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A Relationship-based Cross National Customer Decision-making Model in the Service Industry: In 2012, the CIA World Fact Book showed that the service sector contributed about 76.6% and 51.4% of the 2010 gross national product of both the United States and Ghana, respectively. Research in the services area shows that a firm's success in today's competitive business environment is dependent upon its ability to deliver superior service quality. However, these studies have yet to address factors that influence customers to remain committed to a mass service in economically diverse countries. In addition, there is little research on established service quality measures pertaining to the mass service domain. This dissertation applies Rusbult's investment model of relationship commitment and examines its psychological impact on the commitment level of a customer towards a service in two economically diverse countries. In addition, service quality is conceptualized as a hierarchical construct in the mass service (banking) and specific dimensions are developed on which customers assess their quality evaluations. Using, PLS path modeling, a structural equation modeling approach to data analysis, service quality as a hierarchical third-order construct was found to have three primary dimensions and six sub-dimensions. The results also established that a country's national economy has a moderating effect on the relationship between service quality and investment size, and service satisfaction on investment size. This study is the first to conceptualize and use the hierarchical approach to service quality in mass services. Not only does this study build upon the investment model to provide a comprehensive decision model for service organizations to increase their return on investment but also, provides a congruence of work between service quality and the investment model in the management and decision sciences discipline.
The Influence of Business Intelligence Components on the Quality of Decision Making: Decision makers require the right information at the right time, in the right place and in the right format so that they can make good decisions. Although business intelligence (BI) has the potential to improve decision making, there is little empirical evidence of how well this has been achieved. The purpose of this dissertation is to examine the quality of decisions made using BI. The research question it addresses is what are the key antecedents of decision quality for users of business intelligence systems? The theoretical support for the model is developed based on the literature review that draws on decision support systems (DSS), group decision support systems (GDSS), and BI. Grounded on this literature review, the antecedents of decision quality are operationalized in this dissertation through independent variables such as the problem space complexity, the level of BI usage, the BI user experience, and information quality. The dependent variable is operationalized as decision quality and it captures the self-satisfaction with a decision made by users in a BI environment. The research model was tested using a survey of BI users whose names were provided by a marketing company. This research suggests that BI user experience is a more complex construct than has been initially thought.
Supply Chain Network Planning for Humanitarian Operations During Seasonal Disasters: To prevent loss of lives during seasonal disasters, relief agencies distribute critical supplies and provide lifesaving services to the affected populations. Despite agencies' efforts, frequently occuring disasters increase the cost of relief operations. The purpose of our study is to minimize the cost of relief operations, considering that such disasters cause random demand. To achieve this, we have formulated a series of models, which are distinct from the current studies in three ways. First, to the best of our knowledge, we are the first ones to capture both perishable and durable products together. Second, we have aggregated multiple products in a different way than current studies do. This unique aggregation requires less data than that of other types of aggregation. Finally, our models are compatible with the practical data generated by FEMA. Our models offer insights on the impacts of various parameters on optimum cost and order size. The analyses of correlation of demand and quality of information offer interesting insights; for instance, under certain cases, the quality of information does not influence cost. Our study has considered both risk averse and risk neutral approaches and provided insights. The insights obtained from our models are expected to help agencies reduce the cost of operations by choosing cost effective suppliers.
Factors Influencing BI Data Collection Strategies: An Empirical Investigation: The purpose of this dissertation is to examine the external factors that influence an organizations' business intelligence (BI) data collection strategy when mediated by BI attributes. In this dissertation, data warehousing strategies are used as the basis on which to frame the exploration of BI data collection strategies. The attributes include BI insightfulness, BI consistency, and the organizational transformation attribute of BI. The research population consisted of IT professionals and top level managers involved in developing and managing BI. Data was collected from a range of industries and organizations within the United States. An online survey was used to collect the data to empirically test the proposed relationships. Data was analyzed using partial least square path modeling (PLS). The results of this study suggest that there exists a positive relationship between institutional isomorphism and BI consistency. The results also indicate that there exists a positive relationship between BI consistency and BI comprehensive data collection strategy, and the organizational transformation attribute of BI and BI comprehensive data collection strategy. These findings provide a theoretical lens to better understand the motivators and the success factors related to collecting the huge amounts of data required for BI. This study also provides managers with a mental model on which to base decisions about the data required to accomplish their goals for BI.
Business Intelligence Success: An Empirical Evaluation of the Role of BI Capabilities and the Decision Environment: Since the concept of business intelligence (BI) was introduced in the late 1980s, many organizations have implemented BI to improve performance but not all BI initiatives have been successful. Practitioners and academicians have discussed the reasons for success and failure, yet, a consistent picture about how to achieve BI success has not yet emerged. The purpose of this dissertation is to help fill the gap in research and provide a better understanding of BI success by examining the impact of BI capabilities on BI success, in the presence of different decision environments. The decision environment is a composition of the decision types and the way the required information is processed to aid in decision making. BI capabilities are defined as critical functionalities that help an organization improve its performance, and they are examined in terms of organizational and technological capabilities. An online survey is used to obtain the data and partial least squares path modeling (PLS) is used for analysis. The results of this dissertation suggest that all technological capabilities as well as one of the organizational capabilities, flexibility, significantly impact BI success. Results also indicate that the moderating effect of decision environment is significant for quantitative data quality. These findings provide richer insight in the role of the decision environment in BI success and a framework with which future research on the relationship between BI capabilities and BI success can be conducted. Findings may also contribute to practice by presenting information for managers and users of BI to consider about their decision environment in assessing BI success.
