Introduction to the Special Issue on Thriving Amidst Disruptive Technologies
Bibliographic record
Abstract
Disruptive technologies are thriving to replace the dominant technologies in many industry sectors.Thus, there is a need for a set of theories and technical works that can predict the probability of success of disruptive technologies at their early stages.Referring to the Technology-Organization-Environment (TOE) framework, technological, organizational, and environmental readiness affect enterprises' success in adopting and implementing disruptive technologies.For example, people believe that artificial intelligence (AI) and blockchain are two of the most disruptive technologies that make our world increasingly connected.Further, it is essential to consider the implications of these disruptive technologies and their integrations on security and privacy.For example, blockchain introduces challenging Internet of Things (IoT) security problems.The theme of this special issue is to provide a platform to discuss theoretical and technical approaches, strategies, solutions, and applications to support business transformation in a disruptive technological environment.We solicited research and industry papers related to these specific challenges and others driving innovation in this topic and related research issues, including ( 1) Big Data, Data Analytics, and Business Intelligence; (2) Enterprise Systems and Knowledge Management; (3) Digital Transformation, Management, and Governance; (4) Information Security, Privacy, and Risk Management; (5) Digital Information Systems in the Public Sector, Healthcare, Telecommunications, Transport and Education; (6) Digital Business Platforms, Blockchain, Social Networking, and the IoT; (7) Regional Perspectives on Digital Information Systems; (8) Artificial Intelligence (AI), Robotics, and Machine Learning; (9) Augmented Reality (AR) and Mixed Reality (XR); and (10) Case Studies (e.g., healthcare, customer service, aviation, etc.).This special issue provides the fundamentals of thriving amidst disruptive technologies, covering their computational development, technical capabilities, and roles in academic, societal, corporate, and governmental strategies.The special issue also provides clear evidence that disruptive technologies play an ever-increasingly essential and critical role in supporting our daily life and future, a new discipline for interdisciplinary research in business, information systems, and even social sciences.Two research papers have been presented on this special issue.Referring to the first paper, Ho et al. [ 2024 ] summarized the discussion of how blockchain and distributed ledger technologies can help tackle the fake news and misinformation problem at the 15th International Conference on Information Resources Management [Conf-IRM 2022 ] on October 18, 2022.In the second paper, Zhao et al. [ 2024 ] presented a quantitative metric and language-dependent single qualitative analysis of conformance between legal and smart contracts for constructing the secure blockchain.For future research directions, the AI-driven capability allows companies to gather real-time data from multiple sources, aiding strategy formulation and decision-making [Raj et al. 2023 ].Generative AI (GAI) should be imperative to imbue it with empathy, ethical considerations, and a human-centric approach, referred to as
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.085 | 0.034 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".