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The learning outcomes are as follows: to benchmark and compare the theoretical models of the performance management and appraisal processes. (Questions 1 and 2) Remembering-in Bloom’s Taxonomy; to understand the importance of practicing fair performance appraisal process. (Question 4) Understanding-in Bloom’s Taxonomy; to analyze the implementation and effectiveness of 180-degree performance appraisal method and rating system prevalent in the IT Sector. (Question 1) Applying and Analyzing-in Bloom’s Taxonomy; to assess the impact of perceptual biases on human behavior and performance (Questions 2 and 3) Evaluating-in Bloom’s Taxonomy.

The case study entitled “Is HR Blind? Why do People Leave Managers Not Companies? A Case of Unfair Performance Appraisal and Biases” is a classic example of a flawed and biased performance appraisal process and perceptual biasness, which resulted in the loss of a valuable and talented resource in a leading Indian IT MNC. The present case had been based upon the real-life experience of an employee (i.e. Rahul Verma), who worked with the company from year 2010 to 2021. It was among the top ten IT MNCs employing about 0.1 million people. The objective of the case was to highlight real time issues existing with HR practices, mainly in IT sector organizations. For example, in the present case, do the HR seek proper justification from the manager before taking a harsh decision like forcibly asking an employee to sign a termination contract without looking at the contributions of his qualitative performance or even performance rating (refer to the transcript) for that matter? Was the job of the HR to only ensure how to fit in employees in the faulty bell curve system? Whether the performance appraisal system being followed at the company is adequately capable of identifying and recognizing the talent. Do the different functions really work cohesively and organically toward achieving the intended goals and objectives of the organization? Was this a failure of the manager in recognizing talent or something went wrong at the employee’s part? Was this a failure of the entire HR system or performance management process at the organization that was unable to filter out the capable and skilled resources out of the crowd? Was this a problem of organizational culture that put on stake its most critical resource – the human capital – by allowing the appraisers to evaluate them just because of the hierarchical structure, and not because they are not being competent enough to perform this most critical job objectively? Who ensures the appraiser is free from any kind of prejudice or bias and is capable of fairly assessing the talent resource? So, the present case was a deliberate attempt to throw out these burning questions to the practitioners and students to ponder upon. Does HR really follow the blind process merely acting on the feedback received from the different units of the organization?

With the help of strong theoretical foundation and practical applications, the following objectives and questions have been framed to deliberate and propose the workable solutions for the benefits of the relevant stakeholders.

HR practitioners, HR managers, supervisors, senior management and HR students, IT heads, project managers.

Teaching notes are available for educators only.

CSS 6: Human Resource Management.

The present research work reviews and maps the thematic evolution of the interface between human resource (HR) practices and service quality (SQ) over the last 33 years.

The authors employed systematic literature review (SLR), bibliometric analysis and visualization to comprehensively map 215 papers extracted from the Web of Science and Scopus databases. The present study also helps to document the research themes that evolved through co-occurrence networks and thematic maps.

The study identifies that HR practices are the central drivers for maintaining SQ in an organization and found that teamwork, empowerment, recruitment, selection, training and reward are key for improving the SQ. It concludes the impact of HR practices on SQ, develops the knowledge structure of human resource management (HRM) operations and SQ literature and organizes it under various dimensions as antecedents and outcomes. As its foremost input, the current study proposes human resource practices and service quality (HRPSQ) framework for comprehensive HR practices and SQ in an organization.

The study is unique as it map the journey of HR practices and SQ and proposes a framework for improved performance.

The purpose of this paper is to supplement and update previously published articles about artificial intelligence (AI) instruments and operations in banking sectors with the following objectives in mind: to understand the role of AI in banking sectors; to explore the themes and context in this area based on keywords, co-citations and co-words; and to identify future research direction by evaluating the trend and direction of previous research.

This study adopts a semi-inductive approach with the convolution of bibliometrics and literature review. This study used bibliometrics for the identification of literature across multiple databases and systematic literature review on identified articles to explore heterogeneous sectors within AI in banking and finance.

This study contributes a literature-based model that accounts for both the broadly in AI application in banking and finance: predictive modeling in risk assessment and detection; financial decision-making; client service delivery; and emerging FinTech applications of AI and machine learning.

This study is among the few to address the literature of tools and application of AI in banking through mixed-methods approach and produce a synthesized model for the same.

Self-Service Technology (SST) is a disruptive technology that has reshaped customer interactions, increased efficiency, and enabled data-driven decision-making. Its impact continues to evolve as technology advances and customer expectations change, making it a key consideration for businesses in a dynamic landscape. This chapter delves into critical findings regarding the adoption and implications of SST in tourism and hospitality. The relevant studies are sourced from the Scopus database. A mixed literature review methodology was employed to review papers. The literature review findings show facets of SST adoption, shedding light on the intricate relationships between consumer readiness variables, context-specific influences, preferred SST features, and psychological attributes. The study reveals consumer preferences, including convenience, ease of use, and speed of service, as primary drivers of the adoption of SST. The bibliometric analysis reveals the scope for developing SST literature in tourism and hospitality. Collaborations among scholars, research and funding institutions could help provide the impetus. Research in SST security, sustainability, and resilience could help enhance the SST literature. Comparative studies evaluating SST's social and economic implications are also suggested.

