A Statistical Comparison of Classification Algorithms on a Single Data Set

This research uses four classification algorithms in standard and boosted forms to predict members of a class for an online community. We compare two performance measures, area under the curve (AUC) and accuracy in the standard and boosted forms. The research compares four popular algorithms Bayes, logistic regression, J48 and Nearest Neighbor (NN). The analysis shows that there are significant differences among the base classification algorithms—J48 had the best accuracy. Additionally, the results show that boosted methods improved the accuracy of logistic regression. ANOVA was used to detect the differences between the algorithms; post hoc analysis shows the differences between specific algorithms

Argument Quality, Peripheral Cues and the Elaboration Likelihood Model

The paper discusses the concerns around Elaboration Likelihood Model, in particular regarding how to measure argument quality and peripheral cues

Origami: An Active Learning Exercise for Scrum Project Management

Scrum is a popular project management model for iterative delivery of software that subscribes to Agile principles. This paper describes an origami active learning exercise to teach the principles of Scrum in management information systems courses. The exercise shows students how Agile methods respond to changes in requirements during project implementation, one of the four Agile principles, in a deeper manner than many Agile active learning exercises. This learning activity uses an uncommon approach in Agile exercises in that tasks are provided, estimates made, progress is measured, and pivots to new tasks can be introduced based on task progress. All students were introduced to Scrum through two different lessons – one lecture-focused and one activity-focused. Students were surveyed after each lesson to determine lesson effectiveness. Students indicated they understood Agile concepts after completing the exercise and found the activity engaging. Students’ perceptions of Agile were similar for both lecture and activity lessons. The results from the study find that students’ perception of Agile learning increased when they had the lecture followed by the activity. If class time is constrained to a single lesson then the activity would be more beneficial than the lecture. Detailed instructions are provided for instructors to complete this activity

Online Video Reviews Helpfulness: Exploratory Study

Online reviews assist consumers in making an informed purchase decision and they became a trusted source for product information. This study aims to investigate online video reviews on YouTube to understand what are the most commonly reviewed products and what are the factors of YouTube video reviews which contribute to review helpfulness. We use qualitative and quantitative techniques as research methodologies. The results show that major categories reviewed on YouTube are video games, movies, and technology. Exploratory factor analysis revealed four important factors that may determine online video review helpfulness which are review popularity, comments, video information, and review depth. A conceptual model is introduced based on the factor analysis. The study has significant implications to research as it provides new insights regarding the role of online video reviews in purchases decision making process

Risky Behavior: A Three-Factor Adoption Model

This study seeks to understand the convenience versus privacy risk debate that many consumers knowingly and unknowingly participate in every day, as well as the impacts of these information privacy and trust concerns on innovation adoption. We investigate the relationship between the motivation, perception, and intention-to-use of reputation-aware applications, particularly regarding possible privacy threats. Our theoretical model presents a unique and novel interpretation of technology acceptance of reputation-aware systems that have high privacy risks. Specifically, we combine PMT and TAM to propose a three-factor technology adoption model to evaluate the risk, coping, and benefit calculus of technology adoption. We pose two research questions: (1) What are the factors influencing a user’s assessment of the benefits and threats adopting reputation-aware applications?; and (2) Does a three-factor adoption model present greater predictive power to assess behavioral intentions? The results of our empirical evaluation reveal that behavioral intent regarding the adoption of reputation-aware applications can be predicted using a three-factor adoption model, with the findings significant and reliable across all three factors. Additionally, the results show that the conceptual model has improved predictive power over existing acceptance models in the context of reputation-aware applications. The TAM/TPB components were able to predict approximately 62% of the variance of behavioral intention, whereas the PMT and TPB model was able to predict 74% of the variance of behavioral intention

A Guide for Purposive Sampling on Twitter

In this paper, we demonstrate how to use Twitter to conduct behavioral research and to guide researchers who might benefit from using this social media platform to effectively recruit survey participants. We begin by discussing the advantages researchers gain from using Twitter to recruit subjects for surveys, such as respondent anonymity, purposive sampling (which allows researchers to find respondents who participate in a topic of interest), the ability to reach respondents quickly to investigate ephemeral events, and advantages in replicating subject populations in recruitment. We offer a guide that illustrates the mechanics of using Twitter to recruit subjects and present a successful case study that illustrates how we used this technique in the real world to recruit survey participants. We provide solutions for common issues researchers might encounter when using Twitter to recruit subjects, such as nonresponse bias due to not responding to tweets in a timely manner, initial unwillingness to participate, and the inability to find appropriate survey respondents

A Guide for Stakeholder Analysis in IS/IT Management and Research: The Case of Broadband Availability in Rural North Carolina

