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Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

This paper investigates the dynamics between state affect and trusting cognitive beliefs on post-adoptive information technology (IT) use behaviors in the form of intention to explore and deep structure usage. That state affect can influence behaviors is recognized in practice. In fact, some studies examine the impact of affective constructs, but the way state affect impacts how individuals use IT remains largely unexplored. The authors theorize that state affect, in the form of positive and negative affect, will influence trusting cognitive beliefs regarding an IT artifact (in terms of perceived helpfulness, capability and reliability) and, more importantly, directly influence intention to explore and deep structure usage.

The authors test the model using a sample of 357 IT users. Survey items were derived from established measures, and the data were analyzed using structural equation modeling.

Results of this study suggest that positive affect and negative affect impact trusting cognitive beliefs. Trusting cognitive beliefs positively impact intention to explore with an IT and deep structure usage of an IT. Even in the presence of trusting beliefs, though, positive affect directly impacts intention to explore. Positive affect and negative affect both also have various indirect, mediated effects on intention to explore and deep structure usage.

In order to maximize value from workplace IT, the results suggest managers foster an authentic, positive work environment in order to harness or redirect employees' emotional energies.

Digital Service innovation (DSI) plays a fundamental role in the successful transition from product manufacturer or traditional service provider to a provider of digitally-enabled service solutions. Multiple impediments make managing this transformation using digital technologies difficult for firms, their customers and wider ecosystems. Extant knowledge of these digital technology impediments requires synthesizing and mapping.

A systematic literature review (SLR) was conducted. DSI tools and terminology are synthesized via thematic analysis. Subsequently, impediments to DSI for servitization (covering barriers, challenges and tensions) faced by actors across three key innovation phases: strategic planning, design planning and implementation, and four interaction levels (Micro, Meso, Macro-environment, Macro-ecosystem) are mapped via template analysis.

Six impediment categories (external environmental factors, internal firm factors, capabilities, business models and processes, value creation and interaction) encompassing 28 unique impediment types to DSI during servitization are identified. A framework enabling impediment comparison across innovation phases and ecosystem/network interaction levels, revealing that the majority of barriers can be framed as “challenges” was developed.

Whilst literature is emerging relating to digital servitization, there is a lack of research on the role DSI plays in facilitating digital servitization and no comprehensive study of DSI impediments exists. Additionally, consensus around the cross-disciplinary terminologies used is lacking. This study is a structured attempt to map the domain, summarizing the terms, identifying and clarifying impediment categories and providing recommendations for researchers and managers in tackling the latter.

Counterproductive work behavior (CWB) imposes significant costs on organizations, thus antecedents of CWB are of particular interest to both practitioners and academics. The authors examine how one’s own narcissism interacts with co-worker narcissism to influence willingness to engage in retaliatory CWB against a co-worker.

The data for this study were obtained from Amazon Mechanical Turk participants and Master of Business Administration students, representing a cross-section of employee representatives.

The authors find that employees expect narcissistic co-workers to engage in continuing future CWB and this, in turn, increases employees' willingness to engage in retaliatory CWB. That is, non-narcissistic employees are provoked to engage in organizationally-destructive behaviors by peers perceived as narcissists. This affect is attenuated by the employee’s own narcissism. Relative to non-narcissists, narcissistic employees find a narcissistic co-worker more likeable, which reduces their willingness to engage in retaliatory CWB against the co-worker.

For corporations and HR managers, this study demonstrates the caution necessary when considering hiring and operational practices. Specifically, non-narcissists demonstrate increased willingness to engage in organizationally-destructive behaviors after interpersonal conflict with a narcissistic co-worker.

The authors extend prior research about interpersonal drivers of CWB, which primarily considered superior-subordinate dyad, by examining the joint effects of individual and co-worker narcissism in peer-to-peer relationships.

This study aims to investigate the effect of the virtual spokesperson type on the consumers' preference for new products. To meet the consumer needs of Generation Z, virtual spokespeople have become new assistants in brand marketing. However, how virtual spokespersons drive consumer preference for new products is minimally understood.

This research conducts three experiments to investigate the influence of virtual spokesperson type on consumers' preference for new products.

The research shows that, for radically new products, competent virtual spokespersons improve consumers' perception of self-efficacy and thus consumers' preference; for incrementally new products, warm virtual spokespersons improve consumers' perception of social connection and thus consumers' willingness to buy.

This study broadens research on brand spokespersons and virtual spokespersons. This research also enriches and expands research on the consideration of new product types in brand spokespersons.

This paper aims to clarify the effect of discount discrepancy (DD) on consumers’ purchase intention (PI). The authors propose, test and provide evidence and explanations about the moderation of justification in the relation between consumers’ perceived DD and PI.

The authors conducted three experimental studies with a 2 × 2 factorial design, focusing on consumers’ processing of price discounts. Participants were informed that this study aimed to gather opinions on fashion, clothing and retail sales promotions. They accessed the questionnaire via Qualtrics. Each participant took part in only one study. The experimental conditions were manipulated through scenarios.

Study 1 tested and supported the moderation of justification on the effect of DD on PI. Study 2 tested and supported the moderation of the type of justification for the effect of DD on PI. Study 3 confirmed the findings in Study 2 and revealed the more effective type of justification.

The authors focused on a typically hedonic product category (fashion clothing). Further research should include a wider variety of goods and services, which could lead to different explanations or generalizations.

Sales promotions must refrain from generating DD between the initial price discount and the subsequent smaller discounts. Practitioners must evaluate the gains of an initial, more considerable percentage discount to attract consumers to the store and sell them other products versus the cost of losing sales because of DD. Management should recognize the importance of giving the correct justification for perceived DD, aligning the firm’s justification with the consumer’s motivation to buy the product.

The authors offer subsidies for effective consumer protection policies.

By studying the influence of justification on the effect of DD on PI, the authors propose a mechanism that would reduce the negative effect of DD on consumers’ PI.

This study aims to examine the potentially important effects of academic embeddedness on college of business student retention and performance as well as the mediating effects of self-efficacy on the academic embeddedness student outcomes relationships. Improvements in student retention and performance reduce costs for students and universities and lead to higher incomes for graduates.

Data were gathered from students in an entry-level business course at a public university in a rural western state. Approximately 45% of the students were female, and the average age of participants was 20 years old. A survey was administered midsemester to gather data on academic embeddedness and self-efficacy. Retention was indicated by a student enrolling in a business course in a subsequent semester. Performance was measured using end-of-semester course grades. Logistic and linear regression as well as mediation analysis were used to test the hypotheses.

Academic embeddedness was found to positively predict both retention and performance, while self-efficacy was found to positively mediate the academic embeddedness retention relationship. The direct effect of embeddedness on performance was not found when controlling for self-efficacy.

Student retention and performance are important to both students and academic administrators. The findings of this study suggest that retention and performance can both be improved by focusing on factors that more strongly embed students to their colleges.

Embeddedness has been found to have high predictive validity in the employment context. This is one of the first studies to consider the effects of embeddedness in the academic context.