Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation

Despite the benefits of Artificial Intelligence (AI) and its potential to produce deep insights and predictions, its adoption and usage are still limited in the area of crime management. Over the years, crime rates have been increasing in India, and law enforcement agencies face enormous challenges given the increasing population, urbanization, limited resources, and ineffective conventional models of reactive and investigative policing. There is an unprecedented opportunity for AI to be leveraged together with new policing models such as intelligence-led policing and predictive policing for effective crime management. In this research-in-progress paper, we offer a deeper understanding of factors significant for the adoption intention of AI for crime management in India. Further, on the practical front, the study will help law enforcement agencies to effectively leverage AI and implement innovative policing models for crime management.

With the growing complexity of global supply chains, geopolitical events, pandemics, and just-in-time processes, organizations can benefit immensely in managing supply chain risks by adopting artificial intelligence (AI). Building upon past research in technology adoption, we study factors influencing the adoption intention of AI in SCRM across organizations in India. Based on a qualitative study, we discuss the applications and uniqueness of AI adoption in the field of supply chain risk management (SCRM) and propose a research model on the adoption, implementation, and routinization intention of AI in SCRM at an organizational level. Secondly, we discuss the implications of the study and the benefits to decision-makers and supply chain planners in devising effective strategies when adopting AI in SCRM.

Artificial Intelligence has been increasingly gaining acceptance across advanced functions in numerous fields and industries. This includes marketing, customer support, and leads generation in healthcare, transportation, education, and off late in e-commerce. Machine learning as a subset of artificial intelligence techniques provides various algorithms that enable machines to learn from historical data and make realtime predictions on numbers and texts. Most of the businesses nowadays are trying to increase their reach and making sure that they are available to cater to the customers when they need help. This also enables the companies to market and respond to the queries of potential customers on a realtime basis. Chatter robots or chatbot is one such application of machine learning which allows the business to provide round the clock support to customers and potential leads for marketing questions. Most of the business fail to venture in the domain of hosting chatbot on the website as they do not have enough conversational data with them to train the machine learning algorithm and wait for years to collect enough sample. With the proposed language model-driven chatbots, businesses starting fresh in the domain of the hosting this application can use the user-generated content on social media to fuel the backend framework for the chatbots and start hosting the application.

This paper presents an approach for describing and characterizing algorithms that are discussed as though they embody artificial intelligence. After identifying key assumptions related to algorithms and summarizing work system theory (WST), this paper uses a hypothetical example to introduce aspects of WST and two additional ideas, facets of work and dimensions of smartness in devices and systems. Next, it applies those ideas to aspects of five AI-related examples presented by entrepreneurs and researchers at an MIT AI conference in July 2020. Those examples were selected because they illustrated many AI-related issues. This paper’s contribution is a new approach for characterizing real world applications and impacts of almost any system that uses algorithms or is associated with artificial intelligence.

As the number of research outputs in the field of AI in Marketing increased greatly in the past 20 years, a systematic review of the literature and its developmental process is essential to provide a consolidated view of this area. This study conducted a bibliometric analysis for the knowledge domain of AI in Marketing by using 617 research outputs from the Web of Science database from 1992 to 2020. Knowledge maps of AI in marketing research were visualised by employing CiteSpace software.

Over the past four decades, Indian Information Technology (IT) industry has been delivering traditional software and BPM (Business Process Management) services to its clients across the globe. Providing cost-optimized, yet high-quality services following standard process methodologies has made it an attractive destination to clients across industry verticals. Today, the challenge before this industry is to provide emerging technology solutions to clients in their digital transformation drive. Situated at this pivotal juncture in its journey, the ‘work from home’ (WFH) norm during to the recent COVID-19 pandemic posits challenges of a new kind for this sector. We explore these challenges based on our four-month ethnographic study (Jan-May, 2020) in a service-based IT organization situated in Bengaluru, which over the past five years has been developing Artificial Intelligence (AI) based solutions to its clients.

Education has been transformed by significant breakthroughs in AI, mobile internet, cloud computing and Big Data technologies. More personalized educational settings are developed by increasingly integrating contemporary learning environments with new technologies. However, few examples of executed AI enabled learning interventions have been identified. Therefore, a mapping of literature on AI enabled learning systems was done. 121 studies published in the last five years were analyzed. This paper presents a discussion regarding on what mainly AI enabled contemporary learning environments are designed to achieve. The major contribution of the study is bringing awareness to researchers and system developers on the purposes of AI enabled contemporary learning environments. This review will act as a guide for future studies on how to better design AI enabled learning environments.

There are everyday examples of Artificial Intelligence (AI) in different areas. Some of the prominent AI applications are virtual assistants, robots, AI applications related to computer vision and those used in medicine. This paper attempts to examine the recent trend of the real-world applications of AI and also identify the business models for these. The business models are then examined to see if these are existing business models that are used to enhance businesses using AI or if new AI-driven business models have emerged. The emerging AIdriven business models are Federated learning, the triangular partnership model and the use of Emotion AI to come up with new business models. The existing ones enhanced by AI are the freemium model, Rent to Buy model, leverage customer data and the land and expand model.

Different theories, models and frameworks have been used to study technology adoption, some explaining the determinants of adoption at the individual level, some at the organizational level and some at both. As Artificial Intelligence (AI) is gaining traction in many sectors, it will be beneficial to understand the determinants and barriers to AI adoption. In this paper, an attempt has been made to review journal articles and other reports pertaining to AI adoption and understand the adoption theories used and the factors that facilitate and those that hinder AI adoption. Articles on adoption studies of autonomous vehicles, big data analytics, robots and cognitive engagement applications dominated the list of journal articles. Diffusion of Innovation, Technology, Organization and Environment Framework and the unified theory of acceptance and use of technology (UTAUT) were some of the dominant theories/frameworks used. Factors influencing adoption at the individual level were related to trust, security, purchase price, intrinsic motivation, social influence, utilitarian benefit whereas at the organizational level, it was related to the technical competencies, strategic road mapping for AI, top management support and the digital maturity of the organization.

