Search results

This study aims to investigate the process of developing loyalty in the Chinese mobile health community from the information seeking perspective.

A covariance-based structural equation model was developed to explore the mobile health community loyalty development process from information seeking perspective and tested with LISREL 9.30 for the 191 mobile health platform user samples.

The empirical results demonstrate that the information seeking perspective offers an interesting explanation for the mobile health community loyalty development process. All hypotheses in the proposed research model are supported except the relationship between privacy and trust. The two types of mobile health community loyalty—attitudal loyalty and behavioral loyalty are explained with 58 and 37% variance.

This paper has brought out the information seeking perspective in the loyalty formation process in mobile health community and identified several important constructs for this perspective for the loyalty formation process including information quality, communication with doctors and communication with patients.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

The study aimed to examine the robotic process automation (RPA) contextual (center of excellence and scalability) and the multidisciplinary (TOE) determinants of RPA adoption in service industries in the emerging economy.

Ten factors were identified through literature surveys and popular studies grounded in technology, organization and environment. SPSS AMOS SEM is used for scale measurement and hypotheses testing. A sample of 313 respondents was collected from middle to above middle management executives of service industries from India. The authors tested the hidden layers and non-linear relationships using artificial neural network (ANN) analysis.

The low complexity, center of excellence (CoE), and industry/business partner pressure were significant to the RPA adoption in service industries in emerging economies. Counterintuitively, the scalability showed a negative influence on the RPA adoption, and the process capability did not show influence. The results of SEM and ANN were consistent.

This research can unfold the RPA adoption scholarly debate to multiple services industries beyond the telecom sector in emerging economies.

RPA is a disruptive technology on the artificial intelligence (AI) continuum. It has the potential to change the ways of working and enable technology-driven transformation. However, despite having thriving service industries that can benefit from RPA, emerging economies lag in adoption compared to the developed nations.

The RPA and automation can bring transformation to human society. Large economies such as India and China have large-scale demand for services, and the waiting lines are a common issue struggled by society. RPA can address the scalability issues of several services.

This study is among the first to examine technology-organization-environment (TOE) with RPA, including RPA contextual variables such as the CoE and scalability. Literature reports TOE applications on several emerging technologies of Industry 4.0 such as cloud, blockchain, big data and 3 Dimensional Printing (3DP), but no or little reported studies around RPA in services industries in emerging markets.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.

In the recent scenario, there has been an increasing trend toward lean practices and implementation in production systems for the improvement of an organization’s performance as its basic nature is to eliminate the wastes. The increasing interest of customers in customized products and the fulfillment of customers’ demand with good productivity and efficiency within time are the challenges for the manufacturing organization; that is why adopting lean manufacturing concept is very crucial in the current scenario.

In this paper, the authors considered three different methodologies for fulfilling the objective of our research. The analytical hierarchy process, best–worst method and fuzzy step-wise weight assessment ratio analysis are the three methods employed for weighting all the enablers and finding the priority among them and their final rankings.

Further, the best results among these methodologies could be used to analyze their interrelationships for successful lean supply chain management implementation in an organization. In this paper, 35 key enablers were identified after the rigorous analysis of literature review and the opinion of a group of experts consisting of academicians, practitioners and consultants. Thereafter, the brainstorming sessions were conducted to finalize 28 lean supply chain enablers (LSCEs).

For lean manufacturing practitioners, the result of this study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process.

This paper is the first of the research papers that considered deep literature review of identified LSCEs as the initial step, followed by finding the best priority weightage and developing the ranking of various lean enablers of supply chain with the help of various methodologies.

The helical spring lock washer (HSLW) is a part of nut bolt joint assembly used in different industries like automobile, aerospace, mechanical, chemical, electrical, electronics, etc. It works as a part of temporary joint and plays important role in loosening behavior of assembly under dynamic (vibrations) conditions. Thus, the purpose of this paper is to investigate the performance of HSLW under different controlled operating conditions in order to satisfy its functional requirement.

In the present investigation, a novel test rig is designed and developed to determine the load-deflection characteristics of HSLWs. The test rig facilitates the controlled linear displacement of the HSLW with predetermined angular rotation of the handle gives the corresponding reaction load on the display. Additionally, the repeatability and reproducibility of the test rig was carried out.