Developing Criteria for Extracting Principal Components and Assessing Multiple Significance Tests in Knowledge Discovery Applications: With advances in computer technology, organizations are able to store large amounts of data in data warehouses. There are two fundamental issues researchers must address: the dimensionality of data and the interpretation of multiple statistical tests. The first issue addressed by this research is the determination of the number of components to retain in principal components analysis. This research establishes regression, asymptotic theory, and neural network approaches for estimating mean and 95th percentile eigenvalues for implementing Horn's parallel analysis procedure for retaining components. Certain methods perform better for specific combinations of sample size and numbers of variables. The adjusted normal order statistic estimator (ANOSE), an asymptotic procedure, performs the best overall. Future research is warranted on combining methods to increase accuracy. The second issue involves interpreting multiple statistical tests. This study uses simulation to show that Parker and Rothenberg's technique using a density function with a mixture of betas to model p-values is viable for p-values from central and non-central t distributions. The simulation study shows that final estimates obtained in the proposed mixture approach reliably estimate the true proportion of the distributions associated with the null and nonnull hypotheses. Modeling the density of p-values allows for better control of the true experimentwise error rate and is used to provide insight into grouping hypothesis tests for clustering purposes. Future research will expand the simulation to include p-values generated from additional distributions. The techniques presented are applied to data from Lake Texoma where the size of the database and the number of hypotheses of interest call for nontraditional data mining techniques. The issue is to determine if information technology can be used to monitor the chlorophyll levels in the lake as chloride is removed upstream. A relationship established between chlorophyll and the energy reflectance, which can be measured by satellites, enables …
The Impact of Culture on the Decision Making Process in Restaurants: Understanding the process of consumers during key purchasing decision points is the margin between success and failure for any business. The cultural differences between the factors that affect consumers in their decision-making process is the motivation of this research. The purpose of this research is to extend the current body of knowledge about decision-making factors by developing and testing a new theoretical model to measure how culture may affect the attitudes and behaviors of consumers in restaurants. This study has its theoretical foundation in the theory of service quality, theory of planned behavior, and rational choice theory. To understand how culture affects the decision-making process and perceived satisfaction, it is necessary to analyze the relationships among the decision factors and attitudes. The findings of this study contribute by building theory and having practical implications for restaurant owners and managers. This study employs a mixed methodology of qualitative and quantitative research. More specifically, the methodologies employed include the development of a framework and testing of that framework via collection of data using semi-structured interviews and a survey instrument. Considering this framework, we test culture as a moderating relationship by using respondents’ birth country, parents’ birth country and ethnic identity. The results of this study conclude, in the restaurant context, culture significantly moderates consumers’ perception of service quality, overall satisfaction, and behavior intention.of OA.
Three Essays on Social Media: the Effect of Motivation, Participation, and Sentiment on Performance: 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 consumer reviews. The results show how different motivations lead to different levels of participation in social media and level of participation consequently influences performance. We also find that sentiment of the messages posted on social media significantly influence their performance.
Does Device Matter? Understanding How User, Device, and Usage Characteristics Influence Risky IT Behaviors of Individuals: Over the past few years, there has been a skyrocketing growth in the use of mobile devices. Mobile devices are ushering in a new era of multi-platform media and a new paradigm of “being-always-connected”. The proliferation of mobile devices, the dramatic growth of cloud computing services, the availability of high-speed mobile internet, and the increase in the functionalities and network connectivity of mobile devices, have led to creation of a phenomenon called BYOD (Bring Your Own Device), which allows employees to connect their personal devices to corporate networks. BYOD is identified as one of the top ten technology trends in 2014 that can multiply the size of mobile workforce in organizations. However, it can also serve as a vehicle that transfers cyber security threats associated with personal mobile devices to the organizations. As BYOD opens the floodgates of various device types and platforms into organizations, identifying different sources of cyber security threats becomes indispensable. So far, there are no studies that investigated how user, device and usage characteristics affect individuals’ protective and risky IT behaviors. The goal of this dissertation is to expand the current literature in IS security by accounting for the roles of user, device, and usage characteristics in protective and risky IT behaviors of individuals. In this study, we extend the protection motivation theory by conceptualizing and measuring the risky IT behaviors of individuals and investigating how user, device, and usage characteristics along with the traditional protection motivation factors, influence individuals’ protective and risky IT behaviors. We collected data using an online survey. The results of our study show that individuals tend to engage in different levels of protective and risky IT behaviors on different types of devices. We also found that certain individual characteristics as well as the variety of applications that individuals use on their …
Decision Makers’ Cognitive Biases in Operations Management: An Experimental Study: Behavioral operations management (BOM) has gained popularity in the last two decades. The main theme in this new stream of research is to include the human behavior in Operations Management (OM) models to increase the effectiveness of such models. BOM is classified into 4 areas: cognitive psychology, social psychology, group dynamics and system dynamics (Bendoly et al. 2010). This dissertation will focus on the first class, namely cognitive psychology. Cognitive psychology is further classified into heuristics and biases. Tversky and Kahneman (1974) discussed 3 heuristics and 13 cognitive biases that usually face decision makers. This dissertation is going to study 6 cognitive biases under the representativeness heuristic. The model in this dissertation states that cognitive reflection of the individual (Frederick 2005) and training about cognitive biases in the form of warning (Kaufmann and Michel 2009) will help decisions’ makers make less biased decisions. The 6 cognitive biases investigated in this dissertation are insensitivity to prior probability, insensitivity to sample size, misconception of chance, insensitivity to predictability, the illusion of validity and misconception of regression. 6 scenarios in OM contexts have been used in this study. Each scenario corresponds to one cognitive bias. Experimental design has been used as the research tool. To see the impact of training, one group of the participants received the scenarios without training and the other group received them with training. The training consists of a brief description of the cognitive bias as well as an example of the cognitive bias. Cognitive reflection is operationalized using cognitive reflection test (CRT). The survey was distributed to students at University of North Texas (UNT). Logistic regression has been employed to analyze data. The research shows that participants show the cognitive biases proposed by Tversky and Kahneman. Moreover, CRT is significant factor to predict the cognitive bias in two …
