Purpose Given the competitive landscape in the higher education setting, it is important that universities adopt strategies that create competitive advantage for them. Universities must leverage their resources efficiently to address this goal. Creating a positive brand image is one such strategy. The purpose of this paper is to conceptualize university brand image as its heritage, service quality and trustworthiness and investigate their relationship with student’s satisfaction. It also investigates the role of university reputation as a mediating variable. Design/methodology/approach Data were collected through a mixed method approach. The first stage involved qualitative interviews and focused group discussions with students to understand the factors responsible for student satisfaction with their respective universities. The second stage involved administering a survey questionnaire in two geographies – the USA and India to investigate the hypothesized relationship. The authors use regression analyses to test these relationships. Findings Findings indicate that a distinct brand image plays an important role in students’ level of satisfaction across both the USA and India. Service quality has a greater impact on student satisfaction levels across both contexts (as compared to university heritage and trustworthiness). The authors also find a positive mediating effect of university reputation in the relationship between university brand image and student satisfaction levels. Originality/value The current research contributes to the services marketing literature in the university context. It offers a framework for decision making in universities. It suggests that universities must work toward developing their brand image by focusing on its three dimensions – heritage, trustworthiness and service quality.

Insurance telematics is a recent technology-enabled service innovation advanced by insurance companies and adopted by millions of drivers worldwide. This research study explores the insurance telematics technology acceptance and use among the new Millennials generation, which represents both a challenge and an opportunity for insurers. Drawing on the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB), the study uses data from 138 Millennials in the USA to delve into their perceived attitudinal behavior and intention to use insurance telematics. The findings provide empirical confirmation of the integrative and predictive power of the proposed combined theoretical framework (TAM-TPB) to explain insurance telematics adoption and use. The results also suggest a sophistication-level shift in Millennials preferences from functionality evaluation to applicability value sought through the adoption and use. And the findings ascertain the role of perceived enjoyment, trust, and social media as critical factors influencing Millennials attitudinal behavior and intention to use insurance telematics. Considering these results, the authors further discuss implications for scholars and practitioners, and suggest future research directions.

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PurposeStudies on mining text and generating intelligence on human resource documents are rare. This research aims to use artificial intelligence and machine learning techniques to facilitate the employee selection process through latent semantic analysis (LSA), bidirectional encoder representations from transformers (BERT) and support vector machines (SVM). The research also compares the performance of different machine learning, text vectorization and sampling approaches on the human resource (HR) resume data.Design/methodology/approachLSA and BERT are used to discover and understand the hidden patterns from a textual resume dataset, and SVM is applied to build the screening model and improve performance.FindingsBased on the results of this study, LSA and BERT are proved useful in retrieving critical topics, and SVM can optimize the prediction model performance with the help of cross-validation and variable selection strategies.Research limitations/implicationsThe technique and its empirical conclusions provide a practical, theoretical basis and reference for HR research.Practical implicationsThe novel methods proposed in the study can assist HR practitioners in designing and improving their existing recruitment process. The topic detection techniques used in the study provide HR practitioners insights to identify the skill set of a particular recruiting position.Originality/valueTo the best of the authors’ knowledge, this research is the first study that uses LSA, BERT, SVM and other machine learning models in human resource management and resume classification. Compared with the existing machine learning-based resume screening system, the proposed system can provide more interpretable insights for HR professionals to understand the recommendation results through the topics extracted from the resumes. The findings of this study can also help organizations to find a better and effective approach for resume screening and evaluation.