Artificial Intelligence (AI) usage is rapidly expanding in our society. Private sector has already taken the leap of faith in using AI for efficiency and for generating better value for the customers and shareholders. The promise of AI is quite alluring for the governments as well. It promises to be the breakthrough technology which can catapult public sector to hitherto unseen efficiency and productivity. It has the potential to truly transform the public service delivery and the way government interfaces with citizens – from a demand driven model to a predictive model of public service delivery. However, there are a large number of pitfalls and blind-spots associated with AI, which make its adoption in government particularly challenging. For successful adoption of AI in public sector, governments must understand these challenges clearly and lay down regulatory public policies to ensure that the possible adverse impacts (such as exclusion, bias etc.) of AI are mitigated. This paper attempts to systematically explore these challenges with a view to enable public policy makers to respond to them.

Digital transformation is a key imperative across multiple industries globally. One of the main tenets of digital transformation is improving the intelligence quotient in an organization in order to enable accurate decision-making. Intelligence in the digital era is fueled by data and by the ability to analyze data in a way that generates meaningful insights, thereby making an organization competitive. Literature shows that the benefits of being analytically competitive are widespread and impactful across many organization functions like strategy, finance, marketing and operations; but this can be accomplished only if an organization has the right level of capability for adopting and executing analytics initiatives. Data, leadership, people skills, culture and governance are capabilities that are essential for successful analytics endeavors in an organization. This study is focused on building an end-to-end perspective comprising input factors and outcomes of being analytically competitive.

Online Professional Development (PD) programmes for government school teachers provide benefits of low costs to the administration and flexible schedules for the participants. However, research on the use of technology in PD programmes has reported mixed results, thus warranting further investigation. Exploring the associations between the variation in engagement of and outcomes among the participants may provide insights for future research. The paper presents analysis of pageview logs and survey responses of 6933 participants of an online PD programme. First, four latent online engagement profiles were extracted using mixture modelling. Then, associations between participants’ latent profiles and reported change in self-efficacy beliefs were analyzed. Finally, limitations and implications of the work are presented.

Data mining is a method to refine raw data to useful information. In education, data mining is a significant research part used to progress the value of education by observing students’ performance and understanding their learning patterns. Real-time student feedback would empower faculty and students to comprehend the teaching and learning problems in the most user-friendly way for the students. This paper uses a Lexicon based sentimental analysis technique to analyze students’ feelings and emotions through their feedback by correlating learning analytics to grounded theory. The sentiment analysis technique is a computational process to identify and classify subjective information such as positive, negative, and neutral from the source material. It can extract feelings and emotions from a piece of a sentence. Hence this paper aims to recognize the students’ positive or negative feelings and distinguished emotions, towards online teaching. The methodology undertakes four processes. The first process is data extraction from the feedback collected from the students through open-ended questions (Text) and is used as source material and imported to R studio. The second process is data cleaning /data preprocessing, removal of annoying data, and separation of data. The third process is sentimental analysis, which divides the data into positive, negative, and neutral categories/groups. This lexicon-based method of sentimental analysis is used to classify the sentiments. The results were estimated using sentiment scores and emotional variance. The sentiment scores result found that students have positive sentiments/emotions towards online teaching and emotions vary concerning the online class timing.

With the growing popularity of cryptocurrencies, Blockchain systems have found a new audience within the business environment. The decentralized ledger technology that allows the creation of verifiable transactions has extraordinary applications within business. In this paper the authors have attempted to identify the core benefits of using Blockchain technology to improve Supply Chain Management (SCM). With industry 4.0 transformation in its preliminary stages, a discussion on how Blockchain systems can benefit SCM is important. Like any other information system, Blockchain implementation is not just limited to infrastructure changes but also requires organisational wide changes to reap the benefits of the new system. The inherent technological issues associated with Blockchain are preventing many organizations from adopting Blockchain systems at a large scale. The authors have presented a model to guide the implementation of the Blockchain based systems using the industry accepted Capability Maturity Model (CMM) as the reference.

This article studies the adoption of blockchain technology in Indian Micro, Small, and Medium Enterprises (MSME) in the context of innovations in supply chain management (SCM) using blockchain technology. Besides finance, SCM is one of the main areas where disruptive innovations based on blockchain technology are going to be deployed. Blockchain technology’s unique proposition lies in the attributes of trust, transparency, traceability, immutability, and decentralization. MSME’s form the backbone of the Indian economy. This article provides a socio-technical factors-based analysis of the adoption of blockchain technology in Indian MSME, particularly in a SCM context. Sustainability is an important variable that is expected to moderate the relationship between socio-technical factors and large-scale adoption. The relationships are tested via a survey of professionals in MSMEs in India who are both familiar with blockchain technology and sustainable SCM. This study shall offer a deeper understanding of the application of socio-technical systems theory for the adoption of blockchain technologies by MSMEs in India.

Cryptocurrencies are sub-classes of digital currencies. Trading of these currencies have gained momentum during the past few years and have become new investment avenues for investors. An understanding on the market anomalies, which are patterns in asset prices would help the investors to adopt suitable strategies while trading in this asset class. This study aims to examine the presence of three calendar anomalies, day of the week, turn of the month, and year end effect in the cryptocurrencies. The top five cryptocurrencies which constitute a major share of the market capitalization value are selected for the study and the period of study is from July 23, 2017 to July 9, 2020. Dummy Variable Regression using GARCH (1, 1) model was employed on the log value of returns of the cryptocurrencies. The study provides evidence on the existence of anomalies during Thursdays, the months March and April, and at the turn of the year.

Cryptocurrencies are digital currencies and trading these currencies have gained huge momentum in recent years. The sophisticated features, complexities on regulatory framework, and high volatility of Cryptocurrencies would pose trading challenges to new investors and/or investors with limited knowledge. Investors generally are influenced by fund managers, financial analysts or other investors who are considered as well informed and highly knowledgeable peers. Investors mimic their behaviour to perform trading activities and such behaviour is termed as Herding. Covid 19 pandemic triggered severe uncertainties in the cryptocurrencies market and has led to wide fluctuations in prices causing severe volatility and market crashes. This paper aims to examine the herding behaviour in cryptocurrency market during the pre Covid 19 and Covid 19 pandemic period using the Cross-Sectional Standard Deviation (CSSD) approach. The findings of the paper reveal that herding was evident among all the ten crypto-currencies in normal market conditions of the entire sample period but not during market upswing or downswing. However, the herding behaviour was present in the cryptocurrencies Litecoin, Cardano and Dash during the Covid 19 pandemic period in all market conditions.