Quality Management Theory Development and Investigation of the Constructs within an Organizational Framework: Supply chain management (SCM) and quality management (QM) share some common literature and have overlapping domains that reinforce each other in the supplier and customer relationship management areas. Despite the recognized importance of supplier and customer relationships toward achieving quality goals, limited prior research examines whether SCM represents a distinct construct within the prominent existing quality focused organizational frameworks such as the Malcolm Baldrige National Quality Award (MBNQA). As a result of the absence of the SCM construct in the frameworks, the problem facing researchers is understanding the role of SCM in the implementation of QM practices within an organization. Such an understanding is key to QM theory development for the 21st century organizations. In order to conduct this investigation, we examine several well-studied quality focused organizational frameworks that are validated among the community of researchers, and, widely accepted among practitioners. However, which of these well-known quality management models serve as the best proxy for a quality focused organizational framework is an important area for research in order to better promote QM worldwide. This research involves three essays and uses a mixed methodology of qualitative and quantitative research. Essay 1 compares well-known national quality award frameworks such as the MBNQA, the Deming Prize, and the European Foundation for Quality Management (EFQM) Award through analysis of the extensive literature on each as well as examination of the government documents about the frameworks. Comparisons show the Baldrige framework most widely serves as basic model for national quality award frameworks to increase the awareness of quality and promote the best QM practices. After reviewing the categories and their weightings in the frameworks of MBNQA, the Deming Prize, and the EFQM Award, we identify opportunities to refine the frameworks and promote QM theory development. Essay 2 fills a critical research gap by assessing the …
Costs and Benefits of Mind Wandering in a Technological Setting: Findings and Implications: The central purpose of this dissertation is to develop and test a theoretical model of mind wandering in a technological setting by integrating the emerging work and theory on mind wandering—a shift of attention from the primary task to the processing of internal goals. This dissertation is intended to advance our understanding on the costs and benefits of mind wandering in information systems (IS) research and in turn, contribute to the literature of cognitive IS research. Understanding the consequences of mind wandering in a technological setting is imperative because mind wandering plays a vital role in influencing various outcomes associated with technology use and/or technology learning, such as technology anxiety, software self-efficacy, and task performance. This dissertation is composed of three essays which examine the determinants and consequences of mind wandering and focus of attention on a number of emotional and cognitive outcomes. A multi-method approach (i.e., online survey and laboratory experiment) across three essays is used to test the research models. Essay 1 focuses on developing the measurement items and estimating the impact of mind wandering on users' emotional outcomes (i.e., technology anxiety and users' satisfaction). Drawing upon the content regulation hypothesis of mind wandering, the content of thoughts are differentiated into two categories—technology-related thought (herein IT) and non-technology related thought (herein non-IT). The results show that whereas mind wandering (non-IT) is a major determinant of technology anxiety, focus of attention (IT) is the main predictor of users' satisfaction. Essay 2 focuses on the effect of mind wandering and focus of attention in the IS learning context. The study begins by exploring the hypotheses concerning the roles of executive functions (i.e., inhibition, switching, and working memory) and task complexity in influencing the occurrence of mind wandering and focus of attention, and in turn, cognitive outcomes (i.e., software self-efficacy and …
A social capital perspective on IT professionals' work behavior and attitude.: Abstract Attracting and developing information technology (IT) professionals is one of the top concerns for companies. Although much research has been conducted about the job behavior and attitudes of IT professionals over the last three decades, findings are inconclusive and contradictory. This suggests that something may be missing in how we examine this phenomenon. Most of this research is drawn from theories of motivation, very little examines the effect of social relationships on IT professionals' behavior and attitude. Yet, social capital theory suggests that job behavior and attitude may be greatly influenced by these relationships. This suggests that IT professionals' social capital warrants empirical examination. The primary research question that this dissertation addresses is how social capital affects IT professionals' work attitude and behavior including job satisfaction, organizational citizenship behavior, job performance and turnover intention. The research model in this dissertation examines the influence of three aspects of social capital on IT professionals' job attitude and work behavior: tie strength, the number of ties and the structural holes. Data were collected from 129 IT professionals from a range of jobs, organizations and industries. Results indicate that tie strength in the organization of an IT professional is positively related to job satisfaction. The number of ties outside an organization an IT professional has is also positively related to job performance. However, hypotheses about organizational citizenship behavior and turnover intention are not supported. Several implications for organizational executives and managers are offered based on findings.
Reliable Prediction Intervals and Bayesian Estimation for Demand Rates of Slow-Moving Inventory: Application of multisource feedback (MSF) increased dramatically and became widespread globally in the past two decades, but there was little conceptual work regarding self-other agreement and few empirical studies investigated self-other agreement in other cultural settings. This study developed a new conceptual framework of self-other agreement and used three samples to illustrate how national culture affected self-other agreement. These three samples included 428 participants from China, 818 participants from the US, and 871 participants from globally dispersed teams (GDTs). An EQS procedure and a polynomial regression procedure were used to examine whether the covariance matrices were equal across samples and whether the relationships between self-other agreement and performance would be different across cultures, respectively. The results indicated MSF could be applied to China and GDTs, but the pattern of relationships between self-other agreement and performance was different across samples, suggesting that the results found in the U.S. sample were the exception rather than rule. Demographics also affected self-other agreement disparately across perspectives and cultures, indicating self-concept was susceptible to cultural influences. The proposed framework only received partial support but showed great promise to guide future studies. This study contributed to the literature by: (a) developing a new framework of self-other agreement that could be used to study various contextual factors; (b) examining the relationship between self-other agreement and performance in three vastly different samples; (c) providing some important insights about consensus between raters and self-other agreement; (d) offering some practical guidelines regarding how to apply MSF to other cultures more effectively.
Propensity for knowledge sharing: An organizational justice perspective.: Converting individual knowledge into organizational knowledge can be difficult because individuals refuse to share knowledge for a number of different reasons. Creating an atmosphere of fairness plays an important role in the creation of a knowledge-sharing climate. This dissertation proposes that perceptions of organizational justice are crucial building blocks of that environment, leading to knowledge sharing. Data was collected using a field survey of IT managers representing a broad spectrum of the population in terms of organizational size and industry classification. The survey instrument was developed based on the adaptation of previously validated scales in addition to new items where no existing measures were found. Hypotheses regarding the influence of distributional, procedural, and interactional justice on knowledge sharing processes were tested using structural equation modeling techniques. Based on the theory of reasoned action, which states that attitudes and subjective norms are the major determinants of a person's intention, the hypotheses examining the relationship between attitude toward knowledge sharing, subjective norm and the intention to share knowledge were supported. However, results did not support the hypothesis exploring the relationship between the organizational climate and the intention to share knowledge. The results show that all three types of justice constructs are statistically significant antecedents of organizational climate and interactional justice is an antecedent of an attitude toward knowledge sharing. The study attempts to merge streams of research from sociology and organizational behavior by investigating organizational justice and knowledge management. It contributes to theory by the development of the survey instrument, comprised of seven constructs that were developed by incorporating multiple theories to address various aspects of knowledge sharing and provide application to practice and research. It is relevant to IT managers who need to know how to design information systems that are most effective in distributing knowledge throughout organizations.
The Impact of Blockchain Food Tracing Information Quality and Trust on Intention to Purchase: 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.
