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The case study will help to learn about the importance of pre-sanction precautionary measures before lending to self-help groups (SHGs), to learn about the potential lapses and errors while sanctioning SHG finance and to learn about the importance of bank’s guidelines and compliance before sanctioning loans.

This case study details the tenure of Seema in a rural branch of Safe Bank of India located in Haryana which she joined as a manager in the year 2016. She overachieved the target given by the district collector office, and going by the tide, she kept her reliance on the references provided by non-government organization (NGO) without complying the bank’s instructions. She committed errors while sanctioning the loans, which led towards the upsurge of non-performing assets of the branch. Later on, after investigation it was discovered that she did not follow fundamental bank’s instructions. In wake of those lapses and errors, how she could have avoided those lapses and secure the public money? What were the most important documents while granting agriculture finance and what due diligence she should have taken? How did she treat calls from the government departments? Was she right in trusting the suggestions of the NGO?

This case study caters to students of various streams, namely, management, business administration and law, and can be targeted at both undergraduate and postgraduate students. It could be suitable for several types of courses and students. Furthermore, this case study can also be targeted for various training programmes for bank employees and employees of various lending institutions engaged in agriculture finance and credit linkage programmes.

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CSS 1: Accounting and finance.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

Following a contingency approach, this paper aims to understand when service automation can enhance or destroy value for customers in the frontline by (1) providing a comprehensive overview of factors that influence the value co-creation/co-destruction potential of service automation and (2) zooming in on the combination of service contexts and service tasks to develop research propositions.

This paper uses a grounded theory approach based on qualitative data from multiple methods (i.e. a diary study with follow-up interviews, a consultation of academic experts and a storyboard study) as well as a systematic literature review to develop (1) a Framework of Automated Service Interactions (FASI) and (2) a contingency model for service tasks/contexts.

This paper presents a framework which gives an overview of factors influencing the value co-creation/co-destruction potential of service automation. The framework discerns between three types of factors: service design (i.e. controllable and manageable by the organization), static contingency (i.e. uncontrollable and fixed) and dynamic contingency (i.e. uncontrollable and flexible). Furthermore, the paper presents a contingency model based on the combination of service contexts and service tasks which results in seven research propositions.

This paper brings structure in the fragmented field of service automation. It integrates and summarizes insights regarding service automation and sheds more light on when service automation has the potential to create or destroy value in the organizational frontline.

The lack of conceptual approaches for organizing and expressing capabilities, usage and impact of intelligent machines (IMs) in work settings is an obstacle to moving beyond isolated case examples, domain-specific studies, 2 × 2 frameworks and expert opinion in discussions of IMs and work. This paper's purpose is to illuminate many issues that often are not addressed directly in research, practice or punditry related to IMs. It pursues that purpose by presenting an integrated approach for identifying and organizing important aspects of analysis and evaluation related to IMs in work settings. 

This paper integrates previously published ideas related to work systems (WSs), smart devices and systems, facets of work, roles and responsibilities of information systems, interactions between people and machines and a range of criteria for evaluating system performance.

Eight principles outline a straightforward and flexible approach for analyzing and evaluating IMs and the WSs that use them. Those principles are based on the above ideas.

This paper provides a novel approach for identifying design choices for situated use of IMs. The breadth, depth and integration of this approach address a gap in existing literature, which rarely aspires to this paper’s thoroughness in combining ideas that support the description, analysis, design and evaluation of situated uses of IMs.

Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders.

A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index.

The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”.

The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

Increasing demand on rail transport speeds up the introduction of new technical systems to optimize the rail traffic and increase competitiveness. Remote control of trains is seen as a potential layer of resilience in railway operations. It allows for operating and controlling automated trains and communicating and coordinating with other stakeholders of the railway system. This paper aims to present the first results of a multi-phased simulator study on the development and optimization of remote train driving concepts from the operators’ point of view.

The presented concept was developed by benchmarking good practices. Two phases of iterative user tests were conducted to evaluate the user experience and preferences of the developed human-machine-interface concept. Basic training requirements were identified and evaluated.

Results indicate positive feedback on the overall system as a fallback solution. HMI elicited positive emotions regarding pleasure and dominance, but low arousal levels. Train drivers had more conservative views on the system compared to signalers and students. The training activities achieved increased awareness and understanding of the system for future operators. Inclusion of potential users in the development of future systems has the potential to improve user acceptance. The iterative user experiments were useful in obtaining some of the needs and preferences of different user groups.

Multi-phase user tests were conducted to identify and to evaluate the requirements and preferences of remote operators using a simplified HMI. Training analysis provides important aspects to consider for the training of future users.

The purpose of this study is to identify the research fronts by analysing highly cited core papers adjusted with the age of a paper in library and information science (LIS) where natural language processing (NLP) is being applied significantly.