Researchers, business professionals, and policymakers are now focusing on how disruptive innovations affect markets and business performance in the quickly changing business landscape of today. This study investigates the complexity of disruptive innovations and how they alter established organizational strategies and market structures. The study explores the core traits of disruptive innovations and looks at how they upend established business models and alter the structure of entire industries. This study employs a comprehensive literature review to identify the primary motivators behind disruptive innovations and their diverse impacts on market dynamics. It looks into how disruptive change is sparked by evolving consumer behavior, flexible business models, and emerging technologies. Additionally, research examines how businesses use internal reorganizations and strategic alliances among other tactics to capitalize on and adjust to disruptive innovations. It also examines how market leaders deal with these difficulties in an effort to stay competitive, shedding light on the possible risks and uncertainties related to disruptive innovations. It also highlights how crucial it is for organizations to have innovative cultures and proactive adaptation in order to prosper in a time of swift technological advancements.

Technological start-ups are significant contributor to the innovation and employment provider in an economy. Numerous technological start-ups are established every year; however, only a miniscule percentage of these technological start-ups sustain and scale up in the long run. The aim of this study is to investigate the factors that affect Indian technological start-ups’ competitiveness.

Case study analysis of two technological start-ups (namely, WayCool and Moglix) is undertaken to study the factors affecting the competitiveness of technological start-ups in India. Being a relatively underexplored theme of study in entrepreneurship and strategy, case analysis facilitates exploration and validation of factors influencing competitiveness. Information for case study analysis is drawn from secondary sources of information. The collected data undergoes deductive thematic analysis to systematically identify and examine recurring themes and patterns relevant to the competitiveness of Indian technological start-ups.

Case analysis reveals that innovation intensity, organisational agility and internationalisation influence competitiveness of technological start-ups. The importance of the role of each of these factors for entrepreneurial ventures has been highlighted in literature; however, their effect on competitiveness has not been examined in extant literature.

Being among the few studies on the competitiveness of technological start-ups in specific and start-ups in general, this study highlights the gap in the literature and suggests the need for examining the competitiveness of technological start-ups.

For the practitioners, this study reinforces the need for entrepreneurs to emphasise fundamental factors that build competitiveness. Subsequently, the sources of competitiveness shall enable the start-up to gain a competitive advantage.

This is among the few studies to have explored the competitiveness of technological start-ups in the Indian context.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

In Fiji, Indo-Fijians constitute the second largest community after Indigenous Fijians. Indo-Fijian women face gender and racial inequalities to access sports and Physical Education (PE) in Fijian high schools. To gain a comprehensive view of the sporting realities of these young women, an ethnographic study was carried out with 12 young Indo-Fijian women via participant observations and semi-structured interviews, which were driven by sporting experience-related photographs taken by the participants and participant-voiced free verses. This chapter extracts four young women's experiences from the larger set of data and weaves a one-act play to holistically present their lived sporting experiences. This non-fiction creative piece captures the young women's colloquial words and artistic writings, thus creating a space where the reader can hear their voices and feel their sporting experiences too. We employ intersectional lenses together with Critical Race Theory (CRT) to look at the social factors that influence their sporting lives. The findings reveal that factors such as traditional gender norms, racism, age, classism and internal migration from rural co-ed to urban co-ed schools intersect at various levels to determine the segregation of Indo-Fijian girls from ‘mixed racial’ (team) sports. Moreover, in high school PE lessons, iTaukei (Indigenous Fijian) interests are maintained in sports where PE lessons are focused on dominant sports (rugby, soccer and netball) with emphasis on competition rather than inclusive participation for all. Therefore, this paper supports the opening of pathways for Indo-Fijian girls and women to participate in sports so that they can exercise their rights as Fijian citizens.

This study aims to propose a novel deep learning (DL)-based framework, iRelevancy, for identifying the disaster relevancy of a social media (SM) message.

It is worth mentioning that a fusion-based DL model is introduced to objectively identify the relevancy of a SM message to the disaster. The proposed system is evaluated with cyclone Fani data and compared with state-of-the-art DL models and the recent relevant studies. The performance of the experiments is assessed by the accuracy, precision, recall, f1-score, area under receiver operating curve and precision–recall curve score.

The iRelevancy leads to a better performance in accuracy, precision, recall, F-score, the area under receiver operating characteristic and area under precision-recall curve, compared to other state-of-the-art methods in the literature.

The predictive performance of the proposed model is illustrated with experimental results on cyclone Fani data, along with misclassifications. Further, to analyze the performance of the iRelevancy, the results on other cyclonic disasters, i.e. cyclone Titli, cyclone Amphan and cyclone Nisarga are presented. In addition, the framework is implemented on catastrophic events of different natures, i.e. COVID-19. The research study can assist disaster managers in effectively maneuvering disasters during distress.