User involvement has been made easy and common in the context of mobile applications (apps), where user reviews were often collected to enlighten apps developers on novel features. However, users might not always possess the required technical expertise to make commercially feasible suggestions. The value of user reviews also varied due to their unmanageable volume and content irrelevance. In this study, over 40,000 user reviews with 50 apps were analyzed to empirically examine the association between customer led innovation and the revenues from the apps. Our findings indicated that customer led innovation alone did not lead to significant changes in revenues. Its impact was only significant if the developers responded to the user reviews faster. These results contributed to the user involvement literature by highlighting the importance of the moderating effect of developer responsiveness. Apps developers could also benefit from our empirical evidence that proved the value of user involvement that enhanced innovativeness.

The Education sector is undergoing transformation-using technology. The virtual classrooms are replacing the traditional classrooms. The proposed model envisaged is student-centric, where the student has the choice to model his curriculum depending on the student interests and area to work and not follow the usual model, using credits from micro-credentials added per unit. The challenge is to use the features, benefits of Blockchain to introduce this new Education model technology to improve efficiency by reducing cost and improving accountability. The transformation in Education Framework can revolutionize the future Learning, teaching Industry to reduce cost and time. This revolution will also lead to improved chances for Learners to be employable. The Research paper proposes to use the Modified ADKAR Change Management Model to validate this Research study and will be a significant contribution to the research topic and the Theory known.

Understanding students’ sentiment is valuable to understanding the changes that could or should be made in curriculum design at third level. Learning analytics has shown potential for improving student learning experiences and supporting teacher inquiry. Yet, there is limited research that reports on the adoption and actual use of learning analytics to support teacher inquiry. This study captures sentiment of postgraduate students by integrating learning analytics with the steps of teacher inquiry. This study makes two important contributions to teaching and learning literature. First, it reports on the use of learning analytics to support teacher inquiry over three iterations of a business analytics programme between 2016 and 2019. Second, evidence-based recommendations on how to optimise learning analytics to support teacher inquiry are provided.

Organizations would reap the intended benefits of Electronic Human Resource Management (eHRM) implementations through its sustained usage and adoption by individuals. This study is centered on the view that actual usage behavior is critical to studying eHRM adoption and needs to be measured in the context of the intended eHRM outcomes; operational, relational and transformational. Using a 10-item scale to measure eHRM usage behavior in a research framework grounded in Technology Acceptance Model (TAM2), this study investigates the factors influencing eHRM adoption in terms of “intention to use” and “actual usage behavior”. Results indicate support for most TAM2 hypotheses. The study also enriches our understanding of organizational context factors; scope of implementation influencing Image-Usefulness relationship and post implementation stage influencing Ease of use-Intention to use relationship.

Artificial Intelligence has gradually materialized as an independent research field within information systems and business domains. The new forms of work evolving in the business require substantial experimentation, lead generations, and real-time recommendations. This has driven the extraordinary increase in the adoption of Artificial Intelligence technologies. Even with front runner organizations across the domain envisioning the advantages of early adoption of Artificial Intelligence technologies, some organizations scuffle the adoption owing to various barriers. This paper analyzes the characteristics that lead to and factors inhibiting the adoption of Artificial Intelligence at the organization-level. Through this paper, we report the results of Twitter conversations involving small and medium scale organizations about their level of adoption of Artificial Intelligence and barriers that they are facing. Through this analysis, we provide insights and agenda to help the executives of small and medium scale organizations to prepare for the adoption of Artificial Intelligence.

Disruption at the physical workplace, developed by threats like the coronavirus, triggers revisiting old assumptions and exploring opportunities for new ways of remote working. With the global epidemic spreading, businesses are gearing up with the managers and their respective teams to work from home (WFH). This research has offered a setting for advancing understanding of virtualization of WFH process by exploring the factors that enable or constrain the information and communication technology (ICT) enabled virtualization of processes in employee’s WFH process through empirical support for the process virtualization theory (PVT). Setting pandemic outbreak as a context, outcome of this research is reliant on two independent studies conducted to examine the influencing factors. First study conducted just before the onset of pandemic outbreak, found that parts of the constructs proposed in the PVT had expected outcomes regarding the characteristics of process virtualization. Contrary to this, second study conducted after pandemic outbreak found that major constructs proposed in the PVT behaved otherwise regarding the characteristics of process virtualization. To fill the gaps in empirical knowledge, the enablers and inhibitors so found together may be motivations to anticipate business organizations and their workforces to experiment with this form of work process, predominantly improved flexibility for organizations and employees, improved productivity, quicker responsiveness to the needs and unexpected man-made and natural disasters, lower absenteeism, improved employee retention, greater cost control, along with more general social benefits.

AI-based voice assistant (VA) technologies are facing an unprecedented growth. VA are available as a standalone device like Amazon Echo dot or Google home and also as an extension such as Google maps and OK Google. Extant research has mostly focused on the device specific characteristics to explain the adoption of VA. In this research, we take a different approach and examine the psychological determinants of VA adoption. We look at how factors such as playfulness, escapism, anthropomorphism, and visual appeal of VA influence the attitudes (hedonic and utilitarian) of consumers. Moreover, we also examine the effects of psychological characteristics of VA on usage intentions and satisfaction, which lead to a favorable word-of-mouth (WOM) behavior that is critical for adoption of a technology. Using a structural equation modeling approach, our results suggest that psychological factors have a significant positive influence on both attitudes. Hedonic attitude further influences satisfaction and utilitarian attitude positively impacts usage and satisfaction, which have a positive association with WOM. Our research offers useful insights to marketers to increase the VA adoption and makes contributions to the literature.

