The data produced and collected by organizations represents both challenges and opportunities for the modern firm. Business intelligence and analytics (BI&A) comprises a wide variety of information management technologies and information seeking activities designed to exploit these information resources. As a result, BI&A has been heralded as a source of improved organizational outcomes in both the academic and practitioner literature, and these technologies are among the largest continuous IT expenditures made over the last decade.Despite the interest in BI&A, there is not enough theorizing about its role in improving firm performance. Scholarly investigations of the link between BI&A and organizational benefits are scarce and primarily exploratory in nature. Further, the majority of the extant research on BI&A is techno-centric, conceptualizing BI&A primarily an organizational technical asset. This study seeks to explicate the relationship between BI&A and improved organizational outcomes by viewing this phenomenon through the lens of dynamic capabilities, a promising theoretical perspective from the strategic management discipline. In so doing, this research reframes BI&A as an organizational capability, rather than simply a technical resource. Guided by a comprehensive review of the BI&A and dynamic capabilities literature, as well as a series of semi-structured focus groups with senior-level business practitioners …

In recent years, social media has experienced tremendous growth in the number of users. Facebook alone has more than 1.3 billion active users and Twitter has attracted over 600 million active users. Social media has significantly changed the way humans communicate. Many people use social media to keep in touch with family and friends and receive up-to-date information about what happens around the world. Politicians are using social media to support their campaigns. Use of social media is not restricted to individuals and politicians. Businesses are now using social media to promote their products and services. Many companies maintain Facebook and Twitter accounts to keep in touch with their customers. Consumers also use social media to receive information about products/services. Online product reviews are now an important source of information for consumers. This dissertation aims to address one fundamental research question: how do individual differences among users lead to different levels of performance on social media? More specifically, this dissertation investigates the motivations of use and the predictors of performance in the context of social media. We utilize sentiment mining to predict performance in different types of social media including information diffusion in Twitter and helpfulness and readership of online …

AI-enabled information systems (AI-enabled IS) offer enhanced utility and efficiency due to their knowledge-based endowments, enabling human agents to assign and receive tasks from AI-enabled IS. As a result, this leads to improved decision-making, ability to manage laborious jobs, and a decrease in human errors. Despite the performance-based endowments and efficiencies, there are significant ethical concerns regarding the use of and delegation to AI-enabled IS, which have been extensively addressed in the literature on the dark side of artificial intelligence (AI). Notable concerns include bias and discrimination, fairness, transparency, privacy, accountability, and autonomy. However, the Information Systems (IS) literature does not have a delegation framework that incorporates ethics in the delegation mechanism. This work seeks to integrate a mixed deontological-teleological ethical system into the delegation mechanism to (and from) AI-enabled IS. To that end, I present a testable model to ethically appraise various AI-enabled IS as well as ethically evaluate delegation to (and from) AI-enabled IS in various settings and situations.

Business process re-engineering (part of the Business Process Management domain) is among the top three concerns of Information Technology (IT) leaders and is deemed to be one of many important IT leveraging opportunities. Two major challenges have been identified in relation to BPM and the use of IT. The first challenge is related to involving business process participants in process improvement initiatives using BPM systems. BPM technologies are considered to be primarily targeted for developers and not BPM users, and the need to engage process participants into process improvement initiatives is not addressed, contributing to the business-IT gap. The second challenge is related to potential de-skilling of knowledge workers when knowledge-intensive processes are automated and process knowledge resides in IT, rather than human process participants. The two identified challenges are not separate issues. Process participants need to be knowledgeable about the process in order to actively contribute to BPM initiatives, and the loss of process knowledge as a result of passive use of automated systems may further threaten their participation in process improvement. In response to the call for more research on the individual impacts of business process initiatives, the purpose of this dissertation study is to understand the relationship …

This research study involves three essays and examines CRQ-driven decision making from the points of view of the common firm, social-commerce platform provider, and social-commerce echo-system. It addresses CRQ's progression from traditional business-to-consumer (B2C) initiatives to social platform-specific antecedents and to environment-driven factors lying outside the direct control of the platform provider, yet influencing social commerce business decisions, such as user-generated content from peers (e.g. family, friends) and expert authority (e.g. specialists, experts, professional organizations). The research method used statistical, data mining and computer science techniques. The results suggest that social platform providers should take a proactive approach to CRQ, fully leverage their online platform to improve CRQ while paying special attention to security as a potential barrier, and consider the analysis of elements of the echo-system such as the electronic word of mouth (eWOM) to further drive CRQ and determine the level of alignment between customers and experts, suppliers and products featured, that may lead to value-added managerial insights such as the prioritization, promotion and optimization of such relationships.