The newly designed and developed test rig is capable enough to differentiate the load-deflection characteristics during compressive loading and unloading of HSLWs. Additionally, the loss of strain energy can be determined from the load-deflection characteristics of HSLW.

The present test rig is designed and developed to investigate the load-deflection characteristics under compressive loading and unloading of HSLW. The test rig has least count of 0.4905 N for load measurement and 0.01389 mm for linear displacement.

Product Management, Marketing Strategy, Growth Strategies.

Bachelor of Business Studies, MBA, Executive MBA.

The case documents the humble beginning of Kewal Kiran Clothing Limited (KKCL) in 1981 to its current position as a leading fashion apparel brand in India. However, competition from new national players, emergence of global players in India, private labels of retailers and dawn of Internet retailing has created significant growth challenges for the firm. Mr Jain, the Managing Director of KKCL, is contemplating the growth strategies for the firm and possible changes in the business model, as he is developing the 2014-2015 strategic plan for KKCL. This is imperative to reach the ambitious sales target of INR 10 billion by 2018-2019. The students are expected to assess the performance of KKCL on multiple quantitative and qualitative data points given in the case and exhibits. It encourages them to come up with possible growth strategies for the firm.

The case is expected to guide students in comprehending the multi-thronged challenges pertaining to fashion apparel industry; in Situational Analysis of the firm, which includes assessing internal and external factors; and in recommending the best possible growth strategy after due evaluation and deliberation using Ansoff's Matrix.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

The purpose of this paper is to illustrate how a well-performing company can turn into a loss-making company on account of adverse industry cycle and poor management of risks in the business. The importance of factors like optimal level of leveraging, the ability of the management to deal with external and internal risks, and importance of corporate governance in the process of credit appraisal is understood from this case.

The case relates to the credit appraisal by the banks of a prominent steel company in India. The company, Bhushan Steel Limited, was doing very well. The banks lent aggressively to the company, based on their credit appraisal. However, the company soon turned insolvent on account of poor assessment of risks and deteriorating external factors. While this case may be analysed and studied through the eyes of both the Management and the lenders, the focus is currently on the latter. In a real-world scenario, the challenge for the lender is to sieve through the financial as well as non-financial data and make a valid conclusion on the level of credit worthiness of the borrowing company. This includes the topics of operational efficiency and synergies, commodity price cycles, external credit ratings, operating and financial leverage, regulatory risks and corporate governance.

Post graduate business management programmes – Finance specialisation.

Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 1: Accounting and Finance

The purpose of this paper is to discuss the commercialisation mechanisms of the innovations that emerge from the informal sector of Indian economy. Also known as grassroots innovations, they are said to better fit with the local market demands and conditions in the developing nations of the world. The paper discusses the grassroots innovation ecosystem in India and the role that is played by the state in providing institutional support.

The paper is based on an exploratory study using both the primary and secondary sources of data. Primary data are taken from the interview of the innovators during the field work, whereas secondary data are acquired from research articles published in various journals indexed in Scopus and web of sciences, government publications and reports. The annual reports of National Innovation Foundation are analysed to gather information and to build the arguments for this paper. The secondary data are also collected and evaluated from the database of the grassroots innovators available on Grassroots Innovation Augmentation Network.

The paper provides insight into how the grassroots innovations are commercialised in India through different pathways such as social entrepreneurship, technology transfer and open source technology. It takes four case studies to discuss the institutional support to the grassroots innovator and the challenges in the diffusion of the grassroots innovations.

Due to the chosen research approach, the results cannot be generalised on all grassroots innovations. Researchers are encouraged to conduct a survey of more grassroots innovations in order to derive generalised outputs.

The paper includes implications for understanding the diffusion process of grassroots innovations that can be useful for all the emerging and developing nations.

The paper fulfils an identified need to study the diffusion modes of informal sector innovations and management of grassroots innovations.

Recent research advances in artificial intelligence, machine learning, and neural networks are becoming essential tools for building a wide range of intelligent applications. Moreover, machine learning helps to automate analytical model building. Machine learning based frameworks and approaches allow making well-informed and intelligent choices for improving daily eating habits and extension of healthy lifestyle. This book chapter presents a new machine learning approach for meal classification and assessment of nutrients values based on weather conditions along with new and innovative ideas for further study and research on health care-related applications.