There are several studies that have examined the factors that determine users’ attitude to adopt eGovernment services. However, there are not many studies that have explored what makes users continue to use these services. The purpose of this paper is to identify the most salient factors that influence users to continue to use eGovernment services in India. To achieve this, the paper examines the role of confirmation and satisfaction in influencing citizens’ attitude leading to intention to continue using eGovernment services. In order to investigate the key factors that affect an individual’s use of Information and Communication Technology (ICT) within the context of electronic government, a framework combining Expectation Confirmation Theory and Technology Acceptance Model is used to investigate satisfaction and continuity of use of eGovernment services.

Though technological advancements offer a host of emergent opportunities, they also introduce fresh challenges for organizations. To overcome these challenges, organizations need to be sensitive to the evolving business needs and growing expectations of the customers. In this research-in-progress paper, through the case of GS1, we demonstrate that even for a seemingly stable business, there is a need to continuously evaluate the influence of changes in the situating technological environment, which may lead to changes in the societal demands. GS1, a non-profit organization that develops and maintains global standards for business communication is redefining itself in the face of e-commerce impacted supply chains and evolving customer needs. We posit that organizations need to espouse a service dominant logic mindset, which can help them continually assess and redefine their business models to address the evolving ecological requirements. Our ongoing work aims to identify a set of lessons for firms to tackle this perpetual challenge.

Agriculture in India is prone to a lot of uncertainties and different types of risks. Crop insurance is a mechanism hedge against such contingencies. Though many governments in the past had introduced various crop insurance schemes, the penetration rate was shallow, and there were no private players. Thus, the current Govt introduced PMFBM, which can be a game-changer. Karnataka went a step ahead and implemented an end to end technology platform – Samrakshane - to promote and manage the crop insurance. Samrakshane, being an innovative technology platform enabling the complete automation of crop insurance adoption. The objective of this study is to understand and measure the innovation adoption impact of the crop insurance scheme. In this short paper, we present a conceptual outline of our ongoing research.

IT operations (ITOps) is a key function in IS organisations. To manage and ensure the availability and reliability of the plethora of new/emerging and complex technologies that organisations increasingly adopt, ITOps had undergone significant changes and is currently going through major transformations. This paper assesses the current status of ITOps research. It reviews the literature across the AIS basket of eight journals, specifically over the last four decades. The analysis highlights the key areas in need of updating and research effort. The paper concludes that ITOps is ripe for further research and it is timely for IS research to update its knowledge base on the management of IT including ITOps, considering the significance of emerging technologies in the IT landscape.

With the rapid adoption of homeworking by organisations across the world owing to Covid-19, employees have been separated from their informal, social networks in the traditional office space. This paper explores how individuals maintain social interaction with colleagues when working remotely. A diary study technique was employed and snowball sampling was used. Initial results from the diaries of 29 participants are presented. The findings highlight various challenges that homeworkers face, including task-related inefficiencies relating to technology-enabled communications in the absence of face-to-face interaction. The paper ends by briefly highlighting how the study analysis will proceed.

Nonetheless, the many developments occasioned by and through information and communication technology (ICT), the plethora of debates on what ‘development’ means persist. Researchers, practitioners, policy makers and stakeholders of ICTs for Development (ICT4D) have a good understanding of ICT4D, yet their views differ when it comes to the ‘development’. The ICT (as an entity), do not get interrogated well enough as a research reality or phenomenon in itself. Differing opinions persist. Without a common understanding, diffusion and adoption of ICT4D will continue fragmented. In continuing the conversation, one area to reimagine is the ICT and digital technologies in ICT4D or information systems. This paper revisits Walsham’s (“ICT4D research: reflections on history and future agenda”) 2017 paper, as a backdrop to elucidate the emancipatory ethos of critical realism and introduce the conceptual CIPPUA model. Through explanatory stratified ontological review of the position, nature and identity of digital technologies/objects, this paper contributes to the ongoing discourse on critical realism in digital development, especially the connection of ICT to development. More so, this paper with the conceptual CIPPUA model, contributes in part to the discourse of operationalizing critical realism in practice.

Organizations have adopted Enterprise Architecture (EA) for managing their IT-landscape and ensuring coherence among projects and activities. There is much work about approaches, methods, and tools for EA based on the assumption that their use will create business value. However, the failure of many EA efforts results in the need to investigate the factors influencing EA failure in practice. In this paper, we used a literature review to identify ten EA failure factors. Then we employed the grey-DEMATEL method to explore and analyze the influence of the ten EA failure factors based on the input of five EA experts. The result shows that failure factors are not in isolation, and they can be divided into either causal or effect factors. The factors do not have equal importance but differ in the levels of influence. For the causal factors, the ranking from most to least important is the inability to handle complexity, lack of proven EA methodology, lack of EA knowledge, lack of communication, and lack of tools. For the effect factors, the factors are a lack of support, too high effort, lack of motivation, parallel processes, and unused artifacts. We recommend practitioners to pay more attention to the five causal factors in their EA efforts. Further research is needed to generalize the findings, to understand the dependencies among factors, and to take into account situational dependency of EA failure.

Nature-inspired algorithms are problem-solving tactics and methodologies and have been gaining much recognition for their competence. These algorithms have gained massive acclaim in the last few years to solve puzzling real-world (Nondeterministic polynomial-hard and Nondeterministic polynomial-complete) problems and solve complex optimization problems and functions whose exact solution doesn’t exist. These are the algorithms that are inspired by natural processes and phenomena. The nature-inspired algorithm can be categorized based on some biological processes or any other phenomena which happen in nature. So, in this paper, we have classified some popular nature-inspired algorithms such as Genetic algorithm, Simulated Annealing, harmony search, Black hole, and many more. Based on four parameters from which the first parameter is Subject from which they were inspired. The second parameter is the optimization technique i.e. Stochastic or Deterministic. The third parameter of our classification is the number of solutions they maintain i.e. population-based or trajectory-based. And the last parameter of this categorization is memory i.e. algorithm is memory-based or memory less.

