IT Alignment: Different Firm Types, Different Alignment Configurations

As opposed to the earlier IT-business alignment research where there appears to be a tendency to view realization of alignment through a simple universal approach and in line with the call to examine how the nature of alignment varies across firm types, this study discusses the IT-business alignment dimensions from the five ideal organizational archetypes perspective. The study suggests an integrative framework for IT-business alignment as a function of Mintzberg’s five organizational archetypes. A qualitative case study research methodology is discussed. Enriching the model with the qualitative study and generalizing the results with a quantitative survey study will have important implications for theory and practice

Theory-based Taxonomy of Feedback Application Design for Electricity Conservation: A User-Centric Approach

Electricity consumption feedback applications are considered one of the critical technologies in alleviating the increasing trends of energy consumption and greenhouse gases emissions. Feedback applications are used to motivate electricity users to conserve energy in their households. In this paper, we have relied on an integrative theoretical framework and literature review to propose a comprehensive taxonomy for salient design elements of electricity consumption feedback applications. Using a survey method, we collected data to evaluate the preference and relative importance of the design elements. We found that there is a preferred set of design elements for the feedback applications. Our results could serve as a basis to evaluate the design of existing electricity consumption feedback applications, and help in studying the influence of design elements on beliefs and behaviors related to individuals’ electricity conservation

A Systematic Review on Using Hacker Forums on the Dark Web for Cyber Threat Intelligence

Urgent warnings for private businesses and public organizations to monitor and predict disruptive cyberattacks have been on the rise. The annual cost of cyber-attacks in the worldwide economy is expected to be more than $10.5 trillion in 2025. To that end, new methods are being developed to fight cyberattacks. One such method builds upon leveraging cybercriminal/hacker forums on the dark web to design ‘cyberthreat intelligence’ solutions. The dark web, which is not accessible by the conventional browsers that are normally used to access the surface web, is the part of the web where most of the illegal and illicit content is hosted. It is a major market resource for cybercriminal-hackers for trading and developing cyberthreat content (e.g., malware; novel hacking methods; malicious source code). Therefore, the study of designing cyber threat intelligence solutions (i.e., methods; artifacts) based upon analyzing hacker forums has been undertaken in the literature. To enhance this structured inquiry and to formulate new research directions, we conduct a systematic literature review on leveraging hacker forums and designing ‘threat intelligence’ solutions. In our systematic review, we report our findings based on the PRISMA - Preferred Reporting Items for Systematic Reviews and Meta-Analyses - checklist. We conducted our search on Scopus and Ebscohost, and our search query was the following: (“dark web” OR “dark net” OR “darknet” OR “hacker* forum” OR “underground forum ) AND ( security OR threat intelligence ). Our search included abstracts and English-language documents published in peer-reviewed journals and conferences. We extracted a total of 295 papers and retained 69 papers. Our findings indicate the proposed threat intelligence solutions have been built upon the analysis of different forms of unstructured data, including text, videos, and images. Different solutions had different objectives, including: (1) key actor (hacker) identification (i.e., identifying the key active hackers on the forum who actively engage in and lead discussions and posts), (2) hacker ranking according to expertise (i.e., ranking the forum participant hackers based on their hacking domain-knowledge expertise reflected in their posts), (3) malware identification (i.e., identifying novel malware from hackers’ posts on the forums), and (4) organizational information security risk management and mitigation (i.e., identifying organizational vulnerabilities and developing strategies to mitigate them based on the knowledge retrieved from hacker forums). We found that as of now, the proposed solutions do not consider the factor of temporality, or temporal-based dynamism, in the forums. Key hackers may change, expertise may change, and vulnerabilities may evolve in organizations. We hope that our review catalyzes future research in this area

Designing Privacy Policies with Users: A Human-Centered Approach

Users’ privacy concerns over their electronic data and how it is used across different digital platforms have grown in recent years. New regulations and policies (e.g., General Data Protection Regulation; GDRP) have been developed to grant users their rights to data transparency and intervenability. To that end, ex-post transparency tools have been offered to provide users with insights into how their data is used by business entities. Nonetheless, these tools do not consider individuals’ privacy concerns ex-ante technology design. While ex-post transparency tools attempt to address users’ privacy concerns, they remain limited in terms of users’ agency and autonomy, and thereby do not consider users’ voices. In contrast, ex-ante human-centered design processes would achieve that. Therefore, this research proposes a human-centered approach for designing data privacy policies with users rather than for users. To develop this approach, we primarily draw upon the human-centered design framework, commonly used in the field of Human-Computer Interaction (HCI). We compile and then use the design principles in the extant literature. The overarching objective of this approach is to understand users’ “privacy” needs and thus facilitate a mutual understanding of users’ priorities, values, and constraints. As such, co-designing data policies with users would give them agency and autonomy to actively participate in the design process. We hope that our proposed approach will allow for designing more effective privacy policies

