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This study aims to examine the effectiveness of an integrated intervention program for alcoholism (IIPA) for improving verbal encoding and memory, visuospatial construction, visual memory and quality of life (QoL) in persons with alcohol dependence.

The sample comprised treatment-seeking alcohol-dependent persons (n = 50), allotted into two groups: (1) the treatment as usual (TAU) group (n = 25) and (2) the treatment group (n = 25)]. The groups were matched on age (±1 year) and education (±1 year). The TAU group received standard pharmacological treatment, psychotherapeutic sessions on relapse prevention and yoga for 18 days, while the treatment group received IIPA sessions in addition to the usual treatment. Auditory verbal learning test, complex figure test and QoL scale were administered at pre- and post-treatment along with screening measures.

The two groups were comparable on demographic variables, clinical characteristics and outcome measures at baseline. Pre- to post-treatment changes (gain scores) comparison between the treatment and TAU groups revealed a significant difference in verbal encoding, verbal and visual memory, verbal recognition, visuospatial construction and QoL.

This study suggests that IIPA is effective for improving learning and memory in both modality (verbal and visual) and QoL in persons with alcoholism. The IIPA may help in better treatment recovery.

The IIPA may help in treatment for alcoholism and may enhance treatment efficacy.

IIPA is effective for improving learning and memory in both modalities and QoL in persons with alcohol dependence. The IIPA may help in better treatment recovery.

Recently, disruptive technologies (DTs) have proposed several innovative applications in managing logistics and promise to transform the entire logistics sector drastically. Often, this transformation is not successful due to the existence of adoption barriers to DTs. This study aims to identify the significant barriers that impede the successful adoption of DTs in the logistics sector and examine the interrelationships amongst them.

Initially, 12 critical barriers were identified through an extensive literature review on disruptive logistics management, and the barriers were screened to ten relevant barriers with the help of Fuzzy Delphi Method (FDM). Further, an Interpretive Structural Modelling (ISM) approach was built with the inputs from logistics experts working in the various departments of warehouses, inventory control, transportation, freight management and customer service management. ISM approach was then used to generate and examine the interrelationships amongst the critical barriers. Matrics d’Impacts Croises-Multiplication Applique a Classement (MICMAC) analysed the barriers based on the barriers' driving and dependence power.

Results from the ISM-based technique reveal that the lack of top management support (B6) was a critical barrier that can influence the adoption of DTs. Other significant barriers, such as legal and regulatory frameworks (B1), infrastructure (B3) and resistance to change (B2), were identified as the driving barriers, and industries need to pay more attention to them for the successful adoption of DTs in logistics. The MICMAC analysis shows that the legal and regulatory framework and lack of top management support have the highest driving powers. In contrast, lack of trust, reliability and privacy/security emerge as barriers with high dependence powers.

The authors' study has several implications in the light of DT substitution. First, this study successfully analyses the seven DTs using Adner and Kapoor's framework (2016a, b) and the Theory of Disruptive Innovation (Christensen, 1997; Christensen et al., 2011) based on the two parameters as follows: emergence challenge of new technology and extension opportunity of old technology. Second, this study categorises these seven DTs into four quadrants from the framework. Third, this study proposes the recommended paths that DTs might want to follow to be adopted quickly.

The authors' study has several managerial implications in light of the adoption of DTs. First, the authors' study identified no autonomous barriers to adopting DTs. Second, other barriers belonging to any lower level of the ISM model can influence the dependent barriers. Third, the linkage barriers are unstable, and any preventive action involving linkage barriers would subsequently affect linkage barriers and other barriers. Fourth, the independent barriers have high influencing powers over other barriers.

The contributions of this study are four-fold. First, the study identifies the different DTs in the logistics sector. Second, the study applies the theory of disruptive innovations and the ecosystems framework to rationalise the choice of these seven DTs. Third, the study identifies and critically assesses the barriers to the successful adoption of these DTs through a strategic evaluation procedure with the help of a framework built with inputs from logistics experts. Fourth, the study recognises DTs adoption barriers in logistics management and provides a foundation for future research to eliminate those barriers.

The purpose of this study is to construct the first short-term financial distress prediction model for the Spanish banking sector.

The concept of financial distress covers a range of different types of financial problems, in addition to bankruptcy, which is not common in the sector. The methodology used to predict financial problems was artificial neural networks using traditional financial variables according to the capital, assets, management, earnings, liquidity and sensibility system, as well as a series of macroeconomic variables, the impact of which has been proven in a number of studies.

The results obtained show that artificial neural networks are a highly suitable method for studying financial distress in Spanish credit institutions and for predicting all cases in which an entity has short-term financial problems.

This is the first work that tries to build a model of artificial neural networks to predict the financial distress in the Spanish banking system, grouping under the concept of financial distress, apart from bankruptcy, other financial problems that affect the viability of these entities.

Despite various governmental efforts, female entrepreneurship in the UAE is still not a popular option among women. The purpose of this study is to identify the enablers to female entrepreneurship and to establish relationships among them.

This research uses a two-phased approach. In the first-phase, an empirical study on female entrepreneurs was conducted to find out the most important enablers from among a set of variables identified through a comprehensive literature review. In the second-phase, using interpretive structural modeling, a hierarchy-based model is developed among the most important enablers. Further, these enablers are also classified depending on their driving power and dependence.

Survey results indicate that female entrepreneurs consider enablers as the driving force in creating an entrepreneurial culture. Further, the hierarchy-based model developed in this research helps to identify variables that are of strategic importance and require utmost attention.

The framework presented in this study can be used effectively by the policymakers to develop suitable strategies for improving entrepreneurial behavior among women in the United Arab Emirates.

The novelty of this approach is the integration of questionnaire and interpretive structural modeling methodology and classifying enablers in four clusters. The research provides useful insights about the perception of female entrepreneurs about the entrepreneurial culture in the United Arab Emirates, and a relationship model that may serve as a decision tool for improving female entrepreneurship.

This study aims to investigate the factors that influence South African customers to potentially switch from one bank to another. Instead of using established models and survey techniques, the research measured social media sentiment to measure threats to switch.

The research involved a 12-month analysis of social media sentiment, specifically customer threats to switch banks (churn). These threats were then analysed for co-occurring themes to provide data on the reasons customers were making these threats. The study used over 1.7 million social media posts and focused on all five major South African retail banks (essentially the entire sector).

This study concluded that seven factors are most significant in understanding the underlying causes of churn. These are turnaround time, accusations of unethical behaviour, billing or payments, telephonic interactions, branches or stores, fraud or scams and unresponsiveness.

This study is unique in its measurement of unsolicited social media sentiment as opposed to most churn-related research that uses survey- or customer-data-based methods. In addition, this study observed the sentiment of customers from all major retail banks across 12 months. To date, no studies on retail bank churn theory have provided such an extensive perspective. The findings contribute to Susan Keaveney’s churn theory and provide a new measurement of switching threat through social media sentiment analysis.