This study tries to explore how social media had been used for public administration activities across the global by analysing the tweets tagged with “#government” and “#gov”. On the basis of the literature social media usage by public administration had been classified into five dimensions, namely information dissemination and broadcasting; open transparency; open participation; open collaboration; and ubiquitous engagement. The study collects 296,417 tweets (after cleaning, 174,204 tweets) tagged as “#government” and “#gov” as open government data (as data is available to everyone but managed by private organizations) and this study applies open government data activities and social media analytics to derive the insights. The article also explores how different usage of the social media is effecting sharing and liking of the tweets. The implications of these findings can be important to government of different countries. The article concludes by pointing that social media can be used by citizens for open participation which can subsequently facilitate information dissemination and ubiquitous engagement.

Open Government Data (an innovation in the electronic government enabling public sector information accessible by the public in open formats and the ways to enable such facility) has huge potential to increase transparency, accountability, participation, efficiency in operations, data-driven/evidence-based policymaking, and trust on government institutions. Despite its potential benefits, although a few organizations are proactive and have embraced the OGD movement seriously, still OGD has not been widely adopted in Pakistan which might involve several obstacles that worked against such efforts. Driven by the nature of the research, this study conducted an exploratory field study in Pakistan by interviewing five industry experts in the e-government domain as well as attending a conference, and newspapers. This study identifies eight important antecedents to the adoption of OGD in public sector organizations and proposes future research to test their relationships. As the main theoretical contribution, this study extends organizational behavior toward technology diffusion. The findings of this study incite government, policymakers and managers to consider the factors and prepare future strategies on OGD developments.

In several countries of the global South, hope relies heavily on technologies for economic, social, and cultural development. Multipurpose Community Telecentres have been carefully presented to disadvantaged communities as a hub to facilitate the economic and social empowerment and increase the technological skills of youths. Although several initiatives have emerged, few studies have focused on their impact in Sub-Saharan Africa. To fill the gap observed in the existing literature, this study aims to explore the key determinants associated with the acceptance of telecentres in rural municipalities in Cameroon. It intends to contextualize the Unified Theory of Acceptance and Use of technology model, branching out constructs which are specific to the study environment. This qualitative study, adopting a mixed research methodology, presents the results of needs analysis study for municipal digital services in rural areas. In addition, the study provides additional answers regarding the digital divide in rural Africa.

Identification systems are vital in improving efficiency and enabling innovation for public and private-sector services, such as greater efficiency in the delivery of social safety nets and facilitating the development of digital economies. With all these benefits along with the rapid improvement in the technology has led many countries to adopt a new foundational digital identity system (DIS) or retrofit the existing paper-based identity system especially in the developing economies. Apart from all these benefits, DISs has also been criticized for issues related to the security, privacy, surveillance and exclusion of people from various services they are entitled to. Considering the significant impact of DIS on the people, it is necessary to have an evaluation framework that could help understand the suitability of a DIS in a particular context. In this study, we propose a conceptual evaluation framework specifically for DISs based on the processes followed, regulations and technologies employed.

A typical e-Government project will undergo an impact assessment several times in its lifecycle. An important aspect of impact assessment is to measure benefits from the project, especially to the end-users. Often, when these benefits are measured, projects have positive scores, but when it comes to voluntary adoption of e-Government project on a large scale, the rate of adoption is slow. Not all the people who use it for the first time are willing to continue with the digital alternative. In this paper, we argue that one source of such contradictory findings is a unitary view of an important construct – benefits. In the extant literature, the construct – benefits - has been widely regarded as a single-dimensional construct. We propose that a multi-dimensional approach to measuring benefits will facilitate the respective government department to reach out to all the target audience with significant relative advantage. In order to identify the dimensions of benefits, we use the theory of Maslow’s hierarchy of needs. Accordingly, we propose five dimensions of benefits which would cater to different kinds of individuals among the target users.

The UAE’s banking industry has emerged as one of the most dynamic in the region, and the largest financial center in the Middle East. The industry has been confronted with ‘digital disruption’, ‘digitization’ ‘digital banking’. The key reasons for digitization in addition to competitiveness include improved banking efficiency, attracting and retaining customers, improving analytics, launching innovative services and enhancing customer experience. However, organizations struggle with the implementation of digitalization initiatives and fall behind in achieving their plans. There is very limited literature written on the research to study impact of digital transformation on the retail banking industry in the UAE. Moreover, it is based on secondary research. This study aims to identify the variables that drive digital transformation and study the impact of digital transformation in Retail Banking Industry in the UAE. The results of the study show that customer needs, technology, regulation, business processes, skills and competencies impact digital transformation and should support retail banks in developing a theory of digital transformation in their journey to excel in a competitive marketplace.

Prior literature has established that information seeking is crucial part of the process of online shopping. However, information seeking behaviour is usually treated as a black box and there is a dearth of studies about the information seeking behaviour of consumers during online shopping and its influence on online purchase intention. Our study aims to fill that gap by employing Ellis’ model of information seeking behaviour as our theoretical lens to understand this phenomenon. Ellis’ model has eight features or stages related to information seeking behaviour: starting, chaining, browsing, differentiating, monitoring, extracting, verifying and ending. We first conducted a survey to measure these features. We then constructed a measurement model to validate the survey instrument. We found that only three features passed the validity and reliability tests which were starting, monitoring and verifying. We then used structural equation modeling for hypotheses testing and found a significant relationship only for the verifying feature. In other words, only verifying was positively associated with online purchase intention.

Numerous studies explore that, in current era, Banking and Information Technology (IT) services sector, in various developed/developing countries, either adopted or in the phase of Green IS adoption. Since countries, round the globe, have recognized the significance of Green IS and its impact over environment, it is now vital for every industry to implement Green IS and contribute in environment sustainability. This Research-in-Progress paper observes the effective Green IS implementation through carefully consideration of different Motivational, Technological, Organizational and Environmental factors. Subsequently, in this paper 16 different factors are revealed via Literature Review and Experts opinion and a research model is developed using Technological-Organizational-Environmental (T-O-E) theory and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework for effective Green IS implementation, which will be helpful for academician and practitioners in this field.

