In today's turbulent business landscape, a firm's ability to explore new IT capabilities and exploit current ones is essential for enabling organizational agility and achieving high organizational performance. We propose IT exploration and exploitation as two critical organizational learning processes that are essential for gaining and sustaining competitive advantages. However, it remains unclear how the emerging cloud-based IT architecture affects an organization's ability to explore and exploit its IT capabilities. We conceptualize modularity and virtuality as two critical dimensions of emerging cloud-based IT architecture and investigate how they affect IT exploration and exploitation. We test our hypotheses using data obtained from our field survey of IT managers. We find that modularity is positively associated with both exploration and exploitation whereas virtuality is positively associated with exploration, but not with exploitation. We also find that the effect of modularity on exploitation is stronger than its effect on exploration.

What is individuals' privacy notion, and does it change with the social roles taken up by them? We explored these questions using a qualitative interpretive research approach. We found that individuals have mixed notion of privacy. Individuals view privacy either as a commodity or as a control. Further, we found that an individual's privacy notion is a function of their social role within the society and their privacy preferences. Our research points to the importance of expanding the notion of privacy to encompass a broader understanding of privacy preferences. We theorize our findings using social penetration theory and presents a privacy model which provides the logical framework for interpreting people's views on privacy.

We examined the effects of socio-demographics and personality traits on online privacy concern. We investigated this relationship in general and across different Internet functions. Using a large, diverse, population-representative sample (N = 5,242), we found that females, educated, and wealthier individuals tend to be concerned with online privacy to a greater extent. Among personality traits, agreeableness and conscientiousness were generally associated with an increased probability of being concerned with online privacy. These results imply that socio-demographics and personality traits provide explanatory insights into online privacy concern.

In this research, we study the impact of the factors that predict consumer loyalty of mobile payments and mobile wallets usage in Thailand. We developed a research model based upon previous findings where we propose and examine four constructs of personal innovativeness, consumer attitudes, consumer satisfaction and loyalty. In general, all of the hypotheses were supported by variables related to consumer loyalty with mobile wallet usage. Personal innovativeness analyzed due to its potential mediation effect on loyalty, both from attitudes and from satisfaction. It was found that personal innovativeness was a mediator construct in predicting consumer loyalty. The overall model provides value to the mobile payment and mobile wallet application providers in term of improving service efficiency and business strategies to secure Thai consumer loyalty.

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

Purpose: The application of IoT is finding continuous acceptance in our daily lives, particularly, smart speakers are making life easier and convenient for consumers. This research aims to develop and test an integrated model of factors influencing consumer's adoption of voice-enabled IoT devices. Design/methodology/approach: Based on the VAM, an integrated voice-enabled IoT device adoption model is proposed. Gender differences on five constructs relating with perceived value (perceived usefulness, perceived enjoyment, perceived security risk, perceived technicality and perceived cost) was also examined through PLS-MGA technique. The usage experience of consumers was also controlled in the integrated VAM. Findings: Result shows that Perceived-Usefulness, Perceived-Enjoyment and Perceived-Cost have a strong effect on Perceived-Value. However, Perceived-Technicality and Perceived-Security-Risk are non-influential and have no significant effect on PV. Additionally, Perceived-Value and Social-Influence plays a significant role in predicting adoption intention. Gender differences also exist in consumers perception of usefulness, enjoyment and cost. In comparison to the basic value-based adoption model, the integrated model provides more insight on consumers adoption of voice-enabled IoT devices. Originality/value: Using an integrated model, this study is one of the first scholarly attempt at modelling the influential factors for adopting smart speakers i.e., voice-enabled IoT devices, with implications for improved adoption.