National Institute of Construction Management and Research

Purpose The purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization. Design/methodology/approach The authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM. Findings The statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses ( H1 - H3 ) and the authors did not find support for H4 . Although the authors did not find support for H4 (moderating role of supply base complexity (SBC)), however, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies. Originality/value This study makes some original contribution to the operations and supply chain management literature. The authors provide theory-driven and empirically proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results.

Purpose The existing literature suggests that employee well-being is an important concern for organizations. The purpose of this paper is to carry out an empirical examination to assess whether employee experience of workplace spirituality has positive relationships with multiple forms of employee well-being. Design/methodology/approach This paper focussed on four forms of employee well-being, namely: emotional well-being, psychological well-being, social well-being, and spiritual well-being. It specified and empirically tested, using a survey design, four hypotheses, each proposing a positive relationship between workplace spirituality and one of the four forms of employee well-being. Findings All four hypotheses were supported indicating that workplace spirituality has a positive relationship with emotional, psychological, social, and spiritual well-being. Research limitations/implications This paper may encourage future research to assess whether various forms of employee well-being result from specific dimensions of workplace spirituality. Practical implications Organizations may implement workplace spirituality for simultaneously enhancing multiple forms of employee well-being. Social implications As employee well-being is a matter of social concern, the findings of this study indicating a positive association between workplace spirituality and employee well-being have a social relevance. Originality/value To the author’s knowledge, this is the first study to examine the relationship between workplace spirituality and four forms of employee well-being, namely; emotional, psychological, social, and spiritual well-being. As employee well-being is an important concern for organizations, the contribution of the study findings is that workplace spirituality implementation can simultaneously enhance multiple forms of employee well-being.

This paper presents an empirical study of green supply chain management (GSCM) practices in the Micro, small and medium enterprises (MSMEs) in India. Although the research in the area of GSCM has grown in recent times, the literature has yet to furnish an accepted explanation for why green practices are to be manifested in supply chain management given external and internal pressures. These MSMEs have been involved in such green supply chain practices only to the extent of their participation as suppliers, distributors and in other capacities as business partners. This study confirms and validates that Indian MSMEs face significant pressures from external stakeholders to adopt GSCM practices. Among internal pressures, on-the-job training forces MSMEs in India to adopt GSCM practices. It has been also established that external pressures and adoption of GSCM are fully mediated by internal pressures.

Cloud computing uses the internet instead of discs or memory. Computing services include servers, databases, networks, and programmes. The primary benefit of cloud computing is easy and cheap data backup and access from anywhere. Cloud storage doesn't store consumer data, raising safety concerns. Cloud backup and storage users may not know how data is transported. The user is unaware a third party is secretly accessing their data. For safety, we offer numerous encryption algorithms. This book chapter covered cryptography and cloud computing.

Purpose The purpose of this paper is to investigate the adoption of Internet of Things (IoT) in the agriculture industry by the farmers' in India using the theoretical lens of the behavioral reasoning theory (BRT). Design/methodology/approach A survey on farmers was conducted to examine the adoption of IoT in agriculture industry (IoT-A) using BRT. The data analysis of the primary survey was done by applying the structural equation modelling (SEM) technique. Findings The ‘reasons for’ adoption of IoT-A were as follows: Relative advantage, social influence, perceived convenience, and perceived usefulness. The ‘reasons against’ adoption were as follows: Image barrier, technological anxiety, perceived price and perceived risk. The BRT theory provides the platform to discuss the psychological processing of acceptance of IoT in agriculture industry by the farmers. Practical implications This research has unique implications as it studies the rural consumers’ behavior of innovation adoption namely IoT in agriculture. It provides the specific reasons ‘for’ and ‘against’ IoT adoption in agriculture, which will give directions to the marketers of IoT technology to develop suitable marketing strategies to improve the adoption in rural areas. Originality/value This research takes the first step in the direction toward deliberation of the adoption of IoT-A by farmers in an emerging Indian economy using the BRT theory, which discusses the ‘reasons for’ and ‘reasons against’ adoption in a proposed model.