Technology has sped up the innovation effort in the automobile industry. Further to this automobile innovation such as intelligent climate control, adaptive cruise control, and others, we find in today’s vehicles, it has been predicted that by 2030, there will be driverless vehicles, of which samples are already on the market. The news and the sights of these so-called driverless vehicles have generated mixed reactions, and this motivated our study. Hence the present study focuses on a dataset of tweets associated with driverless vehicles downloaded using the Twitter API. Valence Aware Dictionary and sentiment Reasoner (VADER), a lexicon and rule-based sentiment analysis tool were used in extracting sentiments on the tweets to gauge public opinions about the acceptance and adoption of the driverless vehicles ahead of their launch. The VADER sentiment analysis results, however, show that the general discussion on driverless vehicles was positive. Besides, we generated a word cloud to visually analyze the terms in the dataset to gain further insights and understand the messages conveyed by the tweets in other to enhance the usage and adoption of driverless vehicles. This study will enable self-driving vehicle technology service providers and autonomous vehicle manufacturers to gain more insights on how to transform the transportation sector by investing in research and technology.

In the past few years, Internet of Things (IoT) has led to multiple devices interacting with humans as well as other devices, helping create a huge network of these entities that share data on a regular basis. The use cases of IoT technology have become synonymous with devices used to enable “Smart Home”, wherein various applications inside the house are usually controlled using smartphones or smart speakers. These Smart Speakers and Smart Phones are enabled using Voice Assistants developed by various Tech Giants from all over the world. Various Tech Companies have developed their own Voice Assistants that are integrated in multiple devices, ranging from Smart phones, Smart Speakers to Smart TVs. Using their ability to connect to the Internet and the eco-system formed by the Tech Giants, Voice Assistants are helping to improve the quality of life.Despite the extensive coverage in media, review of Literature showed that no such research about adoption of Voice Assistants by Indian Consumers has been conducted. There hasn’t been any study that evaluates factors that influence the adoption of Voice Assistants by Indian Consumers. That is the gap which this research addresses and tries to fill. Through this study, we aim to build a model which finds out Indian consumers’ acceptance of Voice Assistants for this technology to reach commercialisation.The purpose of this study is to explore the user attitudes, satisfaction, and other factors governing and encouraging or discouraging the intention of Indian Consumers to adopt Voice Assistants. Through analysis, we can find out the inconveniences of adoption and usage process and propose the direction of product improvement.

Baby products, our area of interest, are very sensitive products which are used by tender infants and little kids. Therefore, parents exercise great care while purchasing them and only go for the best, which explains our need to study the online purchase intentions of baby products. Working women with financial resources of their own and coupled with availability of huge information on the internet have empowered them to make informed decisions while purchasing for their babies and kids. Most of the baby products are available online and customers now prefer to buy the products through online shopping websites. But product sales through online particularly relies on the trust of the company and the company website. This article tries to analyze the appearance of the website, features and other related factors of the online shopping portal and their influences on making purchase decisions for baby products among the customers in Chennai. In this regard, data were collected from 174 respondents, through a survey method, by using structured questionnaire and the judgemental sampling method was adopted. Collected data were analysed and results revealed that trust and usability factors were significant. The results also revealed the most important factors for designing a website for achieving more customer satisfaction.

In the automobile industry, an electric car is a vehicle impelled by the electric motors utilizing energy stored in refillable batteries, as a replacement of diesel or petrol engine. The electric car has predicted to rule the Indian market in the next few years. The study aims to know the factors which make interest in buying the electric car. Integrating the Technological acceptance and diffusion of innovation model the present research proposed extended technology acceptance model. The data were collected from 600 non-commercial car owners to test the proposed model. The findings demonstrate the perceived usefulness, perceived ease of us, and attitude are positively influence the customers’ interest to buy the electric car. The relative is one of the innovation factors which have a positive effect on interest to buy the electric car. The result also shows that majority of customers are interested to buy Indian brand cars which will have a low-price range.

A marketing study is not complete without studying the buyer’s behavior. With more people moving to the digital platform for day to day purposes, digital marketing is gaining momentum like never before. Hence this study aims to study the effect of digital marketing on Exploratory Buying Behavior Tendencies (EBBT) of the consumers. Since the ambit of digital marketing ambit is huge, only internet marketing, mobile marketing, E-Mail marketing and Social media marketing were taken for the study. Data from 110 respondents were collected and the results were analyzed using weighted average, correlation and regression. The study concludes that out of the four components in digital marketing, internet marketing has the highest influence on consumers as they buy products.

Globally, there is a disruption in the financial sector due to the emergence of FinTech companies. The industry is changing in the way it functions. Hence, it is important to understand the changing landscape in the Indian context. The key players identified in the landscape are regulators, traditional banks, and FinTech companies. The objective of the study is to understand in Indian context 1) Role of key players in the changing landscape and 2) Influence of FinTech companies on the ecosystem. This study observes that there will be an emergence of the multi-dimensional relationship among the participants in the ecosystem and the scope of regulation will widen. This paper also observes that FinTech has a positive influence on meeting Sustainable Development Goals and the challenges of regulation considering this larger goal will vary based on the risk involved in business models and products, with technology playing a pivotal role.

Technology diffusion has often been triggered unintendedly by crises and disasters, as witnessed in several cases including the demonetization cashcrisis surging mobile payment adoption in India. However, once the shock waves induced by the crisis event weakens over time, there exists a void that questions the sustenance of the technology whose diffusion was a ripple effect of the shock. This is seldom explored by the literature that focuses on the immediate aftermath of the crises. We address this limitation by examining the cash withdrawal patterns from ATMs in India post-demonetization for a continuous period of three years. The results provide strong empirical evidence to support our claims towards the dampening of demonetizations’ ripple effect on mobile payments. The theoretical contributions of the study add further to the existing literature on technology diffusion and technology adoption post-crises with a focus on the digital payment systems. The findings have implications for policymakers and government concerned with the digital economy, with cash emerging as an enemy overshadowing the growth of digital payment methods.

Despite the several advantages, adoption of the FinTech services vary across individuals in different countries. This study proposes a model to examine how the country level ICT competitiveness, demographic and socio-economic factors influence the FinTech services diffusion in a country. In this study, we use Generalized Linear Mixed Model to analyze both the individual-level and country-level variables to explain the variation in the probability of individuals adopting FinTech services while accounting for the variance at each level. National ICT competitiveness especially political and regulatory environment, Infrastructure, ICT usage by firms demonstrated a stronger influence on the adoption of FinTech services. In addition, demographic variables and socio-economic status also demonstrated stronger evidence in explaining the variation in the adoption of FinTech services.