The purpose of our research is to empirically test how system attributes of blockchain build trust through system and information components in blockchain food traceability systems. Findings showed that system attributes of blockchain are strong predictors of trust leading to intention to purchase. A sample of 358 responses were collected from college students through online survey. SmartPLS 3.0 is adopted for data analysis. We made contributions by building a new research model to guide future studies on trust formation in blockchain based systems as well as informing practice to adopt proven features of blockchain to create and capture values for customers.

Managers rely on analytics to make decisions and the choice of the analytical method can influence their decision-making. This dissertation considers three cases and examines how the choice of analytical methods influence interpretations and implications. These areas are communication for health-related information in social media, health information technology investment by hospitals as it relates to patient satisfaction, and health related expenditure policies of countries. These studies develop theoretical models and empirically test them on primary or secondary data, comparing the performance of popular analytical methods. The conduct of these three studies contributes to a better understanding about the choice of analytical methods and allow development of a heuristic approach by offering guidelines for selecting an appropriate methodology. They demonstrate the value of heuristic approaches for use with non-traditional and traditional statistical methods, as the information gained from non-traditional methods (NNs) provides insights into traditional statistical methods, similar to insights gained from exploratory data analysis. The studies also show the value in examining any dataset with multiple methods because they either confirm each other or fail to confirm, providing insights.

The primary focus of this research is explicating the role of emotion in IT post-adoption behavior. Studied in the context of intelligent personal assistants (IPA), a class of conversational artificial intelligence (AI), the first study integrates elements from computer science, communications, and IS disciplines. The research identifies two constructs vital for speech-based technologies, natural language understanding, and feedback, and examines their role in use decisions. This work provides guidance to practice on how best to allocate R&D investments in conversational AI. The second essay examines the IT continuance through the theoretical lens of the expectation-confirmation model (ECM), incorportating cognitive and emotional satisfaction into the ECM framework. Empirical testing of the model suggests that it offers additional clarity on IT continuance phenomena and provides a significant improvement to the explanatory power of ECM in the context of an emerging technology. The third essay is one of the earliest efforts to conceptualize and test a theoretical model that considers emotional attachment in IT continuance behavior. This essay develops a novel model to investigate this phenomenon based on emotional attachment theory, and empirically validates the proposed model in the context of conversational artificial intelligence systems. While the existing theories of IT continuance focus …

Social software has become pervasive including technologies such as blogs, wikis, and social networking sites. Interactive Web 2.0 technology is distinguished from earlier Internet channels, with content provided not only from the website host, but also and most importantly, user-generated content. These social technologies are increasingly entering the enterprise, involving complex social and psychological aspects as well as an understanding of traditional technology acceptance factors. Organizations trying to reap potential benefits of enterprise social software (ESS) must successfully implement and maintain ESS tools. This research develops a framework for assessing knowledge sharing based on reciprocal determinism theory and augmented with technology acceptance, sociological, and psychological factors. Semi-structured interviews with IT professionals, followed by a written survey of employees using ESS are used to collect data. The hermeneutic circle methodology is used to analyze the interview transcripts and structural equation modeling is used to analyze the survey data. Results show technological advantage has no significant effect on the intention to share knowledge, but community cohesiveness and individual willingness significantly affect knowledge sharing intention and behavior. The study offers a synthesized model of variables affecting knowledge sharing as well as a better understanding of best practices for organizations to consider when implementing …

Numerous models of IS success and technology acceptance their extensions have been proposed and applied in empirical. This study continues this tradition and extends the body of knowledge on the topic of IS success by developing a more comprehensive model for measuring IS success and technology acceptance within a government organization. The proposed model builds upon three established IS success and technology acceptance frameworks namely the DeLone and McLean (2003), Venkatesh et al.'s (2003) unified theory of acceptance and use of technology (UTAUT), and Wixom and Todd (2005). The findings from this study provide not only a comprehensive IS success assessment model but also insights into whether and how IS success models are influenced by application variables as applied within a government organization. Exploratory factor analysis and confirmatory factor analysis were performed for instrument refinement and validity test of the existing and proposed models. Using data from employees of a local government municipal, the comprehensive model explained 32 percent variance. Four of the hypothesis were fully supported five were not supported, and four were partially supported. In addition, the results suggest that behavioral intention may not be the best predictor of technology acceptance in a mandatory environment.