Theory-based Taxonomy of Feedback Application Design for Electricity Conservation: A User-centric Approach

Many consider electricity-consumption feedback applications a critical technology in alleviating energy consumption and greenhouse gas emissions. In this paper, following the design science paradigm, I examine a design taxonomy of electricity-consumption feedback applications. I relied on an integrative theoretical framework and literature review to propose a comprehensive taxonomy for salient design elements of electricity-consumption feedback applications. Using a survey method, I collected data from general public to evaluate preferences for and relative importance of the design elements. I found a preferred set of design elements for the feedback applications. The results could serve as a basis to evaluate the design of existing electricity-consumption feedback applications and to help in studying the influence that design elements have on beliefs and behaviors related to individuals’ electricity conservation

Enhancing Trust Equity by Web-Design Elements that Manifest Pro-Environment Commitments

This research explores how manifesting a firm’s commitments to green environment and ecological sustainability by web-design elements may build online trust equity for the firm. Using an exploratory analysis of websites, we have identified four categories of web-design elements that could manifest companies’ commitment for green environment. Survey data indicates that these categories of web elements have the potential to change customers’ opinions regarding a company. To explore this potential, we propose a conceptual model that explores the relationship between the level of web vendors’ pro-environmental commitment and online trust equity as moderated by their salient environmental beliefs. The conceptualization uses the protection motivation theory as the overarching theory. This paper makes number of novel contributions. It is the first identify the categories of web-design elements that manifest companies’ extent of pro-environment commitments. It develops a theory-based conceptual model to examine the relationship between environmentally sustainable activities and online trust equity. Moreover, beyond legal compliance and cost reduction benefits, this research project seeks to examine if the commitment to green environment as manifested in web elements adds business value to companies in terms of trust equity in relationships with its stakeholders

Impact of Knowledge Creation on Financial Performance: An Exploratory Study

Evaluation of the ability of a firm to create new knowledge and to succeed in its future research and development projects possess top interest for different parties such as investors, consultants, and board directors. Prior literature has suggested a relationship between knowledge creation and market value of a firm. In this study, we suggest a design science approach using data mining tools to build a model to explore the relationship between financial performance of a firm as reflected in financial statements and the success of knowledge creation represented in patent success. To achieve this objective, we use clustering analysis, attribute evaluation, and classification algorithms. Our data comprises of 2,500 firm year financial data encompassing 10 accounting measures. Utilizing the design science paradigm and business analytics tools to examine the relationship between financial performance and knowledge creation will have significant implications for theory and practice

Tree-Based Algorithm for Stable and Efficient Data Clustering

The K-means algorithm is a well-known and widely used clustering algorithm due to its simplicity and convergence properties. However, one of the drawbacks of the algorithm is its instability. This paper presents improvements to the K-means algorithm using a K-dimensional tree (Kd-tree) data structure. The proposed Kd-tree is utilized as a data structure to enhance the choice of initial centers of the clusters and to reduce the number of the nearest neighbor searches required by the algorithm. The developed framework also includes an efficient center insertion technique leading to an incremental operation that overcomes the instability problem of the K-means algorithm. The results of the proposed algorithm were compared with those obtained from the K-means algorithm, K-medoids, and K-means++ in an experiment using six different datasets. The results demonstrated that the proposed algorithm provides superior and more stable clustering solutions

Meta for Faith – How Digital Technology is Reconfiguring Faith-Based Institutions

The Covid-19 global pandemic has accelerated the adoption of digital technologies in societies. These developments are evident across institutions; even faith-based institutions that have previously been hesitant have experienced pressure to adopt digital technologies. In this realm, Meta launched two initiatives in 2020 where members of different religious communities engage with Meta to dialogue about applying digital technologies to their missions’ purpose and express and celebrate their faith. The impact of digital technologies utilization in this context has led, among others, to the boundary spanning behavior of religious communities. We use this case of Meta for Faith and leverage institutional theory to identify the affordances of digital technologies in faith based institutions. Thus, we contribute by explaining how technology is transforming faith-based institutions

Tree-Based Algorithm for Stable and Efficient Data Clustering

The K-means algorithm is a well-known and widely used clustering algorithm due to its simplicity and convergence properties. However, one of the drawbacks of the algorithm is its instability. This paper presents improvements to the K-means algorithm using a K-dimensional tree (Kd-tree) data structure. The proposed Kd-tree is utilized as a data structure to enhance the choice of initial centers of the clusters and to reduce the number of the nearest neighbor searches required by the algorithm. The developed framework also includes an efficient center insertion technique leading to an incremental operation that overcomes the instability problem of the K-means algorithm. The results of the proposed algorithm were compared with those obtained from the K-means algorithm, K-medoids, and K-means++ in an experiment using six different datasets. The results demonstrated that the proposed algorithm provides superior and more stable clustering solutions