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This study aims to propose a methodology by integrating three approaches, namely, internal core technology, external knowledge flow and industrial technology development to help companies improve their decision-making quality for technology planning and enhance their research and development (R&D) portfolio efficiency.

The primary focus of this study is thin-film solar technology and patent data is retrieved from the United States Patent and Trademark Office (USPTO) database. This study presents a methodology based on the proposed integrated analysis method, constructed with patent indicators, centrality analysis of social networks and main path analysis.

The results of this study can be itemized as – the core technological competency: companies involved in two specific technology fields have lower strength in R&D portfolio than leading companies with single-core technology. Knowledge flow: most companies in a network are knowledge producers/absorbers and technological development: diverse source and sink nodes were identified in the global main path during 1997-2003, 2004-2010 and 2011-2017.

Latecomer companies can emulate leaders’ innovation and enhance their technological competence to seek niche technology. Using the global main path, companies monitor outdated technologies that can be replaced by new technologies and aid to plan R&D strategy and implement appropriate strategic decisions avoiding path dependency.

The knowledge accumulation process helps in identifying the change of position and the role of companies; understanding the trend of industrial technology knowledge helps companies to develop new technology and direct strategic decisions. The novelty of this research lies in the integrated approach of three methods aiding industries to find their internal core technical competencies and identify the external position in the competitive market.

The evolution of technology has become the mainstream of the current technological innovation era. Technological change is organized in its unique pattern and a new approach that takes place in a systematic and selective manner. Such change is generally molded with the amalgamation of various factors, namely, economic, social or scientific and technological. This paper aims to focus on identifying technological trajectories in a technological ecosystem with the case of m-payment technology.

This study constructs a patent citation network for mobile payment service technology through patent citation data and identifies the main evolution process using the main path analysis of the network. The scope of this study focuses on key innovation using social network analysis and patent citation network, validated using the case of a mobile payment system and analyzing its technological trajectory.

Analyzing technology evolution provides a greater insight of the overall technology landscape to the researcher and practitioner. Analyzing the m-payment technology landscape gives three main categories of m-payment systems: the mobile financial transaction system), the payee mobile device payment selection system and e-wallet services.

The novelty of this research lies in the process of identifying technological evolution using social network and patent citation network analysis. The case of m-payment technology ecosystem is studied quantitatively which is not explored by previous researchers. This research provides a way to develop the main path technology of innovative products or services to identify technology evolution using the case of m-payment landscape.

The ideology of this article is to study the performance concerns of SDN Controllers, with the help of developed SRGM and thereby obtain its optimal testing duration. The effect of undetected uncertainty in the parameter values have also been catered in the proposal.

These uncertainties in the parameter values are studied as the risk of not meeting desired set of requirements, whose removal causes additional cost. Considering these two constructs as attributes of MAUT, the controller's optimal testing duration is obtained.

The article focuses towards obtaining the optimal duration for which the SDN Controllers must be tested. It was observed that the inculcation of risk-attribute has provided the higher utility value as compared to any other existing scenarios.

Plenty of SRGM have been proposed in the literature which talks about the testing stop time determination problems. But, none of them have considered the impact of risk of not meeting the requirements (reliability) along with cost to obtain its testing stop time. Further, validation of the proposed model in presented with the help of two releases versions of SDN controller platform, ONOS, entitled as “Kingfisher” and “Loon” and has acquired promising results.

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.

Introduction: Many organisations nowadays use artificial intelligence (AI) in human resource (HR) activities like talent acquisition, onboarding of new employees, learning and development, succession planning, retention of employees, and automation of administrative tasks. When AI is integrated with HR practices, it helps HR personnel to focus more on the strategic aspects of the HR function and relieve them from routine HR activities.

Purpose: The readiness of employees to accept any change depends on organisational facilitation to change, employee willingness to accept the change, the requirement for change, situational factors, etc. This research studies the factors influencing employees’ change readiness towards acceptance of AI in HR practices. The researchers also strive to develop a conceptual technology adoption model for AI in HR practices by studying the earlier models. Finally, the research explores the acceptance of AI by various service sector employees and identifies whether there is any difference in their acceptance of AI based on demographic variables.

Methodology: A conceptual framework was derived using a combination of previous models, including the Technology Readiness Index (TRI), Change Readiness Scale, Technology Acceptance Model (TAM), Technology, Organization, and Environment (TOE) model, and change readiness scale. A structured questionnaire was designed and distributed to 228 respondents from the service sector based on the conceptual framework. An exploratory factor analysis (EFA) was used to determine the elements that influence employees’ level of change readiness.

Findings: The exploratory results on data collected from 228 respondents show that the model can be used for further research if a confirmatory factor analysis and validity and reliability test are performed. Employees are aware of AI and how it is used in HR practices, based on the study results. Moreover, while most respondents favour using AI in their company’s HR practices, they are wary of some aspects of AI.

Though academic study on the subject is still in its early stages, there is growing interest in using blockchain technology for transforming the supply chain. The academic literature is divided and yet only includes studies evaluating how the supply chain has changed organizations. To comprehend the new phenomena, this study aims to investigate the factors of blockchain technology in driving supply chain transformation. To be more precise, the authors developed from the literature the most prevalent criteria for determining if supply chain transformations are ready to be scaled up.

