Industrial revolution 4.0 makes business competition more challenging and will impact the instability of the company's financial performance. Dynamic environmental conditions make it difficult for companies to make predictions in making decisions. Investing in information technology (IT) is one way for companies to maintain financial stability and competitive advantage in dynamic competition. Resource-Based Theory (RBT) explains that information technology (IT) is a resource that can create a competitive advantage for the company. This study aims to examine the moderating role of dynamic industrial environments and IT strategic emphasis on the relationship between a lag effect of IT investment and firm's financial performance volatility. Using the data of companies listed on the Indonesia Stock Exchange (IDX) for five years starting from 2013-2017, the method used to estimate the research model's parameters is the generalized method of moments (GMM) approach. The results show that the industrial environment and the emphasis on IT strategy have a role in moderating and strengthening the relationship between the time lag in IT investment in reducing the firm's financial performance volatility.

The information and communication technology (ICT) value creation process is inherently unobservable. In addition to the direct effect of ICT on productivity, some information or knowledge can create value through other knowledge activities. In this paper, we study the impact of ICT on productivity through R&D. We tested the mediating effect of R&D between ICT and productivity using panel data from 47 US industries from 1987 to 2013 from the Bureau of Labor Statistics. The results show that R&D partially mediates ICT and productivity. That is, ICT directly increases productivity, and some of its effects can be realized through R&D. Recipients who acquire knowledge through ICT have to interpret codified ideas and apply them to practice. The increased absorptive capacity that can be developed through R&D improves interpretation ability, allowing employees to share more complex ideas. Thus, ICT helps people to effectively communicate, but some information and knowledge can be realized and applied through R&D knowledge activities. This is the first study empirically examining the process of ICT value creation through R&D. It also provides practical guidelines for knowledge management, such as making decisions about ICT and R&D investments that are better done concurrently rather than individually to maximize their impact on productivity.

Indonesia has the largest potential mobile payment (m-payment) market in Southeast Asia. The government has realised this through Government's National Non-Cash Movement to increase its adoption. This is then followed up by advocating its adoption massively. However, for this movement to be a success, ensuring its continued use is critical. Various studies have attempted to contribute to this issue. Incorporating game elements into the application that brings benefits and satisfaction to its user is envisaged as one of the most feasible ways. This study, therefore, sets out to investigate the effects of gamification on the m-payment application, which drive the intention to continued use by employing the Uses dan Gratification Theory (UGT). A total of 826 m-payment users were gathered to be analysed using Structural Equation Modelling. The results show that utilitarian, hedonic and social gratifications have significant effects on the continuance usage intention of m-payment. Theoretically, this study contributes to the literature by showing that gamification applied in the payment significantly affects the m-payment continuance usage intention. Practically, this research informs the m-payment providers to maintain the gamification elements in their applications to ensure their sustainable use. Limitations and future research directions are also discussed.

With the development of deep learning techniques, text mining is producing breakthrough performance improvements, promising future applications, and practical use cases across many fields. Likewise, even though several attempts have been made in the field of financial information, few cases apply the current technological trends. Recently, companies and government agencies have attempted to conduct research and apply text mining in the field of financial information. First, in this study, we investigate various works using text mining to show what studies have been conducted in the financial sector. Second, to broaden the view of financial application, we provide a description of several text mining techniques that can be used in the field of financial information and summarize various paradigms in which these technologies can be applied. Third, we also provide practical cases for applying the latest text mining techniques in the field of financial information to provide more tangible guidance for those who will use text mining techniques in finance. Lastly, we propose potential future research topics in the field of financial information and present the research methods and utilization plans. This study can motivate researchers studying financial issues to use text mining techniques to gain new insights and improve their work from the rich information hidden in text data.

