to be more dramatic when compared with the other variables. Overall, the results show that stock markets in Africa appear to be flexible and resilient against the COVID-19 outbreak but are affected by other exogenous shocks such as volatile commodity prices and the foreign exchange market. The effect is, however, short-lived – between one to five days.

Following the study’s findings, policies should be put in place to support financial markets by way of hedging against commodity instability and securing domestic currency financing. Policymakers are also recommended to concentrate on managing the uncertainties around their exchange rate markets and develop robust and efficient domestic financial markets to encourage local and foreign investors.

Several studies have been carried out on the effects of disasters (such as the COVID-19 pandemic) on stock markets, but only a few studies have examined the resilience of stock markets to health and other exogenous shocks. This study’s attempt is not only to examine the impact of COVID-19 health shocks on stock markets but also to analyse the resilience of the sampled stock markets. The authors also analyse the resilience of stock markets to commodity markets and exchange rates shocks.

Given that global advertising often originates in the West, this study aimed to investigate whether the same racial cues in advertising had different effects on US and Chinese readers who belonged to the majority group in their own country but occupied varied positions in the global racial hierarchy.

The study included two experiments that used US and Chinese college students as participants to test the effect of model race on model likeability, ad attitude, and brand attitude.

The experiments confirmed that model race affected the effectiveness of advertising, though the nature and extent of its effects were mediated by factors such as country and product category. The effects of model race were much more pronounced for the Chinese data than for the US data, and for computer than for beer.

The findings illustrate the inadequacy of the center-periphery approach to global advertising planning, expose the limitations of the social identity theory in predicting racial attitudes in international settings, and outline the ethical challenge for global advertisers to develop effective multicultural advertising.

To date, most studies on racial cues in advertising have focused on the USA, and very few have explored the topic in the context of other markets. As an initial cross-country comparative investigation, this study suggests that multicultural advertising does not have the same effect in international markets.

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

The authors focus on the Bayesian equivalents of the frequentist approach for testing heteroskedasticity, autocorrelation and functional form specification. For out-of-sample diagnostics, the authors consider several tests to evaluate the predictive ability of the model.

The authors demonstrate the performance of these tests using an application on the relationship between price and occupancy rate from the hotel industry. For purposes of comparison, the authors also provide evidence from traditional frequentist tests.

There certainly exist other issues and diagnostic tests that are not covered in this paper. The issues that are addressed, however, are critically important and can be applied to most modeling situations.

With the increased use of the Bayesian approach in various modeling contexts, this paper serves as an important guide for diagnostic testing in Bayesian analysis. Diagnostic analysis is essential and should always accompany the estimation of regression models.