A Novel Approach to Predict the Helpfulness of Online Reviews

Online reviews help consumers reduce uncertainty and risks faced in purchase decision making by providing information about products and services. However, the overwhelming amount of data continually being produced in online review platforms introduce a challenge for customers to read and judge the reviews. This research addresses the problem of misleading and overloaded information by developing a novel approach to predict the helpfulness of online reviews. The proposed approach in this study, first, clusters reviews using reviewer-related, and temporal factors. It then uses review-related factors to predict online review helpfulness in each cluster. Using a sample of Amazon.com reviews, the empirical findings offer strong support to the proposed approach and show its superior predictions of review helpfulness compared to earlier approaches. The outcomes of this study help customers in online shopping and assist online retailers in reducing information overload to improve their customers’ experience

BI-based Organizations: A Sensemaking Perspective

Business intelligence (BI) offers opportunities for managers to master vast data resources for operational and strategic gains, and allows BI-based organizations to generate significant business value. While several researchers emphasized the importance of BI to assist making quality decisions, no study explored the use of BI for improved understanding of business before such decisions are made and assessing the impact of the actions derived from these decisions. To fill this gap we use the theory of organizational sensemaking. The presented research uses hermeneutic phenomenology to study the experiences of decision-makers in using BI-generated insights to guide their actions while altering business processes, structures and information. The study emphasizes the necessity of using BI in the creation and maintenance of individual and organizational identity, as well as, enactment of this identity on the business and its environment, which need to be moulded in response to changing circumstances

Using business intelligence to support the process of organizational sensemaking.

Morteza’s thesis investigated the opportunities in using BI technologies to make sense of a business environment. The results of his research highlighted the need for creating and maintaining an identity for Business Intelligence at both individual and organizational level and enacting this identity on the business and its environment

Revisiting Review Depth in Search for Helpful Online Reviews

This study investigates online review features that constitute review depth and assess their impacts on review helpfulness. It develops a model capturing the moderating effects of heuristic and systematic cues of an online review on the relationship between review length and its helpfulness. In particular, this study examines the moderating effects of price, product type, review readability and the presence of two-sided arguments. For testing the model, a dataset of 568,454 reviews from 256,059 different reviewers on Amazon.com were analyzed. The variables were operationalized using test processing techniques and relationships were empirically tested using regression and machine learning models. The results highlight significant moderating effects of review readability and the presence of two-sided arguments on the relationship between review length and its helpfulness. However, the results did not confirm the moderating effects of price and product type. This article discusses the significant implications for a better understanding of review depth and helpfulness in e-commerce platforms

Knowledge Identity (KI): A New Approach to Integrating Knowledge Management into Enterprise Systems

Despite the extensive studies about KM over the past four decades, the discipline still lacks a clear and practically comprehensive understanding of how KM can be integrated into enterprise systems. To a high degree, the issue is associated with the ambiguous assumptions taken by organizations about knowledge. Many of the assumptions of information systems theories about knowledge require revision, particularly how knowledge is managed. Conceptualizing knowledge as processed data and information has led contemporary design and implementation of enterprise systems to fail to capture the complexity of knowledge. In this article, we critically examine these views. We argue that the answer to the question as to how and to what extent enterprise systems can support KM, depends on the assumptions that organizations take towards the nature and sources of knowledge. To address this question, we introduce the concept of Knowledge Identity (KI) and a model of Enterprise Knowledge Integration

A Review of Hate Speech Detection: Challenges and Innovations

Hate speech on social media platforms has severe impacts on individuals, online communities, and society. Platforms are criticized for shirking their responsibilities to effectively moderate hate speech on their platforms. However, Various challenges, including implicit expressions, complicate the task of detecting hate speech. Consequently, developing and tuning algorithms for improving the automated detection of hate speech has emerged as a crucial research topic. This paper aims to contribute to this rapidly emerging field by outlining how the adoption of natural language processing and machine learning technologies has helped hate speech detection, delving into the latest mainstream detection techniques and their performance, and offering a comprehensive review of the literature on hate speech detection online including the notable challenges and respective mitigating efforts. This paper proposes the integration of interdisciplinary perspectives into deep learning models to enhance the generalization of models, providing a new agenda for future research