Phishing is a social engineering attack to deceive and persuade people to divulge private information like usernames and passwords, account details (including bank account details), and social security numbers. Phishers typically utilize e-mail, chat, text messages, or social media. Despite the presence of automatic anti-phishing filters, phishing messages reach online users' inboxes. Understanding the influence of phishing techniques and individual differences on susceptibility and detection accuracy is an important step toward creating comprehensive behavioral and organizational anti-phishing awareness programs. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to explore the nature of phishing threats that including identifying attack intentions, and psychological and design techniques of phishing attacks. Essay 2 seeks to understand the relative influence of attack techniques and individual phishing experiential traits on people's phishing susceptibility. Essay 3 seeks to understand an individual's cognitive and affective differences that differentiate between an individual's phishing detection accuracy.

This research sought to shed light on information systems security (ISS) by conceptualizing an organization's use of countermeasures using general deterrence theory, positing a non-recursive relationship between threats and countermeasures, and by extending the ISS construct developed in prior research. Industry affiliation and organizational size are considered in terms of differences in threats that firms face, the different countermeasures in use by various firms, and ultimately, how a firm's ISS effectiveness is affected. Six information systems professionals were interviewed in order to develop the appropriate instruments necessary to assess the research model put forth; the final instrument was further refined by pilot testing with the intent of further clarifying the wording and layout of the instrument. Finally, the Association of Information Technology Professionals was surveyed using an online survey. The model was assessed using SmartPLS and a two-stage least squares analysis. Results indicate that a non-recursive relationship does indeed exist between threats and countermeasures and that countermeasures can be used to effectively frame an organization's use of countermeasures. Implications for practitioners include the ability to target the use of certain countermeasures to have desired effects on both ISS effectiveness and future threats. Additionally, the model put forth in this research …

Perceived quality in the context of e-commerce was defined and examined in numerous studies, but, to date, there are no consistent definitions and measurement scales. Instruments that measure quality in e-commerce industries primarily focus on website quality or service quality during the transaction and delivery phases. Even though some scholars have proposed instruments from different perspectives, these scales do not fully evaluate the level of quality perceived by customers during the entire decision-making process. This dissertation purports to provide five main contributions for the e-commerce, service quality, and decision science literature: (1) development of a comprehensive instrument to measure how online customers perceive the quality of the shopping channel, website, transaction and recovery based on the customer decision making process; (2) identification of the determinants of customer satisfaction and the key dimensions of customer behavioral intentions in e-commerce; (3) examination of the relationships among perceived quality, customer satisfaction and loyalty intention using empirical data; (4) application of different statistical packages (LISREL and PLS-Graph) for data analysis and comparison of how these methods impact the results; and (5) examination of the moderating effects of control variables. A survey was designed and distributed to a total of 1126 college students in a …

Customer relationship management (CRM), a primary activity in the business value chain to relate to the customer, involves solicitation, analysis, and the use of the knowledge about the customer to provide goods and services through effective and efficient methods. It is a wise strategy and source of competitive advantage for customer behavior understanding and business performance management. The use of information technology (IT) in CRM allows companies to simplify their processes, to integrate product or service related decision making with the business strategies, and to optimize their operations by embracing analytical techniques. The insurance industry is facing unprecedented challenges and decisions in this data-driven business paradigm. It is a strategic necessity for customer-centric insurers to utilize emerging IT capability to support interactions between customers and business operations. The research in the dissertation seeks to provide insights into the application of early technology innovation and data-driven strategies by investigating the following two groups of CRM technology issues: technology adoption and data-driven technology application. Through three essays, the dissertation explores the use of information technology and data analytical tools to provide insight into how automobile insurance companies make decisions regarding their relationships with their customers. The results from these studies provide a …

Extracting meaningful information from large collections of text data is problematic because of the sheer size of the database. However, automated analytic methods capable of processing such data have emerged. These methods, collectively called text mining first began to appear in 1988. A number of additional text mining methods quickly developed in independent research silos with each based on unique mathematical algorithms. How good each of these methods are at analyzing text is unclear. Method development typically evolves from some research silo centric requirement with the success of the method measured by a custom requirement-based metric. Results of the new method are then compared to another method that was similarly developed. The proposed research introduces an experimentally designed testing method to text mining that eliminates research silo bias and simultaneously evaluates methods from all of the major context-region text mining method families. The proposed research method follows a random block factorial design with two treatments consisting of three and five levels (RBF-35) with repeated measures. Contribution of the research is threefold. First, the users perceived a difference in the effectiveness of the various methods. Second, while still not clear, there are characteristics with in the text collection that affect the …