Technological advancement has been playing an essential role in the development of any sector. Even mutual fund industries are more exposed to the advent in the field of financial technology. Advancement in the FinTech arena has been witnessed in every aspect of the mutual fund industry. Asset Management Companies (AMC’s) are started using robotics to increase their efficiency of fund management and ease their transaction processing or customer servicing. Investors can access the information online, track or redeem their investment any time of the day. Unified Payments Interface (UPI) and Electronic Clearing Services (ECS) are the online modes of payment facilities adopted by most fund houses. Association of Mutual Fund India (AMFI) has been consistently deploying considerable efforts to ensure that every investor has sufficient information to make informed decisions. Even the Know Your Customers (KYC) process has been completed online and more superficial than earlier days. In this paper, an effort has been made to understand the importance of Mutual Fund investments, the awareness among different investors about the mutual fund, the impact of Digitalization on mutual fund investment, and the information availability to every investor related to the mutual funds.

Mobile payments in India and Kenya had grown tremendously in the last decade and this paper intend to analyze the trend, progress and achievements of both the countries in mobile payments especially focusing on socially and economically backward sections of the society. Mobile payment banking system in Kenya exists since 2007, even before digital era began in India. Payments banks in India, as a concept, envisioned in 2014, nevertheless showing lot of promising growth because of the mobile penetration in India. There are lot of similarities in the intention of both the initiatives, as they mainly focus on financial inclusion for economically poor society and rural population. M-Pesa by Safaricom had made an implausible change in Kenya, improving the labor class and rural people access to banking, reducing time spent on transactions and simplifying the process by just making cell phones as their banks. India until first decade of 20th century, was mainly dependent on postal service for rural areas as banking solution, but now rapidly moving to digital era with payment banks as a mobile enabled banking solution. The paper explains the Indian service providers like Paytm, Airtel, Jio, and Indian postal service who had already established in India were able to move to Payment bank services quicker because of the base they had established in the last decade. The paper utilizes the secondary data information extracted from published reports and reports from Reserve Bank of India, Central Bank of Kenya and other independent organizations like FSD Maps, Statista and GSMA to arrive at a comparison between the payments systems in both the countries.

This study investigates the evolving nature of causal mechanisms driving the evolution of a digital platform. By drawing from a rich dataset representing the evolution of a thriving FINTECH platform (i.e. HP-EFS) over a period of 7 years, we propose to a) identify the causal mechanisms responsible for its evolution, and b) further understand the dynamic nature of these causal mechanisms. We integrate the existing literature on Generative Mechanisms with the theoretical streams of Socio-technical systems and Systems theory to address the research question. The contribution of this paper is to propose and elucidate a class of causal mechanisms, ‘Corrective Mechanisms’ for future IS research. We anchor this approach amongst existing IS ‘Generative Mechanisms’ research and argue it’s utility in complementing existing research when explaining digital platform evolution.

With changing banking environment, the efficiency of the operational function of bank is of critical importance and needs timely watch. Apart from measuring the operational performance of banks using DEA approaches, the banking sector today is more inclined to predictive analytics to identify their future performance and improve their competitiveness well in advance. In this sequel, the present paper proposes hybridisation of Data Envelopment Analysis and Artificial Neural Network Approaches for operational performance measurement and prediction for Indian banks using the five-year (2015 to 2019) dataset. Non-oriented non-radial DEA model is adopted in the present study, attempting to provide decision-makers the discretion to identify slacks in performance by maximising outputs and minimising inputs. This can identify causes of inefficiency and suggest necessary steps for improvement. In addition to DEA findings, the paper performs prediction task for obtained efficiency scores. Finding of will be advantageous for policymakers, managers of banking industry for predicting future operational performance of banks until they are able to make required changes for its improvement.

The potential of Health Information Technology (HIT) to increase the quality of healthcare delivery is well documented but improvements can be hindered if physicians and nurses resist HIT. However, the technology is still facing resistance. The literature suggests that user resistance to HIT is predicated on their perception of its impact. However, we do not fully understand how users’ perception is formed. In response, this study investigates the antecedents of perceived threats by examining the organisational factors, the personal traits of the user, HIT-related factors, and the factors related to the interaction between physicians and nurses and the organisation that lead to perceived threats. This study uses a case study of a military hospital to understand the antecedents of perceived threats and user resistance. The findings of the study indicate that dissatisfaction and risks are the main components of perceived threats of HIT for physicians and nurses. Furthermore, the study suggests that the antecedents of perceived threats are: system incompatibility, management support, related knowledge, and lack of trust. This research will contribute to identifying the core reasons for resistance and will lead to a better understanding of the phenomenon, hence, can help organisations solve the root causes of the problem.

Alzheimer’s Disease (AD) is the most common form of dementia that can lead to a neurological brain disorder that causes progressive memory loss as a result of damaging the brain cells and the ability to perform daily activities. This disease is one of kind and fatal. Early detection of AD because of its progressive threat and patients all around the world. The early detection is promising as it can help to predetermine the condition of lot of patients they might face in the future. So, by examining the consequences of the disease, using MRI images we can get the help of Artificial intelligence (AI) technology to classify the AD patients if they have or may not have the deadly disease in future. In recent years, AI-based Machine Learning (ML) techniques are very useful for the diagnosis of AD. In this paper, we have applied different machine learning techniques such as Logistic Regression, Decision Tree, Random forest classifier, Support Vector Machine and AdaBoost for the earlier diagnosis and classification of Alzheimer’s disease using Open Access Series of Imaging Studies (OASIS) dataset, in which a significant performance and result gained on classification with Random Forest classifier.

