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The purpose of this paper is to investigate the moderating role of cultural factors (concern for face and stability of attribution) in the relationships among service recovery quality, postrecovery satisfaction and repurchase intention. Based on the politeness theory, this paper proposes a theoretical model for understanding how concern for face and stability of attribution may affect collectivists’ consumption behavior.

Data were collected in a field survey of 600 Hong Kong consumers who had experienced a telecommunications service failure. Partial least squares structural equation modeling (PLS-SEM) was used to test the theoretical hypotheses.

A cultural factor of concern for face is found to negatively moderate the relationship between service recovery quality and postrecovery satisfaction. Face also positively influences the relationship between postrecovery satisfaction and repurchase intention. Another cultural factor, stability of attribution, is found to negatively moderate the relationship between service recovery quality and postrecovery satisfaction and to negatively moderate the relationship between postrecovery satisfaction and repurchase intention.

This study contributes to the understanding of the relevance of concern for face and stability of attribution in collectivists’ consumption behavior. The findings have significant implications for managers in a position to exploit the cultural value mechanisms of collectivist consumers.

To the best of the authors’ knowledge, this has been the first research to examine the impact of concern for face and stability of attribution among service recovery quality, postrecovery satisfaction and repurchase intention.

The purpose of this study is to provide in-depth knowledge on customer-facing technology and customer experience in the grocery retail sector, including how the value-added potential of customer-facing technologies may be enhanced.

The analysis is based on 30 in-depth interviews with “early adopters” of customer-facing digital retail technologies in the Norwegian grocery sector. Theoretically, the study draws on notions of the customer journey and customer experience.

The study contributes to deepening insights concerning how digital retail technology is used and may be geared to further increase value for customers, specifically how retailers may use data on customers and products to personalize digital retail technology offerings and gain a competitive advantage. The findings underline how customer value is context-dependent and show that while grocery retail customers primarily emphasize utilitarian benefits related to customer-facing technologies, hedonic benefits are valuable biproducts. Moreover, the study showcases how personalization is key in addressing customers’ needs and wants, and may serve to increase the overall value of customer-facing technologies for customers and retailers.

The study’s sector-specific focus on technology in use contributes to enhance knowledge on how digital retail technologies can be leveraged to the benefit of customers and retailers, including customers’ sector-specific needs and wants.

This study examines learner readiness and satisfaction with blended learning systems in India’s post-vaccine classrooms, focusing on the relationship between face-to-face (F2F), online learning (OL), and blended learning (BL) indicators and identifying which predictors within these systems most significantly affect learners’ satisfaction (LS).

An online survey was conducted with 451 students from both public and private universities in India. The data were analyzed using factor analysis and confirmatory factor analysis, followed by multiple regression to test the hypotheses.

The findings reveal a significant positive correlation between learners’ satisfaction and online learning and blended learning, with β values of 28.3 and 27.2, bearing a p-value of 0.000. In contrast, face-to-face (F2F) learning was insignificant, with a β value of 0.070 and a p-value of 0.119. These insights underscore the effectiveness of online and blended learning formats in enhancing learner satisfaction in higher education while also suggesting a re-evaluation of the role of traditional F2F learning methods. The research supports the integration of online learning in higher education due to its balanced mix of teacher-led and student-centered instruction, alongside the practical benefits of reduced travel costs and access to independent study resources.

This study provides insights into student perceptions and attitudes towards blended learning in India’s post-vaccine classrooms. It highlights the importance of tailoring blended learning strategies to meet colleges’ and universities’ diverse learning needs and goals in this evolving context. The findings serve as a valuable resource for educators and administrators, aiding in designing effective blended learning frameworks suited explicitly for higher education in India.

While there is extensive literature on F2F, OL, and BL, limited research compares these learning approaches and their impact on learner satisfaction within the Indian higher education context. This study fills this gap by providing valuable insights for shaping educational strategies in India’s post-vaccine classrooms.

In this study composite and sandwich beams with homogeneous core and homogeneous or Functional Graded Materials (FGM) faces under three point bending have been confronted. The purpose of this paper is to study numerically sandwich beams with homogeneous core and homogeneous or FGM faces under three point bending and to compare the results for the stress and displacement fields with those resulted of coating – substrate and homogeneous beams. Considering a crack in the lower face sheet to study the influence of the material gradation on the stress intensity factors.

At first a static finite element analysis is performed throughout the composite and sandwich beams, which is taking into account the graded character or not of the faces. For this reason five plane models are considered in order to have a comparable study for the stress and displacement fields of composite beams, which are subjected to three point bending. Second a crack in the lower face is considered parallel to the axis of gradation. When subjected to three point bending, this crack will propagate slowly perpendicular to the lower face.

Computed distributions of the stress fields across the core material and near the interfaces are given for different materials gradation of the faces; and possible crack-initiation positions have been identified. Stress intensity factors are calculated using finite element method, and assuming linear fracture mechanics and plane strain conditions.

