A Human Centered Framework for Information Security Management: A Healthcare Perspective

Research on the human element of information security is fragmented at best. This paper presents a management framework for organizations in the health care industry who wish to improve their information security procedures in an effort to comply with HIPAA and other regulations. The emphasis is on securing an organization from internal threats by adequately educating employees and building an organizational culture where security initiatives are valued and respected. The premise of the paper is that a cultural approach is the only way to gain the versatile security environment needed to comply with regulations as vast and complex as HIPAA. We argue that this framework demands that empirical data be collected through careful industry research with health care providers so as to prove the real world value of its application

From Hashtags to Movements: A Framing Perspective of The Role of Social Media in the Emergence and Development of Impactful Social Movements

Social media plays a critical role in social movement activities. This study takes a framing perspective to investigate how social media affordances support the process of creation, communication, and negotiation of frames and assess the impact of the framing process on social movement outcomes

Effect of Explainable Artificial Intelligence and Decision Task Complexity on Human-Machine Symbiosis

Artificial Intelligence (AI) is a tool that augments various facets of decision making. This disruptive technology is helping humans perform better and faster with accuracy (Grigsby 2018). There are tasks where AI decides in real-time without human intervention. For example, AI can approve or decline a credit card application without any human intervention. On the other hand, there are tasks where both AI and human reasoning is required to make the decision. For instance, automated employee selection decision requires a higher level of human involvement. Interaction between humans and machines is required in such decisions. Grigsby (2018) posits that the interaction becomes effective when the machine understands human and human understands machine. This interplay is called human-machine symbiosis that merges the best of the human with the best of the machine. The human decision-makers need to understand how the machine is reaching to a specific prediction. One tool that facilitates this understanding by increasing the interpretability of the algorithm is Explainable AI (XAI). XAI is a tool that explains the results to the decision-maker in a human-understandable manner (Rai 2020). As a result, the decision is more transparent and fairer. Other than the benefits of transparency and fairness, there is an emerging regulatory requirement for explaining machine-driven decisions. The General Data Protection Regulation addresses the right to explanation by enabling the individuals to ask for an explanation for algorithm’s output (Selbst and Powles 2017). That is why the decision-makers need to convert their decision-making tool from a black box to a glass box. To enhance the explainability and interpretability, two broad categories of XAI techniques are model-specific XAI and model-agnostic XAI (Rai 2020). The model-specific techniques incorporate interpretability in the inherent structure of the learning model whereas the model-agnostic techniques use the learning model as an input to generate explanation. These models ensure transparency and fairness in human-machine decision making. Another important factor for effective human-machine symbiosis is decision task complexity (Grigsby 2018). Task complexity in decision making can be characterized by the number of desired outcomes, conflicting interdependencies among outcomes, path multiplicity, and uncertainty (Campbell 1988). When the decision-making task is unstructured and complicated, then the decision-maker’s need for understanding the algorithmic process increases. Moreover, decision task complexity is a factor of trust in the autonomous system, and trust is a factor of human-machine symbiosis (Grigsby 2018). Furthermore, decision task complexity is related to the mental workload and cognitive ability of the decision-makers (Grigsby 2018; Speier and Morris 2003). In the extant literature, there is a gap in explaining how the interplay between XAI techniques and decision task complexity impacts the decision makers perception about the human-machine symbiosis. Therefore, the objective of this research is to investigate the effect of XAI and decision task complexity on perceived human-machine symbiosis. Using the theories of information overload and algorithmic transparency, we develop a causal model to explain the relationship. We will run a randomized 2×2 factorial experiment to test the model. The paper will have theoretical and practical implications

Impact of Culture on Knowledge Management: A Meta-Analysis and Framework

Culture, both national and organizational, can have profound impacts on knowledge management. Yet the literature on exactly how culture impacts knowledge management is complex with no clear generalizable results. A meta-analysis was conducted on 52 articles from ten IS journals for the years 2000–2010 combining both quantitative and qualitative studies in a unique methodological approach. Key findings include a marked shift away from normative language towards more interpretive and critical discourse emphasizing the power issues inherent in the cultural context of knowledge management. Trust and openness are key organizational cultural dimensions that impact knowledge management processes, but these traits are achieved through effective business leadership, rather than a particular technological artifact. The most striking generalizable finding from the cross-case analysis is that organizational culture can overcome or mitigate differences in national culture. An overall framework is provided to illustrate the findings and to serve as an important guidepost for future research

Robust Optimization for Multiobjective Programming Problems with Imprecise Information

A robust optimization approach is proposed for generating nondominated robust solutions for multiobjective linear programming problems with imprecise coefficients in the objective functions and constraints. Robust optimization is used in dealing with impreciseness while an interactive procedure is used in eliciting preference information from the decision maker and in making tradeoffs among the multiple objectives. Robust augmented weighted Tchebycheff programs are formulated from the multiobjective linear programming model using the concept of budget of uncertainty. A linear counterpart of the robust augmented weighted Tchebycheff program is derived. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs

User misrepresentation in online social networks: how competition and altruism impact online disclosure behaviours

Their sheer size and scale give social networks significant potential for shaping popular opinions. While the spread of information and influence within social networks has been popular area of research for some time, more recently a research trend has appeared in which the researcher seeks to understand how users can aggressively influence community opinions, often using misrepresented or false information. Such misrepresentations by users are deeply troubling for any social network, where revenue-generation and their reputation depend on accurate and reliable user generated information. This study investigates the individual motivations that both promote and inhibit intentions towards personal information misrepresentation. These motivations are hypothesised to result from the dichotomy of competitive and altruistic attitudes existing with social network communities. Results of a survey analysis involving 502 users of Facebook offer insights useful for understanding social network information sharing practices. Marketing strategies, in particular, should benefit from the careful evaluation of the factors that lead to honesty (or dishonesty) among OSN users

Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection

A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer programming problems

ICT Policies in Developing Countries: An Evaluation with the Extended Design-Actuality Gaps Framework

Information and communication technologies (ICT) are often represented as a factor in global economic growth and social development. Consequently, countries and governments invest large amounts of resources in the ICT sector. However, it is not certain whether the results of these investments necessarily match expectations. In order to investigate this conundrum, this study evaluates government policies for Information Communication Technologies (ICT) growth in a developing country by extending and utilizing the design-actuality gaps framework. A qualitative analysis of government’s ICT policy documents (i.e., design) and interviews with 35 citizens and 54 government officials (i.e., actuality) shows significant design-actuality gaps. Additional insights are derived from two focus groups involving 11 citizens. The analysis shows that not only there are gaps between policy design and actuality but also the dimensions of design and actuality are different. The causes of these gaps are discussed along with implications for practitioners and a theoretical extension of the design-actuality gaps framework. This research contributes to the literature on design-actuality gaps, ICT in developing countries, and government policy evaluation

Feature reduction improves classification accuracy in healthcare

Our work focuses on inductive transfer learning, a setting in which one assumes that both source and target tasks share the same features and label spaces. We demonstrate that transfer learning can be successfully used for feature reduction and hence for more efficient classification performance. Further, our experiments show that this approach increases the precision of the classification task as well