Radio frequency identification technology (RFID) and time series forecasts are used to create a dependable IoT-based traffic system. The system regulates city traffic. The suggested method estimates junction traffic volume over time using LSTM neural networks. RFID technology improves data collection accuracy and reliability. Data preparation includes outlier identification to remove anomalies. Training the LSTM model on preprocessed data reveals traffic volume trends. The trained model predicts traffic volume using historical data. Prediction performance is quantified by MAE, MAPE, and R2. The proposed approach is tested using four intersection traffic data. Results indicate that LSTM-based traffic volume estimation works. The optimal design is determined by evaluating system performance for 12-to-168-time steps. The experimental findings suggest that the proposed method can accurately anticipate traffic volume, helping traffic managers enhance flow. RFID and time series projections bolster traffic system reliability.

Artificial intelligence and machine learning applications in image processing are examined in this chapter. It covers AI methods including supervised, unsupervised, reinforcement, and deep learning. Genetic algorithms, rule-based systems, expert systems, and fuzzy logic are AI methods. SVM, decision trees, random forests, K-means clustering, and PCA are machine learning methods. CNN, RNN, and GANs are utilised for object recognition, classification, and segmentation. The chapter discusses how artificial intelligence and machine learning affect accuracy, efficiency, and decision-making. The need to choose proper measurements and procedures for assessment and performance analysis is also stressed. Ethics like justice, privacy, transparency, and human-AI cooperation are covered in the chapter.

Purpose This paper aims at developing an early warning signal model for predicting corporate default in emerging market economy like India. At the same time, it also aims to present methods for directly estimating corporate probability of default (PD) using financial as well as non‐financial variables. Design/methodology/approach Multiple Discriminate Analysis (MAD) is used for developing Z ‐score models for predicting corporate bond default in India. Logistic regression model is employed to directly estimate the probability of default. Findings The new Z ‐score model developed in this paper depicted not only a high classification power on the estimated sample, but also exhibited a high predictive power in terms of its ability to detect bad firms in the holdout sample. The model clearly outperforms the other two contesting models comprising of Altman's original and emerging market set of ratios respectively in the Indian context. In the logit analysis, the empirical results reveal that inclusion of financial and non‐financial parameters would be useful in more accurately describing default risk. Originality/value Using the new Z‐ score model of this paper, banks, as well as investors in emerging market like India can get early warning signals about the firm's solvency status and might reassess the magnitude of the default premium they require on low‐grade securities. The default probability estimate (PD) from the logistic analysis would help banks for estimation of credit risk capital (CRC) and setting corporate pricing on a risk adjusted return basis.

Supply chain management is essential to a company's success in today's fiercely competitive business environment, regardless of the industry. Businesses are increasingly turning to big data analytics as a useful tool for supply chain improvement to meet the expanding needs of customers, save operational costs, and boost overall efficiency. In order to improve critical areas including demand forecasting, inventory management, logistics optimization, and risk reduction, research has been conducted on the collecting and use of big data in supply chain management. Big data analytics and supply chain management integration can result in more cost-effective operations, better customer service, and increased resilience to disturbances. Organisations that effectively use big data will gain a competitive advantage in the constantly changing supply chain landscape as technology and data capabilities continue to grow.

Remote areas benefit from lower costs for transporting enormous amounts of data between a source node and multiple networking devices. Noise and constraints necessitated a 64 kb/s broadcaster. Bit defect rates, abnormalities, and frequent retransmission prevented data movement.This could greatly minimize channel usage. The maximum transmission unit distance was 64B. Data packet size limits exacerbated issues. Massive data traffic, connection imprecision, and changing topology affect network structure, making data dissemination from source nodes to all devices difficult.This book chapter suggests using an advanced throughput optimal broadcast in point-to-multipoint wireless networks to improve wire-less link precision by using the Mayfly optimization method, a recent swarm intelligence soft computing technique, to improve geometrical configuration of interfering terminals and forecasted per-flow throughput