The originality of the proposed analysis is to investigate for the first time numerically the influence of the FGMs or homogeneous faces in the core material of sandwich beams under three point bending relative to the coating – substrate and to the homogeneous beams. Second to study the influence of a crack in the lower graded face sheet on the overall behavior of the composite beam and to investigate the influence of the material gradation on the values of stress intensity factors.

The purpose of this paper is to incorporate a multimodal biometric system, which plays a major role in improving the accuracy and reducing FAR and FRR performance metrics. Biometrics plays a major role in several areas including military applications because of robustness of the system. Speech and face data are considered as key elements that are commonly used for multimodal biometric applications, as they are simultaneously acquired from camera and microphone.

In this proposed work, Viola‒Jones algorithm is used for face detection, and Local Binary Pattern consists of texture operators that perform thresholding operation to extract the features of face. Mel-frequency cepstral coefficients exploit the performances of voice data, and median filter is used for removing noise. KNN classifier is used for fusion of both face and voice. The proposed method produces better results in noisy environment with better accuracy. In this proposed method, from the database, 120 face and voice samples are trained and tested with simulation results using MATLAB tool that improves performance in better recognition and accuracy.

The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent.

The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent.

While some modern organisations have established “virtual work teams”, which are said to be comprised of people who are geographically separated and who work across boundaries of space and time using computer driven communication technologies, it is also true that many organisations remain structured around conventional face‐to‐face teams. Increasingly, the conventional face‐to‐face team is endeavouring to increase its productivity by utilising some of the technology and characteristics of the virtual team. In fact, it may not be practical any longer to draw a distinction between conventional face‐to‐face teams and virtual teams, due to the invasive nature of technology throughout most modern organisations.

Examines the effectiveness of teaching business cases using collaborative groups of students. A quasi‐experiment was conducted in which a class of students was randomly placed in face‐to‐face groups and given a case with questions to answer collectively. Then for a different case, these same students were randomly assigned to electronic case groups. The electronic groups conducted all their group interactions with their fellow group members in universities in the USA, Canada and Mexico via e‐mail over the Internet. The groups rated both their group experiences, face‐to‐face and electronic. Also each student completed the Myers Briggs personality typing instrument. The four dimensions of personality type were then correlated with the case ratings; and significance tests across the two subgroups, electronic and face‐to‐face, were assessed. While there were few significant differences across the electronic and face‐to‐face groups, the extroverts found the electronic groups to be a novel experience. This may be explained by the technical frustrations encountered by the students and the lack of richness of the electronic group exchange.

Many individuals who have no or limited experience of using electronic communication technologies (ECTs), e.g. telephone, fax, voice, and e‐mail are concerned that media like e‐mail are a limited, if not poor, way of communicating, and that they cannot replace face‐to‐face communication. Reports on research examining how relatively sophisticated ECT users use ECTs to communicate, and how electronic communication may affect face‐to‐face communication. Finds that ECTs are most effective in the administrative stages of a project rather than in the initial conceptualizing stages or final project wrap up and is the primary form of communication between co‐workers. Discusses the implications for workplace strategies that involve remote work.

Aims to develop an efficient and robust system for real‐time personal identification by automatic face recognition.

A wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme are introduced to improve the computation efficiency in the face detection task. In addition, a Gabor‐based low feature dimensional pattern is proposed to deal with the face recognition problem.

The proposal of a wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme is effective to improve the computation efficiency in the face detection task. The introduction of a low feature dimensional pattern is powerful to cope with the transformed appearance‐based face recognition problem. In addition, the use of aggregated Gabor filter responses to represent face images provides a better solution to face feature extraction.

Provides guidance in the design of automatic face recognition system for real‐time personal identification.

Biometrics recognition has been emerging as a new and effective identification technology that attains certain level of maturity. Among many body characteristics that have been used, face is one of the most commonly used characteristics and has drawn considerably large attentions. An automated system to confirm an individual's identity employing features of face is very attractive in many specialized fields.

Introduces a wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme to improve the computation efficiency in the face detection task. Introduces a Gabor‐based low feature dimensional pattern to deal with the face recognition problem.

Handling feature interaction is an unsolved issue in feature recognition approach. This paper presents a method for recognizing the presence of feature interactions. First, based on the convex hull concept, a so‐called reference face is defined. Second, by adding the reference face into the attributes adjacency graph (AAG), a modified AAG is obtained. Two general feature types, namely depression and protrusion features, are identified by the reference face. The basic features such as slots, pockets and bosses are represented by the modified AAG. Any features that remain unrecognized by the modified AAG are regarded as interacting features. The types of reference faces and feature face are also classified. Based on the kind of face classification, the interacting features are finally recognized via a process of virtual face extension and volume addition.