Organizational Competency Through Information: Business Intelligence and Analytics as a Tool for Process Dynamization: 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 with BI&A experience, this study develops and tests a model of BI&A enabled firm performance. Using a snowball sample, an online survey was administered to 137 business professionals in 24 industries. The data were analyzed using partial least squares (PLS) structural equation modeling (SEM). The findings support the contention that BI&A serve as the sensing and seizing components of an organizational dynamic capability, while transformation is achieved through business process change capability. These factors influence firm financial performance through their impact on the functional performance of the firm’s business processes. Further, this study demonstrates that traditional BI&A success factors are …
The Effect of Value Co-creation and Service Quality on Customer Satisfaction and Commitment in Healthcare Management: Despite much interest in service quality and various other service quality measures, scholars appear to have overlooked the overall concept of quality. More specifically, previous research has yet to integrate the effect of the customer network and customer knowledge into the measurement of quality. In this work, it is posited that the evaluation of quality is based on both the delivered value from the provider as well as the value developed from the relationships among customers and between customers and providers. This research examines quality as a broad and complex issue, and uses the “Big Quality” concept within the context of routine healthcare service. The last few decades have witnessed interest and activities surrounding the subject of quality and value co-creation. These are core features of Service-Dominant (S-D) logic theory. In this theory, the customer is a collaborative partner who co-creates value with the firm. Customers create value through the strength of their relations and network, and they take a central role in value actualization as value co-creator. I propose to examine the relationship between quality and the constructs of value co-creation. As well, due to the pivotal role of the decision-making process in customer satisfaction, I will also operationalize the value co-creation construct. Building upon the “Big Quality” concept, this study suggests a new approach by extending the quality concept to include the value-creation concept in Service Dominant Logic. This study identifies the associated constructs and determinants of Big Quality in routine healthcare management service, and examines the relationship among the associated quality constructs, customer satisfaction, and customer commitment. This study employed an online survey methodology to collect data. In data analysis, I used the variance-based structural equation modeling (PLS-SEM) approach to confirm the factor structure, proposed model, and test the research hypotheses. The results show that the customer’s …
Enterprise Social Software: an Empirical Assessment of Knowledge Sharing in the Workplace: 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 and maintaining ESS tools for employee knowledge sharing and collaboration.
Accuracy and Interpretability Testing of Text Mining Methods: 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 algorithms ability to extract meaningful results. Third, this research develops an experimental design process for testing the algorithms that is adaptable into other areas of software development and algorithm testing. This design eliminates the bias based practices historically employed by algorithm developers.
The Effect of It Process Support, Process Visualization and Process Characteristics on Process Outcomes: 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 between IT configurations (particularly process support and process visualization), process characteristics and individual level process outcomes, such as task performance and process knowledge. In the development of the research model we rely on organizational knowledge creation literature and scaffolding in Vygotsky’s Zone of Proximal Development, business process modeling and workflow automation research, as well as research on the influence of IT on individual performance. The theoretical model is tested empirically in experimental settings using a series of two studies. In both studies participants were asked to complete tasks as part of a business process using different versions of a mock-up …
Development and Exploration of End-User Healthcare Technology Acceptance Models: This dissertation consists of three studies that collectively investigate the factors influencing the consumer adoption intention towards emerging healthcare technologies. Essay 1 systematically reviews the extent literature on healthcare technology adoption and serves as the theoretical foundation of the dissertation. It investigates different models that have been previously applied to study healthcare technology acceptance. Meta-analysis method is used to quantitatively synthesize the findings from prior empirical studies. Essay 2 posits, develops, and tests a comprehensive biotechnology acceptance model from the end-user's perspective. Two new constructs, namely, perceived risk and trust in technology, are integrated into the unified theory of acceptance and use of technology. Research hypotheses are tested using survey data and partial least square – structural equation modeling (PLS-SEM). Essay 3 extends the findings from the Essay 2 and further investigates the consumer's trust initiation and its effect on behavioral adoption intention. To achieve this purpose, Essay 3 posits and develops a trust model. Survey data allows testing the model using PLS-SEM. The models developed in this dissertation reflect significant modifications specific to the healthcare context. The findings provide value for academia, practitioners, and policymakers.
Information Privacy and Security Associated with Healthcare Technology Use: This dissertation consists of three studies that investigate the information privacy & security associated with healthcare technology use. Essay 1 PRISMA-style systematically reviews the existing literature on privacy information disclosure in IoT technology and serves as the theoretical foundation of the current research. It is crucial to comprehend why, how, and under what consequences individuals choose to disclose their personal and health information since doing so is beneficial to the company. This SLR method allows us to find those factors that significantly impact individuals' behavioral intention to disclose personal information while using IoT technologies. Essay 2 posits, develops, and tests a comprehensive theoretical framework built upon the theory of planned behavior and the health belief model to examine factors affecting willingness to disclose PHI in order to use WFDs. A research survey is designed and distributed to a crowdsourcing platform, Mechanical Turk (M-Turk). Research hypotheses are tested using partial least square – structural equation modeling (PLS-SEM). To achieve this purpose, Essay 3 extends the findings from the previous essay and further investigates the caregiver context. Therefore, we developed a novel theoretical model utilizing privacy calculus theory and the technology acceptance model to investigate the willingness of the elderly to disclose personal health information needed to use caregiver robots. Survey data were collected using crowdsourcing utilizing Amazon's Mechanical Turk (M-Turk) and Prolific. Research hypotheses are tested using partial least square – structural equation modelling (PLS-SEM). The findings provide value for academia, practitioners, and policymakers.
Incorporating Ethics in Delegation To and From Artificial Intelligence-Enabled Information Systems: 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.
