In information systems design there are two schools of thought about what factors are necessary to create a successful information system. The first, conventional view holds that system performance is a key, so that efficiency characteristics such as system usability and task completion time are primary concerns of system designers. The second, emerging view holds that the visual design is also the key, so that visual interface characteristics such as visual appeal, in addition to efficiency characteristics, are critical concerns of designers. This view contends that visual design enhances system use. Thus, this work examines the effects of visual design on computer systems. Visual design exerts its influence on systems through two mechanisms: it evokes affective responses from IT users, such as arousal and pleasure and it influences individuals’ cognitive assessments of systems. Given that both affective and cognitive reactions are significant antecedents of user behaviors in the IT realm, it is no surprise that visual design plays a critical role in information system success. Human-computer-interaction literature indicates that visual aesthetics positively influences such information success factors as usability, online trust, user satisfaction, flow experience, and so on. Although academic research has introduced visual design into the Information Systems (IS) field and validated its effects, visual design is still very limited in three contexts: product aesthetics in e-commerce, mobile applications and commercial emails. This dissertation presents three studies to help fill these theoretical gaps respectively.
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.
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.
This study, utilizing Preston and Karahanna’s framework for shared vision development and Agency Theory, explores the impact of vision development factors and factors associated with incentive plans on shared vision and alignment. Results of the study confirm the strong relationship between shared vision and alignment, and indicate that having an effective management team is important with respect to developing and maintaining shared vision and alignment within the organization. Several vision development factors such as using the language of the business, participation on the top management team (TMT), and having knowledge of the business impact shared vision through their influence on teamwork. Also, results of this study suggest that participation on the TMT by the CIO/IT leader is more important than the individual’s position in the organizational hierarchy. In addition, attributes associated with incentive plans such as achievable and clear measures, measures linked to organizational goals, measures that align the interests of the individual with those of the organization, regular plan reviews, and using a balanced scorecard approach with respect to incentive plan design positively impact teamwork and shared vision. For practitioners, this highlights the importance of incentive plans as powerful tools that can be used to reinforce shared vision, effective teamwork, and alignment within the organization. Also, the CIO/IT leader needs to be knowledgeable of the business and must fill the role of both a technologist as well as an enterprise leader. This person must be an evangelist communicating the value and benefits of IT to the rest of the organization.
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 ...
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 ...
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 ...
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.
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.
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.
This research seeks to derive and examine a multidimensional definition of information security awareness, investigate its antecedents, and analyze its effects on compliance with organizational information security policies. The above research goals are tested through the theoretical lens of technology threat avoidance theory and protection motivation theory. Information security awareness is defined as a second-order construct composed of the elements of threat and coping appraisals supplemented by the responsibilities construct to account for organizational environment. The study is executed in two stages. First, the participants (employees of a municipality) are exposed to a series of phishing and spear-phishing messages to assess if there are any common characteristics shared by the phishing victims. The differences between the phished and the not phished group are assessed through multiple discriminant analysis. Second, the same individuals are asked to participate in a survey designed to examine their security awareness. The research model is tested using PLS-SEM approach. The results indicate that security awareness is in fact a second-order formative construct composed of six components. There are significant differences in security awareness levels between the victims of the phishing experiment and the employees who maintain compliance with security policies. The study extends the theory by proposing and validating a universal definition of security awareness. It provides practitioners with an instrument to examine awareness in a plethora of settings and design customized security training activities.
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 large southwestern university in the U.S. Exploratory factor analysis, confirmatory factor analysis, and structural equation modeling with both LISREL and PLS-Graph are used to validate the comprehensive instrument and test the research hypotheses. The results provide theoretical and normative guidelines for researchers and practitioners in the e-commerce domain. The research results will also help e-commerce platform providers or e-retailers to improve their business and marketing strategies by providing a better understanding of the most important factors influencing customer behavioral intentions.
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.
The social network sites (SNS) industry has recently shown an abnormal development pattern: An SNS could rapidly accumulate a large number of users, and then suffer a serious loss of users in a short time, which subsequently leads to the failure of the Web site in the highly competitive market. The user's social network site continuance is considered the most important factor for an SNS to keep its sustainable development. However, little knowledge of the user's SNS continuance raises the following research question: What factors could significantly influence the user's SNS continuance intention? To address this research question, I study the question from three lenses of research, including the I-view, the social interactivity view, and the trust based view. The I-view is an extension of the IS continuance model. From this research perspective, I tested the influence of the utilitarian factor (i.e., perceived usefulness) and the hedonic factor (i.e., perceived enjoyment) on the user's satisfaction in the I-view. In addition, I extend the umbrella construct, confirmation, into two sub-constructs, informativeness and self-actualization, and respectively study their influences on the utilitarian factor and the hedonic factor. I find that the user's perceived enjoyment has a significant positive effect on the user's satisfaction, thereby motivating the user to continue using the SNS. The perceived informativeness of an SNS and the user's self-actualization through information sharing with others on the Web site both have significant positive effects on the user's perceived usefulness and perceived enjoyment. From the social interactivity perspective, I suggest that a user's social gains could have a projection effect on the user's satisfaction in an SNS and his or her SNS continuance intention. Most previous studies emphasized on the influence of social connection outcomes (i.e., social capitals) on the user's behavioral intention, but ignored the fact that an individual would ...
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 ...
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.
We are entering an era of business intelligence and big data where simple tables and other traditional means of data display cannot deal with the vast amounts of data required to meet the decision-making needs of businesses and their clients. Graphical figures constructed with modern visualization software can convey more information than a table because there is a limit to the table size that is visually usable. Contemporary decision performance is influenced by the task domain, the user experience, and the visualizations themselves. Utilizing data visualization in task performance to aid in decision making is a complex process. We develop and test a decision-making framework to examine task performance in a visual and non-visual aided decision-making by using three experiments to test this framework. Studies 1 and 2 investigate DV formats and how complexity and design affects the proposed visual decision making framework. The studies also examine how DV formats affect task performance, as measured by accuracy and timeliness, and format preference. Additionally, these studies examine how DV formats influence the constructs in the proposed decision making framework which include information usefulness, decision confidence, cognitive load, visual aesthetics, information seeking intention, and emotion. Preliminary findings indicate that graphical DV allows individuals to respond faster and more accurately, resulting in improved task fit and performance. Anticipated implications of this research are as follows. Visualizations are independent of the size of the data set but can be increasingly complex as the data complexity increases. Furthermore, well designed visualizations let you see through the complexity and simultaneously mine the complexity with drill down technologies such as OLAP.
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.
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.
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