The coronavirus (COVID-19) epidemic is the cause of several disasters on human health and livelihoods in many countries around the globe. Even though everyone is at risk of infections regardless of ethnicity, income, age, and political affiliation, the consequences of the epidemic will weigh enormously in the global south, at the level of the very fragile sanitary architecture, the economic, social and cultural fabric. This study examines the key determinants of social media adoption and the consequences of their use in managing a public health crisis of International Concern like COVID-19. We propose a theoretical framework resulting from a combination of several approaches, such as the Health Belief Model, the Technology Acceptance Model and the theory of Social Influence. Moreover, we use a mixed research method to carry out various investigations in our study. The findings and recommendations of this research will serve as a research base for government agencies, health organizations and associations in the reflections and strategic actions being implemented to effectively fight against COVID-19 and equip marginalized communities with efficient information through the use of social media.

Recently, Artificial Intelligence has started showing up in the realm of health care innovations with researchers exploring its potential for healthcare organisations. Since healthcare possess industry specific features, the context and challenges of exploring AI adoption in healthcare is different than other industries. This study intends to conduct grounded theory to review the strategic, cultural, environmental and operational factors towards adoption of AI technology in Indian hospitals. The study uses purposive sampling to conduct semi-structured in-depth interviews of the decision makers of various healthcare organizations across the country. The present study would contribute to the existing literature on the impact of disruptive technology on healthcare as it would be a comprehensive study assessing the determinants of adoption in hospitals.

Advancement in digital technology and the need to provide alternate healthcare delivery channels to individuals in developing countries has led to the boom in mobile Health (mHealth). A wide range of mHealth applications (apps) and services are available today to combat the maternal and newborn health disparities in India. Yet, there is scant research in understanding the predictors of pregnant women’s adoption towards pregnancy apps in developing countries. The objective of this study is to identify the most significant predictors influencing behavioural intention to use pregnancy apps. To meet this objective, a conceptual model was developed and empirically tested by extending UTAUT with relevant constructs namely personal innovativeness in IT and perceived risk. A conceptual model along with the hypothesized causal paths among the constructs are empirically validated with the help of structural equation modeling using Smart PLS 3.0 with a sample of 220 pregnant women. Results showed that intention to use pregnancy apps by women was predicted by six influencing factors: performance expectancy, effort expectancy, facilitating conditions, social influence, personal innovativeness and attitude. Perceived risk had no significant effect on the behavioural intention to use pregnancy apps.

Customer Relationship Management is a critical aspect for all service industries and extremely important in aviation industry. With the changing aviation scenario, the travel industry is facing more challenges. Identifying and retaining the profitable customers is very essential for survivability. Customers are well informed about the services that offer them the maximum value proposition and retaining high value customers is very challenging. Airlines currently use many techniques for CRM, but there are drawbacks in the system which can be complimented with emerging technologies. Artificial intelligence & Internet of Things are evolving domains, which have gained lot of importance during the last decade, predominantly due the capacity of systems to gather, store, process & transfer huge amount of data. This paper is indented to improve CRM with the prudent use of AI & IOT and involve airports in implementing smart CRM, ensuring long term profitability and sustained revenues.

The urbanization trend and the prevailing energy crisis in India, makes it important to study the potential of IoT technology in the Indian buildings market. This research work proposes a systems dynamics model of IoT based climate control systems diffusion in Intelligent Buildings. The modeling process leverages the generic Bass model of technology diffusion and augments it with causal loops based on the enablers and barriers to IoT diffusion in buildings identified through literature review. The model encompasses the technological, social, financial, business, regulatory and environmental aspects of the system, and uses stock-flow concepts to represent their dynamic interplay. The model will be useful to value chain players in the construction sector by providing them support for strategic decision-making concerning market entry and new product development. It will also help policy makers to assess industry readiness to tackle the prevailing energy crisis and devise strategies to mitigate it.

Occupant behavior influences energy savings in intelligent buildings over and above the advanced technologies deployed. IoT based interconnected sensors, along with personal devices such as mobile phones, wearables and virtual assistants, can assist building management systems with occupant behavior information paving the way for personalized comfort coupled with energy savings. This work investigates the occupant adoption of such an IoT climate control service in the context of Indian office spaces by examining four theories viz., TRA, TPB, TAM and VAM. The results indicate that TAM and VAM have higher explanatory power among the models considered. Further, TAM’s constructs 1. Perceived benefits, viz., improvement in comfort, productivity and wellbeing, and 2. Perceived ease of use, viz., access convenience to climate control in offices are identified as the most significant causal constructs. The results can pave way for IoT players to formulate business and technology strategies for product management and targeting specific customer segments.

The Internet of Things (IoT) continues to evolve amongst the recent technologies which has a huge growth potential in terms of deployments and usage. The revenues on IoT is already nearing three trillion US dollars by 2020 despite COVID turbulences and the peak scale is expected to be touched by 2022. Literature says there would be 50 billion+ devices consuming 2 Zettabytes of data bandwidth. While more research is ongoing on the technical coverage of IoT and its features, less attention is paid to the behavioral aspects about the perception and usage of the IoT services. This paper makes an empirical study towards the influence of Trust on the acceptance of IoT and the adoption of IoT Services, with an update on UTAUT model. With the survey from 100+ IoT users applied with SEM reveals the significance on the Trust on IoT Provider.

‘When aviation takes off again, we (industry stakeholders) must ensure it is on a more sustainable flight path, COVID-19 gives us a chance to design an aviation industry fit for the future’– World Economic Forum [14]. IoT in Aerospace & Defense produces more smart and connected products which offers better operation & control, material & energy management, traffic planning, staff & passenger information management, data analytics, and others. The longer life cycle in the A&D sector presents fewer opportunities to introduce capability advancements to stay ahead of the competition. Strong product strategy decisions can make the difference between success and failure in winning business during the available window of opportunity. Developing Product strategy and identifying suitable product in quicker time is more relevant in the current pandemic scenario with reduced spending for research and development initiatives. The investigative research is targeted towards developing a design space exploration methodology to develop IoT products in A&D. This methodology helps in identify the gaps in the existing methods and improvement opportunities to develop the IoT products, quick launch in the market to stay ahead of competition and to stay in the market. This methodology could help A&D players to develop optimized product development strategy by identifying the IoT Values which yields customer benefits with qualitative early decisions in IoT product development cycle.