Three Essays on Collective Privacy and Information Security: In Essay 1, we seek to expand the insights on an individual's decision to share group content. Social networking sites (SNS) have become a ubiquitous means of socializing in the digital age. Using a survey, we collected data from 520 respondents with corporate work experience to test our research model. Our analysis highlights the complex interplay between individual and group factors that shape users' risk-benefit analysis of sharing group content on social networking sites. Furthermore, the results of this study have important implications for social networking site design and policy, particularly with regard to providing granular control over the privacy settings of group content and clear and concise information about the potential risks and benefits of sharing group content. Essay 2 aims to extend the knowledge of information security policy (ISP) compliance. Using a comprehensive approach, we extended the perspective of control mechanisms in the context of ISPs. It is evident that maintaining information security is an important concern for organizations of all sizes and industries. Organizations can establish policies and procedures to regulate and ensure compliance with information security policies, and various control mechanisms can be employed to ensure compliance. Among these control mechanisms, enforcement, punishment, evaluation, and recognition have been identified as important factors that influence information security policy compliance. In Essay 3, we delve deep into the current digital era and the reality of individuals becoming particularly vulnerable to privacy breaches. In the third essay, we offer a thorough examination of existing literature to gain insight into the disparities between users' stated privacy concerns and their actual information-sharing behavior. Our analysis reveals that, in addition to technological and environmental factors, cultural and personal differences significantly contribute to the paradoxical behavior observed among individuals. Utilizing the S-O-R (stimulus-organism-response) framework, we emphasize the necessity of examining the intricate interplay …
Three Essays on Information Privacy of Mobile Users in the Context of Mobile Apps: The increasing demand for mobile apps is out the current capability of mobile app developers. In addition, the growing trend in smartphone ownership and the time people spend on mobile apps has raised several opportunities and risks for users and developers. The average time everyday a user spend on smartphones to use mobile apps is more than two hours. The worldwide mobile app revenue increase is estimated to grow 33%, $19 billion. Three quarter of the time used on mobile apps is solely for using game and social networking apps. To provide more customized services and function to users, mobile apps need to access to personal information. However, 80% of mobile apps put people's information privacy at risk. There is a major gap in the literature about the privacy concerns of mobile device users in the context of mobile apps. This dissertation addresses one fundamental research question: how does individuals' privacy change in the context of mobile apps? More precisely, the focus of this dissertation is on information privacy role in individuals' and mobile app developers' protective behaviors. We investigate the information sensitivity level influence on mobile app developers' emphasis on privacy across mobile app categories. The results show information sensitivity level has a significant impact on developers' emphasis on secondary usage of information. Moreover, we analyze the privacy trade-off dynamism in using a new social networking app and how it could result in emotional attachment. Results show initial use and initial disclosure influence the privacy trade-off from pre-use to initial-use period. Finally, the effect of privacy concern and engagement on emotional attachment is demonstrated. This dissertation addresses one fundamental research question: how does individuals' privacy change in the context of mobile apps? More precisely, the focus of this dissertation is on information privacy role in individuals' and mobile app …
A Heuristic Approach to Selection of Analytical Methods: Three Empirical Healthcare Studies: 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.
Three Essays on Internet of Things Adoption and Use: Internet of Things (IoT) is a promising technology with great potential for individuals, society, governments, and the economy. IoT is expected to become ubiquitous and influence every aspect of everyday experience. Thus, IoT represents an important phenomena for both organizational and behavioral information system (IS) researchers. This dissertation seeks to contribute to IS research by studying the aspects that influence IoT adoption and use at both consumer and organizational levels. This dissertation achieves this purpose in a series of three essays. The first essay focuses on IoT acceptance in the context of smart home. The second essay focuses on examining the effect of artificial intelligence (AI) capabilities on consumers' IoT perceptions and intentions. Finally, the third essay focuses on the organizational investment and adoption of IoT technologies.
Decision Making in Alternative Modes of Transportation: Two Essays on Ridesharing and Self-Driving Vehicles: This manuscript includes an investigation of decision making in alternative modes of transportation in order to understand consumers' decision in different contexts. In essay 1 of this study, the motives for participation in situated ridesharing is investigated. The study proposes a theoretical model that includes economic benefits, time benefits, transportation anxiety, trust, and reciprocity either as direct antecedents of ridesharing participation intention, or mediated through attitude towards ridesharing. Essay 2 of this study, focuses on self-driving vehicles as one of the recent innovations in transportation industry. Using a survey approach, the study develops a conceptual model of consumers' anticipated motives. Both essays use partial least square- structural equation modeling for assessing the proposed theoretical models.
Three Essays on Information Security Risk Management: Today's environment is filled with the proliferation of cyber-attacks that result in losses for organizations and individuals. Hackers often use compromised websites to distribute malware, making it difficult for individuals to detect. The impact of clicking through a link on the Internet that is malware infected can result in consequences such as private information theft and identity theft. Hackers are also known to perpetrate cyber-attacks that result in organizational security breaches that adversely affect organizations' finances, reputation, and market value. Risk management approaches for minimizing and recovering from cyber-attack losses and preventing further cyber-attacks are gaining more importance. Many studies exist that have increased our understanding of how individuals and organizations are motivated to reduce or avoid the risks of security breaches and cyber-attacks using safeguard mechanisms. The safeguards are sometimes technical in nature, such as intrusion detection software and anti-virus software. Other times, the safeguards are procedural in nature such as security policy adherence and security awareness and training. Many of these safeguards fall under the risk mitigation and risk avoidance aspects of risk management, and do not address other aspects of risk management, such as risk transfer. Researchers have argued that technological approaches to security risks are rarely sufficient for providing an overall protection of information system assets. Moreover, others argue that an overall protection must include a risk transfer strategy. Hence, there is a need to understand the risk transfer approach for managing information security risks. Further, in order to effectively address the information security puzzle, there also needs to be an understanding of the nature of the perpetrators of the problem – the hackers. Though hacker incidents proliferate the news, there are few theory based hacker studies. Even though the very nature of their actions presents a difficulty in their accessibility to research, a glimpse of …