By excavating international databases, 3,087 core papers that received at least 5% of the total citations have been identified. By calculating the average mean years of these core papers, and total citations received, a CPT (citation/publication/time) value was calculated in all 20 fronts to understand how a front is relatively receiving greater attention among peers within a course of time. One theme article has been finally identified from each of these 20 fronts.

Bidirectional encoder representations from transformers with CPT value 1.608 followed by sentiment analysis with CPT 1.292 received highest attention in NLP research. Columbia University New York, in terms of University, Journal of the American Medical Informatics Association, in terms of journals, USA followed by People Republic of China, in terms of country and Xu, H., University of Texas, in terms of author are the top in these fronts. It is identified that the NLP applications boost the performance of digital libraries and automated library systems in the digital environment.

Any research fronts that are identified in the findings of this paper may be used as a base for researchers who intended to perform extensive research on NLP.

To the best of the authors’ knowledge, the methodology adopted in this paper is the first of its kind where meta-analysis approach has been used for understanding the research fronts in sub field like NLP for a broad domain like LIS.

Since the start of the COVID-19 pandemic, one dark social-media phenomenon in particular has experienced a significant rise: consumer-to-consumer (C2C) conflicts, i.e. consumers who verbally attack each other in response to COVID-19 service failures. The aim of this paper is to uncover the sources of such conflicts and to gain an insight into the corresponding conflict moderation strategies that international brands adopt.

The methodology consists of non-participatory netnographic observations of 13 national, international, and global online brand communities (OBCs) on Facebook. The authors use purposeful sampling to collect relevant data on conflict sources and brand moderation strategies during COVID-19 service failures and a hybrid approach to thematic analysis to derive distinct themes from these data.

The paper identifies five C2C conflict sources: brand attack, brand dissatisfaction, brand skepticism, brand contention and brand defense; these are then classified as having either an individualistic (self-oriented) or collectivistic (other-oriented) orientation. The authors also uncover several moderation strategies: non-engaging, automated, bolstering, asserting (direct, indirect) and informing (factual, empathetic, apologetic), which are broadly categorized into two levels based on their passive vs active approach and authoritative vs cooperative orientation. The paper further highlights that brands adapt their moderation strategies to specific sources of C2C conflicts, thereby producing a range of OBC outcomes.

The study's empirically informed framework comprising sources of undesirable conflicts and brand moderation strategies offers a practical tool that can aid marketing managers in nurturing civil C2C engagement and interactive behaviors in their OBCs. By adopting our framework, brand and marketing practitioners can tailor their communication strategies toward different sources of C2C conflict and minimize their adverse consequences, thus, fostering an overall constructive OBC engagement.

The authors offer a novel framework to international marketing research, consisting of C2C conflict sources and corresponding moderation strategies that take place in response to service failures during the COVID-19 pandemic. These insights, in turn, inform international marketers about new ways of transforming the dark side of OBCs into a source of competitive advantage based on real-world brand practice.

Construction 4.0 technology is a novel innovative technology that has been proved to enhance project performance. However, information on the concept's awareness, adoption readiness and challenges in developing economies is still scanty. The purpose of the study is to appraise awareness, adoption readiness and challenges of Construction 4.0 technologies in Nigeria to bring to the fore the state of art of these innovative technologies in the study area.

The study used a convenient sampling technique to select 129 construction professionals (architects, engineers and quantity surveyors) in Osun State, Nigeria, who provided data for the study through a closed-ended structure questionnaire survey. The quantitative data supplied were analysed using frequency, percentile, Cronbach's alpha, mean score (MS) analysis and analysis of variance (ANOVA).

The overall awareness level of construction professionals in the study area about Construction 4.0 technologies is at a moderate level (MS = 3.03). The analysis of each component of the Construction 4.0 technologies shows that BIM (MS = 3.69) has the highest level of awareness, while augmented reality (MS = 2.51) has the least awareness level. More results show a significant difference in the opinion of the respondents, a significant difference in the respondents on 36% of the components of Construction 4.0 technologies. The adoption readiness of the Nigerian construction industry (NCI) to Construction 4.0 technologies is at an initial level (MS = 2.86). However, the 3D printing (MS = 3.36) and augmented reality (MS = 2.49) have the highest and lowest adoption readiness ratings, respectively. There is no significant difference in how respondents ranked the NCI adoption readiness on 73% of the components of Construction 4.0 technologies. The main challenges of Construction 4.0 technologies in the study area are lack of standardisation (MS = 4.02), lack of investment in research and development and cost of implementation (MS = 3.87) each. The result shows that there is perfect unanimity in the way respondents ranked the challenges of Construction 4.0.

The study provided information on the status quo of Construction 4.0 technologies in the NCI to enhance improvement in practice and the attendant project delivery.

The study attempted to bring to the fore the state of the art on awareness, adoption readiness and challenges of Construction 4.0 technologies in Nigeria. The study's information will be valuable to improve project delivery.