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In an empirical examination of the link between product involvement and brand loyalty, a convenience sample of 253 students were asked to complete a questionnaire relating to two products which had been found in preliminary qualitative research to be associated with contrasted levels of involvement. The factor structure of involvement was found to vary between the two product categories (sneakers and pens). Furthermore, the link between product involvement and brand loyalty was found to involve different aspects of product involvement for each of the products concerned. Hence, future researchers in the area should be mindful that product involvement and brand loyalty are not universal constructs: they should be examined within specific consumer and product parameters.

Gives an in depth view of the strategies pursued by the world’s leading chief executive officers in an attempt to provide guidance to new chief executives of today. Considers the marketing strategies employed, together with the organizational structures used and looks at the universal concepts that can be applied to any product. Uses anecdotal evidence to formulate a number of theories which can be used to compare your company with the best in the world. Presents initial survival strategies and then looks at ways companies can broaden their boundaries through manipulation and choice. Covers a huge variety of case studies and examples together with a substantial question and answer section.

The rapid growth of technology, including artificial intelligence (AI), in the banking industry has played a disrupting role in traditional banking channels. This study aims to investigate factors that influence the attitudes and perceptions of digital natives pertaining to mobile banking and comfort interacting with AI-enabled mobile banking activities.

Data were collected from 218 digital natives. This paper uses multivariate regression and two separate multiple regression analyses to examine the differential effects of technology-based (i.e. attitudes toward AI, relative advantage, perceived trust and security in specific mobile banking activities) and non-technology based (i.e. need for service, quality of service) factors on mobile banking usage and AI-enabled mobile banking services.

This study identifies determining factors for mobile banking and AI-enabled mobile banking services. Results indicate a divide in how digital natives perceive relative advantage between our two dependent variables. Consistent with previous studies, the relative advantage construct has the most impact on mobile banking usage. However, relative advantage was not significant for AI-enabled mobile banking, suggesting an extra layer of complexity that goes beyond convenient fast banking.

A limitation of this study is that it does not incorporate age groups outside of digital natives. Further research is needed to test for differential effects between age groups. In addition, the discovery of no significant impact of relative advantage on AI mobile banking warrants more research on the similarities and differences between mobile banking and AI-enabled mobile banking.

To better appeal to digital natives, it is suggested that the banking industry emphasize mobile banking’s anywhere/anytime access to financial accounts, as this is important to college-age customers who may not live near their local banking institution. Moreover, the paper suggests that improvement to mobile banking features for one-on-one interpersonal contact with bank employees is needed.

This study addresses the gap in the understanding of how digital natives perceive mobile banking in comparison to AI-enabled mobile banking services.

Undertakes a systems study, in the language of mathematics, of the concepts of “whole” and “part”. The concepts of linked systems, product systems, Cartesian product systems and inverse limit systems are used to study “wholeness”, and their factor systems are used and considered as “parts”. Discusses comparisons between models of “whole” and between “parts” and “whole”. Presents the application background and poses some open questions.

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.

Phobias and panic disorder are commonly treated within primary care settings. Cognitive behavioural therapy (CBT) is a recommended treatment for these disorders but access is limited due to too few therapists, expense and patients' reluctance to enter therapy. Computerised CBT (CCBT) is a self‐help option designed to offer patients the potential benefits of CBT with less therapist involvement. The review described here sought to identify studies evaluating the effectiveness of CCBT for phobias and panic disorders.

The end-effects is a well-recognized phenomenon occurring in the linear induction motor (LIM) which makes the analysis and control of the LIM with good performance very difficult and can cause additional significant non-linearities in the model. So, the compensation of parameters uncertainties due to these effects in the control system is very necessary to get a robust speed control. The purpose of this paper is to propose a new technique of LIM end-effects estimation using the inverse rotor time constant tuning in order to compensate the flux orientation error in the indirect field-oriented control (IFOC) control law.

First, the dynamic model of the LIM taking into consideration the end-effects based on Duncan model is derived. Then, the IFOC for LIM speed control with end-effects compensation is derived. Finally, a new technique of LIM end-effects estimation is proposed based on the model reference adaptive system (MRAS) theory using the instantaneous active power and the estimated stator currents vector. These estimated currents are obtained through the solution of LIM state equations.

Simulations were carried out in MATLAB/SIMULINK to demonstrate the effectiveness and robustness of LIM speed control with the proposed MRAS inverse rotor time constant tuning to estimate end-effects value. The numerical validation results show that the proposed scheme permits the drive to achieve good dynamic performance, satisfactory for the estimated end-effects of the LIM model and robustness to uncertainties.

The end-effects causes a drop in the magnetizing, primary and the secondary inductance, requiring a more complex LIM control scheme. This paper presents a new approach of LIM end-effect estimation based on the online adaptation and tuning of the LIM inductances. The proposed scheme use the inverse rotor time constant tuning for end-effects correction in LIM vector control block.

Engagement, motivation, and persistence are usually associated with positive outcomes. However, too much of it can overtax our psychophysiological system and put it at risk. On the basis of a neuro-dynamic personality and self-regulation model, we explain the neurobehavioral mechanisms presumably underlying engagement and how engagement, when overtaxing the individual, becomes automatically inhibited for reasons of protection. We explain how different intensities and patterns of engagement may relate to personality traits such as Self-directedness, Conscientiousness, Drive for Reward, and Absorption, which we conceive of as functions or strategies of adaptive neurobehavioral systems. We describe how protective inhibitions and personality traits contribute to phenomena such as disengagement and increased effort-sense in chronic fatigue conditions, which often affect professions involving high socio-emotional interactions. By doing so we adduce evidence on hemispheric asymmetry of motivation, neuromodulation by dopamine, self-determination, task engagement, and physiological disengagement. Not least, we discuss educational implications of our model.

Prior literature has found that as uncertainty in a firms information environment increases, optimism increases in equity analysts’ earnings forecasts. The studies suggest an economic incentive explanation, commonly called the management‐relations hypothesis. However, there is conflicting evidence that managers would prefer pessimistic forecasts and encourage analysts to “walk‐down” their forecasts to prevent negative earnings surprises. To test these contradictory findings, this study uses an experimental setting to remove economic incentives from the analyst’s decision process and isolate the cause of observed bias in analysts’ reports. The results of the experiment show that an increase in the perceived uncertainty of the forecasting task results in significantly lower relative optimism in analysts’ earnings forecasts. This finding is consistent with a negativity hypothesis and the managementrelations hypothesis extolled in the empirical research. The findings also show that relative forecast optimism bias is positively related to the level of analysts’ buy/sell recommendations consistent with more recent findings that suggest that analysts use motivated reasoning (the tendency to process information in a manner that supports one’s goal) in their judgments of forecasted earnings and recommendations. Together, these results suggest that analysts consider and use financial information differently depending on their decision goal.

Technology has always inspired social change, but its scale and complexity have begun to bewilder even the politicians and policymakers. Several recent national foresight studies point to a need for socio‐organizational or “soft” technologies to help Europe manage change and respond to major new economic opportunities. Research is required in fields such as neuro‐linguistic programming, the psychology of knowledge management and the ergonomics of the man‐machine interface. “Electronic pets” showed that we can learn to love machines – now the challenge is to embed technology in such a way as to marry science with society.