Decision-Making with Big Information: The Relationship between Decision Context, Stopping Rules, and Decision Performance: Ubiquitous computing results in access to vast amounts of data, which is changing the way humans interact with each other, with computers, and with their environments. Information is literally at our fingertips with touchscreen technology, but it is not valuable until it is understood. As a result, selecting which information to use in a decision process is a challenge in the current information environment (Lu & Yuan, 2011). The purpose of this dissertation was to investigate how individual decision makers, in different decision contexts, determine when to stop collecting information given the availability of virtually unlimited information. Decision makers must make an ultimate decision, but also must make a decision that he or she has enough information to make the final decision (Browne, Pitts, & Wetherbe, 2007). In determining how much information to collect, researchers found that people engage in ‘satisficing' in order to make decisions, particularly when there is more information than it is possible to manage (Simon, 1957). A more recent elucidation of information use relies on the idea of stopping rules, identifying five common stopping rules information seekers use: mental list, representational stability, difference threshold, magnitude threshold, and single criterion (Browne et al., 2007). Prior research indicates a lack of understanding in the areas of information use (Prabha, Connaway, Olszewski, & Jenkins, 2007) and information overload (Eppler & Mengis, 2004) in Information Systems literature. Moreover, research indicates a lack of clarity in what information should be used in different decision contexts (Kowalczyk & Buxmann, 2014). The increase in the availability of information further complicates and necessitates research in this area. This dissertation seeks to fill these gaps in the literature by determining how information use changes across decision contexts and the relationships between stopping rules. Two unique methodologies were used to test the hypotheses in the conceptual …
Three Research Essays on Online Users' Concerns and Web Assurance Mechanisms: Online users struggle with different concerns whenever they use information systems. According to Miyazaki and Fernandez (2001), there are three important categories of concerns for online users: privacy concern, third party fraudulent behavior concern ("system security"), and online website fraudulent behavior concern ("security"). Kim, Sivasailam, and Rao (2004) proposed a similar categorization for web assurance dimensions. They argue that online websites are supposed to address users' privacy, security, and business integrity concerns to decrease user concerns. Although several researchers tried to answer how different factors affect these concerns and how these concerns affect users' behavior, there are so many ambiguities and contradictions in this area. This Essay I in this work develops a comprehensive map of the role of online privacy concern to identify related factors and categorize them through an in-depth literature review and conducting meta-analysis on online privacy concern. Although users have concerns about their privacy and security, there is still growth in the number of internet users and electronic commerce market share. One possible reason is that websites are applying assurance mechanisms to ensure the privacy of their users. Therefore, it could be an interesting research topic to investigate how privacy assurance mechanisms affect users concern and, consequently, their behavior in different concerns such as e-commerce and social networking sites. Different types of web assurance mechanisms are used by websites. The most prevalent among these assurance mechanisms include web assurance seals and assurance statements and privacy customization features. Essay II and III aims to address how these mechanisms influence e-commerce and social networking sites users' behavior. Essay II applies the procedural fairness theory by Lind and Tyler (1988) to explain how and why the web assurance mechanisms affect consumers' perceived risks. Essay III addresses the issue of self-disclosure on social networking sites. Applying protection motivation theory, this …
Extensions of the General Linear Model into Methods within Partial Least Squares Structural Equation Modeling: The current generation of structural equation modeling (SEM) is loosely split in two divergent groups - covariance-based and variance-based structural equation modeling. The relative newness of variance-based SEM has limited the development of techniques that extend its applicability to non-metric data. This study focuses upon the extension of general linear model techniques within the variance-based platform of partial least squares structural equation modeling (PLS-SEM). This modeling procedure receives it name through the iterative PLS‑SEM algorithm's estimates of the coefficients for the partial ordinary least squares regression models in both the measurement model and the overall structural model. This research addresses the following research questions: (1) What are the appropriate measures for data segmentation within PLS‑SEM? (2) What are the appropriate steps for the analysis of rank-ordered path coefficients within PLS‑SEM? and (3) What is an appropriate model selection index for PLS‑SEM? The limited type of data to which PLS-SEM is applicable suggests an opportunity to extend the method for use with different data and as a result a broader number of applications. This study develops and tests several methodologies that are prevalent in the general linear model (GLM). The proposed data segmentation approaches posited and tested through post hoc analysis of structural model. Monte Carlo simulation allows demonstrating the improvement of the proposed model fit indices in comparison to the established indices found within the SEM literature. These posited PLS methods, that are logical transfers of GLM methods, are tested using examples. These tests enable demonstrating the methods and recommending reporting requirements.
Three Essays on the Role of Social, Legal and Technical Factors on Internet of Things and Smart Contracts Adoption in the Context of COVID-19 Pandemic: I extended and adapted the current technology acceptance models and privacy research to the peculiar context of the COVID-19 pandemic to ascertain the effective "power" of IT in fighting such a pandemic. The research models developed for the purpose of this study contain peculiar modifications to the technological-personal-environmental (TPE) framework and privacy calculus model because of the unique technologies implemented and the peculiar pandemic scenario. I developed three studies that investigate the interaction between social, legal, and technical factors that affect the adoption of IoT devices and blockchain systems implemented to fight the spread of COVID-19. Essay 1 systematically reviews existing literature on the analysis of the social, legal, and technical components in addressing phenomena related to IoT architecture and blockchain technology. The employment of a comparable coding method allows finding which of the above components is prominent in relation to the study of IoT and blockchain. Essay 2 develops a technological acceptance model by integrating the TPE framework with new constructs, i.e., regulatory environment, epidemic ecosystem, pre-epidemic ecosystem, perceived social usefulness, and technical characteristics. Essay 3 further explores the interplay between social, legal, and technical factors toward the adoption of smart contracts in the context of the COVID-19 pandemic. Essay 3 integrates the privacy calculus model by introducing new constructs, i.e., technical characteristics, regulatory environment, and perceived social benefits. For both Essays 2 and 3, research surveys were developed and distributed to undergraduate and graduate students in a major university located in the US. The research hypotheses were tested using partial least square modeling.
Factors Influencing Continued Usage of Telemedicine Applications: This study addresses the antecedents of individuals' disposition to use telemedicine applications, as well as the antecedents of their usage to provide insight into creating sustained usage over time. The theoretical framework of this research is Bhattacherjee's expectation-confirmation IS continuance model. By combining a series of key factors which may influence the initial and continued usage of telemedicine applications with key constructs of Bhattacherjee's IS continuance model, this study aims to provide a deeper understanding of barriers to telemedicine app usage and how to facilitate continued use of these apps. Online survey data was collected from college students who are telemedicine application users. A total of 313 responses were gathered, and data analysis was conducted using SmartPLS 3. This dissertation contributes by looking at the IS adoption and IS continuance research simultaneously to connect these two research streams as well as suggesting the usage context of some established IS theory being different with regard to healthcare applications.