Organisational Communication, in today’s organizations has not only become far more complex and varied but has become an important factor for overall organizational functioning and success. The way the organization communicates with its employees is reflected in morale, motivation and performance of the employees. The objective of the present paper is to explore the interrelationship between communication and motivation and its overall impact on employee performance. The paper focuses on the fact that communication in the workplace can take many forms and has a lasting effect on employee motivation. If employees feel that communication from management is effective, it can lead to feelings of job satisfaction, commitment to the organisation and increased trust in the workplace. This study was conducted through a comprehensive review and critical analysis of the research and literature focused upon the objectives of the paper. It also enumerates the results of a study of organizational communication and motivational practices followed at a large manufacturing company, Vanaz Engineers Ltd., based at Pune, to support the hypothesis propounded in the paper.

Purpose The purpose of this paper is to analyze the factors affecting mobile banking adoption among young Indian consumers. Design/methodology/approach The authors use a cross-sectional survey research design to empirically examine the factors affecting mobile banking adoption among young Indian consumers. The study sample consists of 269 respondents aged between 23 and 30 years from India. Findings The findings of the study suggest that perceived usefulness (PU), perceived ease of use (PEU), perceived credibility (PC) and structural assurance (SA) are strong determinants of user satisfaction (US) and behavioral intention (BI) to use the mobile banking service. US was found to partially mediate the relationship between PU, PEU, PC and SA and BI to use the service. Perceived risk was found to be statistically insignificant in terms of its relationship with BI to use the service. Research limitations/implications The results of this study provide good evidence for banks to further revamp their work practices in the area of mobile banking to enhance the overall penetration of mobile banking in India. Originality/value The study identifies factors influencing mobile banking adoption among young Indian consumers. Furthermore, this study suggests that US partially mediates the relationship between factor influencing mobile banking adoption and BI.

Purpose This paper aims to examine the technology usage for talent management and its effect on organizational performance. Design/methodology/approach The grounded theory approach was used for this research. Semi-structured interviews with 122 senior HR officers of national and multinational companies in India were conducted after extensive literature review. NVivo 8.0 software was used for the analysis of the interview data. Findings Technology usage for talent management contributes to talent analytics and strategic HR management (SHRM). It was found that talent analytics and SHRM lead to developing a high-performing talent pool, which in turn contributes to organizational performance. Originality/value This study used the grounded theory approach to develop the proposed conceptual model for organizational performance using talent management technology. This study delivers important insights for talent managers, HR technology marketers and developers of technology.

Purpose Covid-19 outbreak has compelled the world-wide education system to use the digital collaboration platform (DCP) for online learning, for robust inclusive sustainable education. The purpose of this paper is to understand the adoption intention and effectiveness of DCP using technology acceptance model (TAM) for online learning among students studying in higher education institutes (HEIs) in India. Design/methodology/approach A structured questionnaire has been adopted to survey and collect data from 324 students studying in HEI of Maharashtra state in India. The questionnaire consisted of 28 constructs. The constructs in this section were measured using a five-point Likert scale ranging. In the first step, first-order confirmatory factor analysis is carried out by using the software IBM AMOS-20. The initial model is generated for six constructs, and outcomes are used to analyze the model’s goodness of fit and construct validity. In second step, structural equation modelling is carried out to do the path analysis of the proposed model. Findings The findings connote that the interactivity, cost-effectiveness and the core TAM constructs as perceived usefulness form positive attitude towards usage of DCP and intention to adopt it in near future by the students of HEI of India. The research is an attempt to provide possible explanations for the epochal relationships between the constructs and discusses the usage of information, which can be further used to enhance the acceptance of DCP among students in urban as well as rural India. Research limitations/implications The results and findings will provide a direction to the various stakeholders such as educators, management, learners and the parents on the adoption intention of digital collaborative platform from a learner’s point of view. This will lead to the knowledge which will help in practical implementations of these technologies. Practical implications The results and findings will provide a direction to the various stakeholders such as educators, management, learners and the parents on the adoption intention of DCP from learner’s point of view. This will lead to the knowledge which will help in practical implementations of these technologies. The findings imply that the interactivity, cost-effectiveness and the core constructs of TAM such as perceived usefulness form positive attitude towards usage of DCP and intention to adopt it in near future by the students of HEI of India. This research provides possible explanations for the significant relationships between the constructs and discusses how this information can be used to enhance the acceptance of DCP among students in urban as well as rural India. Social implications This research provides possible explanations for the significant relationships between the constructs and discusses how this information can be used to enhance the acceptance of DCP among students in urban as well as rural India, which is the need of hour for sustainable education. Originality/value There are tremendous studies on online learning and use of digital platforms including the constructs of TAM but in the times of Covid-19, where it has become mandatory for all educational institutes to use the digital collaborative platform for continuance of education. The study is original and is an attempt to understand students’ perspective towards usage of DCP and its effectiveness in learning in the rural parts of Maharashtra from where the students hail to study in HEI in Pune and Mumbai.