This study followed a combination of two multi-criteria decision making methods evaluation based on distance from average solution and complex proportional assessment) methodology in this research: planning, investigating, executing out, establishing a rating of the criteria and evaluating it.

The study shows that the “organizational driver” and the “technology driver” are the factors most important to the transformation of the supply chain, whereas the “financial driver” and the “regulatory driver” are less important. This study also makes some managerial recommendations to address the factors impeding the supply chain’s transformation. Each factor’s significance was explored, and a proposed study agenda was also presented.

Although the main forces behind the transformation of the supply chain have been recognized, further research into statistical correlation is required to confirm how the various elements interact.

This research aids decision-makers in comprehending the key forces behind supply chain transformation. Managers and decision-makers might better predict and allocate the necessary resources to start the road toward digitization and make well-informed choices once these aspects have been investigated and understood.

In light of the pandemic’s effects on the world and the increase in businesses embracing the digital economy, the supply chain transformation is more important than ever. Beyond blockchain deployment and the pilot studies on digital transformation, there is a gap. The topics and factors this study uncovered will operate as a framework and recommendations for more theoretical investigation and practical applications.

The purpose of this study is to investigate the influence of credibility and information quality (IQ) of online food vloggers on consumer attitude and purchase intention towards street food consumption. It also examines the relationship between consumer attitude and purchase intention influenced by online food vlogger reviews in North India.

In order to evaluate the framework, primary data were gathered from 389 street food consumers located in Jammu, Chandigarh and Delhi – cities situated in northern India. The collected data were then subjected to analysis using the partial least squares-structural equation modeling (PLS-SEM) techniques.

The results indicate that the perceived value of street food, influenced by the credibility of food vloggers and the quality of information provided, has a positive impact on consumer attitude and purchase intention towards street food consumption. A positive impact of consumer attitude on the purchase and consumption of street food was also observed.

This research offers a thorough investigation into the elements that influence consumers' opinions regarding vloggers endorsements. The findings reveal that consumer's attitudes towards vloggers recommendations are mainly influenced by the quality of information provided, followed by credibility and the intention to make a purchase. Furthermore, this research is of significance to practitioners and academics interested in comprehending consumer behavior in the realms of tourism and food-related endeavors.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0158

The learning outcomes include to identify and discuss important personality characteristics of an entrepreneur, to explore the business planning pitfalls that torment many new ventures and to understand key concepts of business model thinking.

The case is about Fetakart, a one-year old venture, which offered custom designed and printed t-shirts in Gujarat, India. The venture was launched by Himanshu Dhadnekar in 2018. Himanshu was a young first-generation entrepreneur who displayed entrepreneurial characteristics since his school days. Later, he was involved with a few failed startups, worked as an employee in different companies and curated new ventures. Fetakart was born out of another venture, MotivPrints, which sold a large variety of custom designed and high-quality printed products such as visiting cards, office essentials, apparels, packaging and marketing material. At MotivPrints, Himanshu discovered the market for customized t-shirts in India but with very little knowledge of the business model or capabilities of leading players such as Bewakoof.com. The venture started struggling and faced several problems including high cost demands by manufacturers and lack of funding. Where did Fetakart go wrong? Was Fetakart born out of impulse? Did Himanshu have a viable business model for Fetakart?

The case can be discussed in the class of entrepreneurship at master’s level. It can also be used in entrepreneurship specialization course and strategic management elective(s) in the second year of post-graduation. The case can be particularly useful for young entrepreneurs associated as incubatees with a business incubator and for an executive development program related to new ventures.

Teaching Notes are available for educators only.

CSS 3: Entrepreneurship.

Digital technology and SDGs have gained increasing interest from the research community. This chapter aims to explore the field through a holistic review of 188 publications from 2017 to 2022. For the systematic review of 188 articles, a three-step methodology comprising of PRISMA guidelines was performed, bibliometric analysis and text analysis using VOS-Viewer and Sentiment Analysis using RStudio had been undertaken. Bibliographic coupling revealed the following clusters Digital Space (Over all SDG), Localising SDGs, Financial Systems and Growth (SDG 8), Sustainable Supply Chain (SDG 9), Education (SDG 4), Energy Management (SDG 7), Smart Cities (SDG 11 and 13), Gender, Skills, and Responsibility (SDG 5 and 12), Food Management (SDG 1, 2 and 3), Business Innovation (SDG 8 and 9) and ICT (SDG 9). Next, co-occurrence analysis highlighted the following clusters Circular Economy (SDG 8), Higher Education System (SDG 4), Digital health (SDG 3), Industry 4.0 (SDG 9) and Supply Chain Management (SDG 9). Next, text analysis traced the most relevant areas of work within the theme. Finally, sentiment analysis revealed positive sentiments of the field. The research concluded that only a few SDGs had found major focus while the others don't have any solid ground in the literature. This chapter presents a knowledge structure by mapping the most relevant SDGs in the context of digital technology and sets directions for future research.