Stakeholder analysis is a methodology that can provide valuable insights about a phenomenon. Information systems and information technology researchers have utilized stakeholder analysis to understand and learn from successes, failures, and other aspects of IS/IT initiatives. In this tutorial, we provide guidelines for conducting a stakeholder analysis currently missing in the IS/IT discipline despite being called for a long time. For our analysis, we review and combine studies from within the IS/IT discipline with work in organizational and strategic management and public policy. Our guidelines start with determining who the stakeholders are related to a phenomenon and what key concerns these stakeholders have about the phenomenon. In the next step, we relate stakeholders to one another and across the key concerns and point out how to identify possible coalitions. Last, we describe how to apply these findings to determine strategies for managing stakeholders or build theory around a phenomenon and its concerns. These final steps can be used to make policy recommendations, provide guidance for IS/IT-related initiatives, or present constructs and relationships that can be tested by future researchers. We demonstrate the applicability of our guidelines with a case study about broadband availability in rural North Carolina

A Guide for Stakeholder Analysis in IS/IT Management and Research: The Case of Broadband Availability in Rural North Carolina

Stakeholder analysis is a methodology that can provide valuable insights about a phenomenon. Information systems and information technology researchers have utilized stakeholder analysis to understand and learn from successes, failures, and other aspects of IS/IT initiatives. In this tutorial, we provide guidelines for conducting a stakeholder analysis currently missing in the IS/IT discipline despite being called for a long time. For our analysis, we review and combine studies from within the IS/IT discipline with work in organizational and strategic management and public policy. Our guidelines start with determining who the stakeholders are related to a phenomenon and what key concerns these stakeholders have about the phenomenon. In the next step, we relate stakeholders to one another and across the key concerns and point out how to identify possible coalitions. Last, we describe how to apply these findings to determine strategies for managing stakeholders or build theory around a phenomenon and its concerns. These final steps can be used to make policy recommendations, provide guidance for IS/IT-related initiatives, or present constructs and relationships that can be tested by future researchers. We demonstrate the applicability of our guidelines with a case study about broadband availability in rural North Carolina

Predicting social networking sites continuance intention: Should I stay or should I go?

This research develops and tests models to predict continuance intention on social networking sites. The models adds new factors which are relevant to social networking sites continuance intention. The social networking site continuance model adds five factors: personal innovativeness, habit, attitude toward alternatives, interpersonal influence, and consumer switching costs to enhance the predictive power of information systems continuance. Interpersonal influence, alternative perceptions and procedural and relational costs are theorized to have a direct effect on continuance intention. Personal innovativeness and habit are theorized to have a direct and moderating effects on continuance intention. The results have a large positive effect of the explanatory power in explaining more of the variance of continuance intention on a social networking site. The information systems (IS) continuance model explains approximately 66.8% of the variance and the social networking site continuance model with the five added factors explains 76.7% of the variance and is considered to have a large effect in the explained variance. All of the factors have statistical significance; the factors with the largest path coefficients are, in order, satisfaction & perceived usefulness (β = 0.3686), consumer switching costs (β = 0.2496), alternative perceptions (β = -0.2069), habit (β = 0.1642), personal innovativeness (β = -0.0589) and interpersonal influence (β = -0.0451). Habit and personal innovativeness, as moderators, were not statistically significant and did not substantially aid in the interpretation of the factors. The research helps explains the relevant factors for why users of social networking sites will continue to use or abandon a site

An Exploration of Agile Project Management & Technical Debt

Concerns about technical debt have risen with the adoption of agile project management. The relationship between technical debt and agile techniques is still unclear (Kruchten, 2012). Cunningham (1992) describes technical debt as immature code that works fine and is acceptable to the customer but needs to be rewritten before negative outcomes will occur. The negative outcomes of technical debt occur in two major areas in software development; issues of evolvability and maintainability (Kruchten, 2012). Kruchten notes technical debt can be visible (e.g., additional time to generate new features, the number of defects, low external quality) and invisible (e.g., low internal quality, documentation debt, architectural debt). Some debt will be measurable and quantifiable where other debt is hard to assess and qualitative, e.g. software engineers will discuss code smells for code that is not quite right. \ \ Many methods fall under the agile project management umbrella, including Scrum and Extreme Programming (XP). Agile methods generally follow an iterative and incremental approach to product development where software is frequently delivered (Beck, 2001). Scrum has little to no effect the engineering of the software code directly but is more project management focused. XP includes both engineering practices and management practices and attempts to address technical debt through refactoring, specifically refactoring mercilessly, the application code. The extent to which agile methods discourage or encourage debt is unclear (Holvitie, 2014). \ \ Technical debt is in the software code, and software engineers are responsible for generating the technical debt. But technical debt is both a software problem and a management problem. Management and engineering are responsible for management of the debt. \ \ How might technical debt concerns be addressed from the perspective that management and engineering share responsibility? What would technical debt look like from an ideal perspective? Who drives technical debt, the software engineers, management, both? Technical debt has some positive aspects like the ability to speed-up the short-term delivery of working software and negative aspects slowing down the longer-term delivery, or susceptibility to bugs. What is the role of engineering, who creates the debt, in the elimination of the debt? How can management be informed of both the quantifiable concerns and the qualitative concerns of technical debt?