Purpose The purpose of this paper is to explore the relationship between quality management (QM) and performance, specifically how the infrastructure and core QM practices affect quality and business performance, in Indian manufacturing organizations. Design/methodology/approach In this study, the empirical data were drawn from 262 manufacturing organizations in India. The research model was tested using the structural equation modeling technique. Findings The findings of the empirical study revealed that infrastructure QM practices have a positive effect on core QM practices and indirectly on quality performance, whereas, core QM practices have a positive effect on quality performance. Also, quality performance has a positive effect on business performance. Research limitations/implications This study considered QM from two dimensions (infrastructure and core quality practices), the study further contributes to the understanding of the different roles played by diverse QM dimensions in determining business performance in terms of increased return on investment, shareholder and stakeholder value. Practical implications The study showed that infrastructure quality practices support the application of core quality practices. Therefore, managers must develop and maintain their organization’s quality system and sufficient resources need to be allocated to both types of practices in order to achieve the superior business performance. Originality/value This study considers both total quality management and Six Sigma practices for defining a new set of infrastructure and core QM practices in Indian manufacturing organizations.

Purpose Coronavirus (COVID-19) outbreak has utterly disrupted the worldwide education system and compelled an emergency immersion of unplanned and rapid online teaching-learning. The online teaching readiness would highly depend on the competencies of teachers and skills to adapt the pedagogy and new roles by the teachers. In this context, this study aims to assess higher education institutions (HEIs) teachers’ readiness to handle online education based on the online teaching readiness competencies model. Design/methodology/approach A structured questionnaire has been adopted to survey and collect data from 296 teachers of HEIs across India. The questionnaire consisted of 29 constructs. The constructs in this section were measured using a five-point Likert scale ranging. In the first step first-order confirmatory factor analysis (CFA) is carried out, by using the software IBM AMOS-26. The initial model is generated for five constructs and outcomes are used to analyze the model’s goodness of fit and construct validity. In the second step structural equation modeling (SEM) is carried out to do the path analysis of the proposed model. Findings The findings connote that the level of course design competencies, communication competencies, time management competencies are not sufficient among the teachers of HEI of India, whereas the technical competencies possessed by the teachers meet the requirements for readiness to handle online education. The research is an attempt to provide possible explanations for establishing relationships between the constructs and discusses the usage of information, which can be further used to enhance the online teaching readiness competencies for the teachers of HEIs of India. Practical implications The research is an attempt to provide possible explanations for establishing relationships between the constructs and discusses the usage of information, which can be further used to enhance the online teaching readiness competencies for the teachers of HEIs of India. Originality/value Teachers’ competencies are a vital part of teaching online which has become the need of the hour in this COVID-19 outbreak. Because of the need for emergency response and strategies to minimize learning disruption at higher education, the study identifies the online teaching readiness competencies possessed by the online teaching communities and provides guidelines to enhance their capacity to build up the longer-term resilience of education systems. The study will be a ready reckoner for online training competencies which can be used as training need analysis to make each teacher highly competent to impart knowledge using online teaching platforms.

