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This paper aims to understand and identify the various barriers in adopting new telecom services in rural areas for improving the penetration and revenue of the telecom companies. These barriers are modeled to study their inter-relationships and prioritize them for strategizing appropriate management action plans.

Delphi technique has been used to form a consensus with the telecom managers working in rural areas to finalize the barriers. An integrated Interpretive Structural Modeling–Analytic Network Process (ISM–ANP) approach has been adopted to establish the complex relationships, cluster the relationships, to understand and prioritize the telecom service adoption barriers.

The major contribution of this research is imposing directions and dominance of various barriers to promote better adoption of new telecom-based mobile services in rural areas. The proposed integrated method can aid in decision making by providing more informative, accurate and a better choice than using either ISM or ANP in isolation.

The generalizabilty of these research findings is limited, as it was generated specific to rural telecom service adoption barriers in Indian context. Because decision-making problems are usually complex and ill-structured, every decision is based on the decision-maker’s expertise, preferences and biasness of the experts who showed their interest to participate in the research.

This paper forms the basis of identifying the reasons for poor adoption of telecom-based mobile services in rural India. This study would help the telecom companies and the managers to understand and develop strategies to target the rural audience by introducing action plans and innovative mobile services to overcome the identified barriers. By applying the proposed methodology, telecom companies can classify and prioritize their action plans as short-, medium- and long-term plans to systematically overcome the identified barriers.

This paper provides a base for understanding various factors that affect the adoption of telecom-based mobile services. It demonstrates the use of an innovative approach to develop an integrated model to understand the barriers.

The purpose of this paper is to identify the factors influencing investment decisions in mobile services for profitablity and to become a global leader in mobile services sector.

A two-stage methodology is followed. In the first stage, factors are identified from literature, and are validated with telecommunication domain experts using the t-test. In the second stage, interpretive structural modeling (ISM) is used to understand the complex interrelationships among various factors. Further, MICMAC analysis is performed to analyze the indirect relationships and their effect on different factors by stabilizing the rank based on driving and dependence power. Based on MICMAC analysis, four clusters are identified to aid the policy- and decision-makers.

The major contribution of this research is imposing directions and dominance of various factors to make informed decision-making for investment in mobile services to meet the upcoming demand for mobile services in Indian telecommunication sector.

The applicability of these research findings is limited to emerging telecommunication market.

This paper forms the basis for identifying various factors that act as the driving force for the Indian telecommunication operators to pay special attention toward mobile services, with telecommunication data analytics and developing context-aware services. This paper will aid policy-makers in the government, managers in telecommunication companies and other stakeholders such as content providers, channel partners and application developers to take a lead role in developing appropriate mobile services to meet local needs of Indian users. It will help in developing strategies to collaborate and motivate other stakeholders, including device manufacturers to understand and work collaboratively to become world leader in mobile services.

This paper provides a framework for understanding the various factors that encourage telecommunication companies to establish and invest in mobile services and setup a separate vertical in their organization with a focus on mobile services to meet the future demands of Indian market. Appropriate utilization of telecommunication data analytics, personalization of services, customization in local languages and support for convergent services would encourage adoption of mobile services.

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.

ANN shows more accuracy than MLR with an R²-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R²-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.

ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

This paper aims to focus on the basic principle of WO₃ gas sensors to achieve high gas-sensing performance with good stability and repeatability. Metal oxide-based gas sensors are widely used for monitoring toxic gas leakages in the environment, industries and households. For better livelihood and a healthy environment, it is extremely helpful to have sensors with higher accuracy and improved sensing features.

In the present review, the authors focus on recent synthesis methods of WO₃-based gas sensors to enhance sensing features towards toxic gases.

This work has proved that the synthesis method led to provide different morphologies of nanostructured WO₃-based material in turn to improve gas sensing performance along with its sensing mechanism.

In this work, the authors reviewed challenges and possibilities associated with the nanostructured WO₃-based gas sensors to trace toxic gases such as ammonia, H₂S and NO₂ for future research.

This paper aims to develop a framework for critical information infrastructure (CII) protection in smart government, an alternative measure for common cybersecurity frameworks such as NIST Cybersecurity Framework and ISO 27001. Smart government is defined as the government administration sector of CII due to its similarity as a core of smart technology.

To ensure the validity of the data, the research methodology used in this paper follows the predicting malfunctions in socio-technical systems (PreMiSTS) approach, a variation of the socio-technical system (STS) approach specifically designed to predict potential issues in the STS. In this study, PreMiSTS was enriched with observation and systematic literature review as its main data collection method, thematic analysis and validation by experts using fuzzy Delphi method (FDM).

The proposed CII protection framework comprises several dimensions: objectives, interdependency, functions, risk management, resources and governance. For all those dimensions, there are 20 elements and 41 variables.

This framework can be an alternative guideline for CII protection in smart government, particularly in government administration services.

The author uses PreMiSTS, a socio-technical approach combined with thematic analysis and FDM, to design a security framework for CII protection. This combination was designed as a mixed-method approach to improve the likelihood of success in an IT project.

The purpose of this article is to provide an interview with Professor M.S. Rao.

The interview is conducted by an independent interviewer.

Professor M.S. Rao is a leadership development practitioner with over 30 years' experience. He has written 16 books, including Secrets of Your Leadership Success – The 11 Indispensable E's of a Leader, and Smart Leadership: Lessons for Leaders. In his book Soft Leadership: Make Others Feel More Important, Professor Rao advances his model for soft leadership, blending characteristics of soft skills training with traditional leadership theory. This approach is endorsed by management guru David Ulrich, who provided the foreword for the book. Professor Rao also maintains two popular blogs, http://profmsr.blogspot.com (Where Knowledge is Wealth) and http://professormsraoguru.blogspot.com (Knowledge Grows When Shared).

The paper provides useful information from a leading figure regarding soft leadership.

The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to elucidate how artificial intelligence (AI) herald great promise in human resource management in decreasing cost, attrition level and enhancing productivity. Considering the dearth of studies on recent trends in human resource management (HRM) in the context of AI, the study elucidates the role of AI in facilitating seamless onboarding, diversity and inclusion (D&I), work engagement, emotional intelligence and employees’ mental health. Thus, a conceptual model of recent trends in HRM in the context of AI and its organisational outcomes is proposed. A systematic review and meta-synthesis method are undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes. The findings of the study suggest that using natural language processing (NLP) and robots has eased the onboarding process. D&I is promoted using data analytics, big data, machine learning, predictive analysis and NLP. Furthermore, NLP and data analytics have proved to be highly effective in engaging employees. Emotional Intelligence is applied through AI simulation and intelligent robots. On the other hand, chatbots, employee pulse surveys, wearable technology, and intelligent robots have paved way for employees’ mental health. The study also reveals that using AI in HRM leads to enhanced organisational performance, reduced cost and decreased intention to quit the organisation. Thus, AI in HRM provides a competitive edge to organisations by enhancing the performance of the employees.