Factors that Drive New Technology Product and Service Use and Continuance: Understanding information technology and its related products and services is increasingly important because the everyday use of technology continues to expand and broaden. Despite this need for greater understanding, the extant theories that explore the dominant factors that drive intention to use a new technology are limited. The Technology Acceptancy Model (TAM) is the most popular model in explaining traditional technology adoption. The limitations of the TAM in grasping the overall evaluation of technology or service are one of motivations for developing new models in this dissertation. The two antecedents of the TAM- perceived usefulness and perceived ease of use- only capture partial utility of a service (or product). In addition, some researchers argued that key factors used in an initial acceptance model such as perceived usefulness and perceived ease of use are not strong predictors of future continuance intention of the service because they do not consider future switching intention in the later stage. Hence, one goal of this dissertation is to develop and test new models to predict factors that drive intention and continuance intention decisions of new technology related products or services. This research involves three studies that examine different aspects of adoption and continuance intention decisions of new technology-related products or services. Essay 1 posits and empirically tests a new model that examines service vendor quality, service outcome quality, and trust as drivers of cloud-based services by adopting the frameworks from marketing, behavior intention and information technology research. The model is referred to as the quality trust model (QTM). The quality of cloud-based services involves the quality of vendors and the quality of service outcomes; its effectiveness is mirrored by trust of the services. Data from an online survey of 355 respondents were used to test the research model. The results show that vendor quality and …
Information systems success and technology acceptance within a government organization.: 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.
IT Offshoring Success: A Social Exchange Perspective: Spending by U.S. companies in offshore IT services continues at unprecedented levels despite a high failure rate. This study fills a gap in the existing literature by examining the client-vendor offshoring relationship through the theoretical lens of social exchange theory at the organizational level of analysis from the client's perspective. Social exchange theory focuses on the exchange of activities between two parties, whether they are individuals or companies and was used as a basis for examining the client and vendor relationship. Variables were identified by a review of the literature primarily from IT outsourcing and offshoring but also from general IT, marketing, sociology and organizational science literature. Data was collected using a field survey of Fortune 500 CIOs representing a population of organizations at the forefront of the offshoring phenomenon. The survey instrument was developed based on the adaptation of previously validated scales. Hypotheses regarding the correlations between social variables such as trust, communication, dependence, power, shared values and offshoring success were tested using Spearman's rho correlation. Seven of the hypotheses were supported, four hypotheses were not supported and one hypothesis was deemed not testable due to lack of information.
Defining the Information Security Posture: An Empirical Examination of Structure, Integration, and Managerial Effectiveness: The discipline of information security management is still in its infancy as evidenced by the lack of empirical scholarly work in this area. Most research within the information security domain focuses on specific technologies and algorithms and how it impacts the principles of confidentiality, integrity, and availability. But, an important area receiving little attention is the antecedents of effective information security management at the organizational level (Stanton, Guzman, Stam & Caldera, 2003). The little empirical research that has been conducted in this area has shown that information security management in many organizations is poor (Baskerville, 1993; Shimeall & McDermott, 1999). Several researchers have identified the need for methods to measure the organization-wide information security posture of organizations (Eloff & Von Solms, 2000; James, 1996). This dissertation attempts to measure the organization-wide information security posture by examining benchmark variables that assess role, planning orientation, and performance structure within the organization. Through this conceptualization of an organization's information security posture, a means is presented to measure overall information security and how it impacts the effective utilization of information security strategies. The presence of the dependent variable, effectiveness, gives academics and practitioners a success measure which can guide more effective decision making in the information security domain. An additional aim of this dissertation is to empirically examine the influence of management practices and decisions on effective use of information security strategies within the organization. The issues of centralization versus decentralization of information security activities will be evaluated along with its impact on information security posture of organizations and the effectiveness of the organization's information security strategies. Data was collected from 119 IT and information security executives. Results show that how the organization structures information security activities is not correlated with more effective utilization of information security strategies. Meanwhile, the organization's information security posture …
General Deterrence Theory: Assessing Information Systems Security Effectiveness in Large versus Small Businesses: 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 can be used by practitioners to both assess their current ISS effectiveness as well as to prescriptively target desired levels of ISS effectiveness.
The Impact of IT Capability on Employee Capability, Customer Value, Customer Satisfaction, and Business Performance: This study empirically examines the impact of IT capability on firms' performance and evaluates whether firms' IT capabilities play a role in improving employee capability, customer value, customer satisfaction, and ultimately business performance. The results were based on comparing the business performance of the IT leader companies with that of control companies of similar size and industry. The IT leader companies were selected from the Information Week 500 list published annually from 2001 to 2004. For a company to be selected as IT leaders, it needed to be listed at least twice during the period. Furthermore, it had to be listed in the American Customer Satisfaction Index (ACSI) so that its customer satisfaction level could be assessed. Standard & Poor's Compustat and the ACSI scores were used to test for changes in business performance. The study found that the IT leaders had a raw material cost measured by cost-of-goods-sold to sales ratio (COGS/S) than the control companies. However, it found no evidence that firms' IT capability affects employee capability, customer value, customer satisfaction, and profit. An important implication from this study is that IT becomes a commodity and an attempt to gain a competitive advantage by overinvesting in IT may be futile.
Impact of Forecasting Method Selection and Information Sharing on Supply Chain Performance.: Effective supply chain management gains much attention from industry and academia because it helps firms across a supply chain to reduce cost and improve customer service level efficiently. Focusing on one of the key challenges of the supply chains, namely, demand uncertainty, this dissertation extends the work of Zhao, Xie, and Leung so as to examine the effects of forecasting method selection coupled with information sharing on supply chain performance in a dynamic business environment. The results of this study showed that under various scenarios, advanced forecasting methods such as neural network and GARCH models play a more significant role when capacity tightness increases and is more important to the retailers than to the supplier under certain circumstances in terms of supply chain costs. Thus, advanced forecasting models should be promoted in supply chain management. However, this study also demonstrated that forecasting methods not capable of modeling features of certain demand patterns significantly impact a supply chain's performance. That is, a forecasting method misspecified for characteristics of the demand pattern usually results in higher supply chain costs. Thus, in practice, supply chain managers should be cognizant of the cost impact of selecting commonly used traditional forecasting methods, such as moving average and exponential smoothing, in conjunction with various operational and environmental factors, to keep supply chain cost under control. This study demonstrated that when capacity tightness is high for the supplier, information sharing plays a more important role in effective supply chain management. In addition, this study also showed that retailers benefit directly from information sharing when advanced forecasting methods are employed under certain conditions.
An Analysis of Information Technology (IT) Post-Adoption Behavior: 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 on purely rational, goal-oriented factors, this study incorporates non-cognitive aspects by including the emotional consequences of IT continuance and offers evidence that attachment can exist even in the absence of cognitive factors.