Abstract The evolution of supply chain management, such as green supply chain and sustainable supply chain implementation in the industry, has been a great momentum over the past two decades, particularly from the mid of third industrial revolution to the current fourth industrial revolution. Given this interest, engineering and management scholars have examined various concepts and theoretical developments of green supply chain management (GSCM), especially during the past decade. The purpose of this paper, therefore, is to critically review the extant literature on pressures, practices, and performance of GSCM. The paper surveyed more than 150 research articles published in high‐impact refereed academic journals. The review synthesis has been discussed through GSCM pressures (government rules and regulations, corporate social responsibility, investment recovery, and green market), GSCM practices (eco‐design, internal environmental management, waste management, green purchasing aspect, quality, and product recovery), and GSCM performance (financial, operational, and environmental). Bibliometric results were presented, including the types of sources, year‐wise publications, country‐wise output, journal‐wise output, and top contributors in GSCM research.

Initiating the study into digital twin technology, the planning and implementation of the 6G network necessitates real-time interaction and alignment between physical systems and their virtual representation. From simple parts to intricate systems, the digital twin's flexibility and agility improve design and operational procedure efficiency in a predictable manner. It can validate policies, give a virtual representation of a physical entity, or evaluate how a system or entity behaves in a real-time setting. It evaluates the effectiveness and suitability of QoS regulations in 6G communication, in addition to the creation and management of novel services. Physical system maintenance costs and security threats can also be reduced, but doing so requires standardization efforts that open the door to previously unheard-of difficulties with fault tolerance, efficiency, accuracy, and security. The fundamental needs of a digital twin that are focused on 6G communication are covered in this chapter. These include decoupling, scalable intelligent analytics, data management using blockchain.

One of the most valuable resources is the forest, home to many animals and plants. Forest fire agencies worldwide have studied forest fire prevention and detection. Worldwide, natural and man-made calamities occur. Forest fires are environmental tragedies. The dense forest fire devours everything in its path. This research examines the forest fire detection and alert system to detect fires early. This research identifies forest fires before they spread to safeguard wildlife and natural resources. An Arduino microcontroller, flame sensor, ultrasonic sensor, thermistor, smoke sensor, buzzer, and GPRS are in every IoT (internet of things) device. Each IoT sensor records sensor values in the thing speak cloud. The cloud storage may pick and map forest fire threats by eliminating features from the input. MLP mapping maps forest fire danger, while AROC maps forest fire hazard. GPRS delivers cloud-based SMS warnings. Finally, forest department officials may interact.

Abstract The traditional methods used in big data, like cluster creation and query-based data extraction, fail to yield accurate results on massive networks. To address such issues, the proposed approach involves using the Hadoop Distributed File System (HDFS) for data processing, the map-reduce programming paradigm for data processing, and query optimization techniques to quickly and effectively extract accurate outcomes from a variety of options with a high processing capacity. The methodology proposed in this work makes use of Gaussian Mixture Model (GMM) for data clustering and the Salp Swarm Algorithm (SSA) for optimization. The security of preprocessed data stored on networked clusters with interconnections has been ensured by SHA algorithms. Finally, incorporating into consideration the important parameters, evaluation findings for the experimental performance of the model in the indicated methodology are produced. For this work, the estimated range of input file sizes is 60–100 MB. The processing of 100 MB of input files yielded an accuracy of 96% and results for specificity and sensitivity of 90% and 93%, respectively. The outcomes have been compared with well-known methods like fuzzy C-means and K-means approaches, and the results show that the proposed method effectively distributes accurate data processing to cluster nodes with low latency. Moreover, it uses the least amount of memory resources possible when operating on functional CPUs. As a result, the proposed approach outperforms existing techniques.