With the advances in technology and the trend towards increased computer-based experiential learning in education settings, business games are being increasingly used by business educators. This article utilizes Toulmin's Sensemaking Framework to investigate the sensemaking process of business professionals to reveal how they consciously reason about the value of business games for learning complex business concepts and principles. Using the analysis of responses from 43 business professionals, our study identifies key areas where business professionals find value in business games and the limitations of using business games. First, business games are found to be an effective tool when teaching practical business skill sets to business professionals. Second, business games enhance the overall learning process in professional business training. Third, despite the advantages, some pitfalls in applying business games to practice are found. We also found sub-themes, claims, and argument patterns of how business professionals evaluate the value of business games through a grounded theory qualitative analysis method. Analysis results show several ground-warrant patterns exist in the arguments on values of business games including general principle - causal reasoning, personal experience - generalization, and personal projection - generalization. With these findings, we believe this paper contributes to the theory and practice of business game design, development, and the game playing and learning process.

The Digital Government landscape is changing to reflect how governments try to discover innovative digital solutions, and how they transform themselves in the process. In addition, with the advent of information and communication technology (ICT), e-governance became an essential part of the government. Among the services provided by the Korean government, the Minwon24 online portal is the most used one. However, it has some processing limitations, namely: (1) it provides a cumbersome document authenticity service; (2) people cannot know what happened even if the agency handles the documents arbitrarily. To address the issues outlined above, blockchain processing can be a good alternative. It has a tremendous potential in that it has maximum transparency and a low risk of being hacked. Resource management is one of the areas where blockchain is frequently used. The present study suggests a new model based on blockchain for Minwon24; the proposed model is a type of resource management. There are three participants: issuer, owner and receiver. The proposed model has two stages: issuing and exchanging. Issuing is creating civil documents on the database, which is BigchainDB in this study. Exchanging, the next stage, is a transaction between the owner and the receiver. Based on this model, the actual program is built with the programming language Python. To evaluate the model, the study uses various criteria and it shows the excellence of the model in comparison to others in prior research.

Home theft and burglary are prevalent in Dhaka city. Internet of things (IoT), in contrast, is commonly recognized as among the most advanced home security systems. However, the factors that attract young people to use IoT for household security have yet to be examined. Consequently, the purpose of this article is to validate the attitude-social influence-self-efficacy (ASE) model with personal innovativeness and perceived trust. We collected data from Dhaka citizens aged 15 to 24 using a purposive sample technique and 370 valid responses were chosen for the study. According to the analysis, all of our proposed hypotheses were found significant with a 73.6% variance. Furthermore, the effects of attitude and social influence were shown to be the highest and lowest, respectively, and trust and innovativeness were both nearly strong main predictors of ASE. Significantly, since this is one of the few studies in the technology adoption domain using this model, a solid foundation for IoT adoption for security purposes is established.

Although existing studies on e-health have usually focused on e-health services adoption intention, there is a dearth of studies on the barriers that affect e-health services retention intention especially in India. Additionally, although studies have mostly focused on utilizing expectation-confirmation model to understand innovation related barriers, innovation resistance theory (IRT) has been overlooked. As Indian e-health service providers face stiff challenges due to customer's unwillingness to continue using the service, there is a need to bridge the research gap that exists in this context. This mixed-method study, based on responses received from 289 participants and 1154 online negative reviews from e-Health providers in India, examines the barriers from the IRT stance. Results of this study reveal a notable negative association between tradition, value and financial barrier and intention to continue using e-health services. Additionally, continuance intention affects recommendation. The study concludes with various implications and scope for future research.

Artificial Intelligence has been a conceptual area for several decades. It has been studied extensively through experiments by the Information Systems community. When Information Systems supported with Information Technology became all pervasive in business and other allied areas, gradually the advancements in Artificial Intelligence also emerged as innovations across domains. Artificial Intelligence by definition is expected to substitute Human Intelligence, thereby making a huge space for innovation. In fact, all processes effected by human intelligence are liable to be replaced by AI which in itself is a massive innovation space. This paper will study the publication's repository (Scopus and Google Scholar from 1983 till 2021) in the area of Artificial Intelligence and innovation, then analyze the trend to gain insight into the evolution of AI as a vehicle for innovation.

With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information-both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people's willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study.