Towards explaining user satisfaction with contact tracing mobile applications in a time of pandemic: a text analytics approach

This research project investigates the critical phenomenon of the post-adoption use of Contact Tracing Mobile Applications (CTMAs) in a time of pandemic. A panel data set of customer reviews was collected from March 2020 to June 2021. Using sentiment analysis, topic modeling and dictionary-based analytics, 10,337 reviews were analyzed. The results show that after controlling for review sentiment and length, user satisfaction is associated with users’ perception of utilitarian benefits of CTMA, their CTMA-specific privacy concerns, and installation and use issues. Our methodological approach (using various text analysis techniques for analyzing public feedback) and findings (influential factors on consumers’ satisfaction with CTMA) can inform the design and deployment of the next generation of CTMAs for managing future pandemics

On Justification: Legislating a Digital First Artifact

The \u27digital first\u27 paradigm and its ontological reversal proposition bring new risks and implications for governing and regulating digital technologies. This article reports the findings from a qualitative study of the justifications used in legislating a \u27digital first\u27 artifact: Australia’s COVIDSafe contact tracing app. We build on justification theory (‘orders of worth’ framework) and use deductive qualitative analysis for examining 74 parliamentary records of proceedings (Hansards) in 2020 and 2021. The findings are structured in 38 empirical themes and 15 conceptual categories, which pertain to five orders of worth used in justifying the actors’ positions. This research unpacks the complexities of the justifications invoked in the legislative debates and sheds light on the novel and important yet understudied practices of governing ‘digital first’ artifacts

What We Don’t Know (Yet) about Human-AI Collaboration

New questions about how humans can – and should – collaborate with Artificial Intelligence (AI) are emerging rapidly with the emergence of generative AI solutions like “ChatGPT”. The AI solutions available today go far beyond previously considered IT because it can re-code procedures, transform data, generate content, and thus alter the process and outcomes of work at an unprecedented scale. The consequence of this development is the question of whether AI is outperforming and replacing humans at non-routine tasks such as knowledge work (KW). This is a non-trivial question because knowledge worker (KWers) and the knowledge-intensive organizations embedded in were, for a long time, seen to be seemingly unaffected by the technological developments stemming from AI. Today, however, there is very limited understanding of the ways that KWers adjust to, and integrate, AI at work. This includes questions addressing ethical concerns related whether technology inhibits or facilitates KWers. With these theoretical challenges in mind, this research in progress sets out to sets out to address the existing research gaps existing in human-AI collaboration within knowledge-intensive domain: 1) there is out-of-dated understanding of relationship between the use of technology and the evolution of KW; 2) how are KWers highly attached with technology influenced by AI; and 3) the expectation about how human-AI collaboration should shape the nature of KW still remain unclear. Thus, this research aims to revisit the concept of KW in light of ongoing AI technology progress, outline the AI-driven phenomenon in knowledge-intensive domain and generate in-depth insights on how human–AI collaboration is reshaping the nature of KW

Iterative Seed Word Generation for Interactive Topic Modelling: a Mixed Text Processing and Qualitative Content Analysis Approach

Topic models have great potential for helping researchers and practitioners understand the electronic word of mouth (eWoM). This potential is thwarted by their purely unsupervised nature, which often leads to topics that are not entirely explainable. We develop a novel method to iteratively generate seed words to guide the interactive topic models. We assess the validity and applicability of the proposed method by investigating the critical phenomenon of Contact Tracing Mobile Applications (CTMAs) post-adoption during a time of the COVID-19 pandemic. The results show that constructs developed through our interactive topic modeling can capture primary research variables related to the phenomenon. Compared to existing topic modeling methods, our approach shows superior performance in explaining users’ satisfaction with CTMAs