Does Quality Management Practice Influence Performance in the Healthcare Industry?: This research examines the relationship between quality management (QM) practices and performance in the healthcare industry via the conduct of three studies. The results of this research contribute both to advancing QM theory as well as in developing a unique text mining method that is illustrated by examining QM in the healthcare industry. Essay 1 explains the relationship between operational performance and QM practices in the healthcare industry. This study analyzed the findings from the literature using meta-analysis. We applied confirmatory semantic analysis (CSA) to examine the Baldrige winners' applications. Essay 2 examines the benefits associated with an effective QM program in the healthcare industry. This study addressed the research question about how effective QM practice results in improved hospital performance. This study compares the performance of Baldrige Award-winning hospitals with matching hospitals, state average, and national average. The results show that the Baldrige Award can lead to an increase in patient satisfaction in certain periods. Essay 3 discusses the contribution of an online clinic appointment system (OCAS) to QM practices. An enhanced trust model was built on understanding the mechanism of patients' trust formation in the OCAS. Understanding the determinants related to patients' trust and willingness to use OCAS can provide valuable guidance for medical institutions to establish health information technology-based services in the quality service improvement programs. This research has three significant contributions. First, this research analyzes the role of QM practices in the healthcare industry. Second, this research attempts to develop a unique text mining method. Third, this research provides a validated trust model and contributes to the body of research on the trust of healthcare information technology.
A Study of the Intent to Fully Utilize Electronic Personal Health Records in the Context of Privacy and Trust: Government initiatives called for electronic health records for each individual healthcare consumer by 2014. the purpose of the initiatives is to provide for the common exchange of clinical information between healthcare consumers, healthcare providers, third-party payers and public healthcare officials.This exchange of healthcare information will impact the healthcare industry and enable more effective and efficient application of healthcare so that there may be a decrease in medical errors, increase in access to quality of care tools, and enhancement of decision making abilities by healthcare consumers, healthcare providers and government health agencies. an electronic personal health record (ePHR) created, managed and accessed by healthcare consumers may be the answer to fulfilling the national initiative. However, since healthcare consumers potentially are in control of their own ePHR, the healthcare consumer’s concern for privacy may be a barrier for the effective implementation of a nationwide network of ePHR. a technology acceptance model, an information boundary theory model and a trust model were integrated to analyze usage intentions of healthcare consumers of ePHR. Results indicate that healthcare consumers feel there is a perceived usefulness of ePHR; however they may not see ePHR as easy to use. Results also indicate that the perceived usefulness of utilizing ePHR does not overcome the low perceived ease of use to the extent that healthcare consumers intend to utilize ePHR. in addition, healthcare consumers may not understand the different components of usage: access, management, sharing and facilitating third-party ePHR. Also, demographics, computer self-efficacy, personal innovativeness, healthcare need and healthcare literacy impact a healthcare consumer’s privacy concerns and trusting intentions in the context of ePHR and intent to utilize ePHR. Finally, this research indicates that healthcare consumers may need a better understanding of the Health Insurance and Portability and Accountability Act of 1996 (HIPAA) regulations of ePHR as well as …
Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers: In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed. Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are two text data computer algorithms that have received much attention individually in the text data literature for topic extraction studies but not for document classification nor for comparison studies. Since classification is considered an important human function and has been studied in the areas of cognitive science and information science, in this dissertation a research study was performed to compare LDA, LSA and humans as document classifiers. The research questions posed in this study are: R1: How accurate is LDA and LSA in classifying documents in a corpus of textual data over a known set of topics? R2: How accurate are humans in performing the same classification task? R3: How does LDA classification performance compare to LSA classification performance? To address these questions, a classification study involving human subjects was designed where humans were asked to generate and classify documents (customer comments) at two levels of abstraction for a quality assurance setting. Then two computer algorithms, LSA and LDA, were used to perform classification on these documents. The results indicate that humans outperformed all computer algorithms and had an accuracy rate of 94% at the higher level of abstraction and 76% at the lower level of abstraction. At the high level of abstraction, the accuracy rates were 84% for both LSA and LDA and at the lower level, the accuracy rate were 67% for LSA and 64% for LDA. The findings of this research have many strong implications for the …
Three Essays on Artificial Intelligence Adoption and Use: Artificial intelligence (AI) is quickly transforming business operations and society, as AI capabilities are incorporated into applications ranging from mobile personal assistants to self-driving cars. The potentially disruptive nature of AI calls for an extensive investigation into all aspects of AI-human interactions at individual, group, organizational and market levels. However, there is paucity of academic information systems (IS) research in this area that goes beyond the development and testing of specific narrow AI capabilities. AI represents an important opportunity for organizational and behavioral IS researchers, but also presents challenges associated with the underlying complexity of AI technologies and the diversity of AI applications. Understanding how existing AI research and business practice relate to traditional areas of IS research is an important step towards creating a comprehensive behavioral and organizational AI research agenda. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to understand the current state of business AI research and practice in business through a quantitative literature review, relate the findings to traditional IS research areas, and identify potentially fruitful research areas for AI-focused IS research. Essays 2 and 3 seek to address specific research questions related to one of such research areas, namely, human interactions with AI enabled applications. Essay 2 focus on user experience with a chatbot, a popular AI application, and Essay 3 explores how user experiences with AI assistant apps differ from their interactions with more traditional IT artifacts.
Relationship Quality in Social Commerce Decision-Making: 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.
Hybrid Models in Automobile Insurance: Technology Adoption and Customer Relations: 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 framework for managers to devise effective approaches to enhancing the performance of their business.
Measurement of Positive Continuance Intention Drivers within a Service Domain: The contribution of this dissertation is how model measurement allows examination of the balance between what is practical in terms of consumer concerns versus what is optimal in terms of cost control. Essay 1 examines a research framework that incorporates various service recovery strategies and simultaneously evaluates their comparative influences. Essay 2 evaluates the complex interrelationships among different factors related to the post-complaint behavioral process. Essay 3 fills a research gap by examining the role of brand equity by operationalizing a reflective model using PLS in operations management (OM) research. These three essays provide insight into the quality management domain and the value that is achieved via a data driven examination of theory. Moreover, this research will provide operations management practitioners a basis to carry out future research on quality management phenomena as well as insight into how to balance cost control and service recovery strategies with the goal of achieving a competitive advantage.

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