International Journal of Technology Intelligence and Planning (5 papers in press)

Regular Issues

• Research on Initial Coin Offerings: a Bibliometric Analysis  
  by Wan Mohd Hirwani Wan Hussain, Abu H. Ayob, Alya Geogiana Buja, Rabiah Ahmad 
  Abstract: Advanced business activities today have driven the emergence of new technologies for facilitating complex financial transactions. As such, initial coin offerings (ICOs) have become an alternative for companies and investors in diversifying capital investment. Despite its promising future, little is known on the progress of the academic research on this subject. To shed light, our study reviews a total of 184 publications on ICOs from the Scopus database. The analysis focuses on prominent pattern of leading institutions, impactful research, citation trends, and keyword analysis. From that, this study contributes to improve understanding on a novel yet under-researched niche of ICOs for the benefits of both academics and practitioners.
  Keywords: Initial coin offerings; Blockchain; Bibliometrics; Scopus database; VOSviewer.
  DOI: 10.1504/IJTIP.2025.10075812
   
  
• Aligning AI: a New Paradigm for Decision-Making in Resource-Limited Agritechs  
  by Hussein Lakkis, Helmi Issa 
  Abstract: Artificial Intelligence (AI) is transforming businesses by driving automation, predictive analytics, and data-driven decisions across industries. However, the intersection of AI's unpredictability with resource-limited and risk-sensitive sectors like agriculture create uncertainties and challenges that demand cautious management. This research empirically examines the impact of three diverse AI characteristics (i.e., autonomy, ambidexterity, and alignment) on decision-making with resource allocation as a moderator in the context of agritechs. Data was collected from multiple sources that mainly focused on agritech (agriculture technology) startups in France (n= 151). The findings revealed significant linear relationships for autonomy and ambidexterity characteristics and a nonlinear relationship for the alignment characteristic. This research introduces "alignment" as a new AI characteristic for optimal decision-making and proposes "Amber AI" as a transformative paradigm beyond Red and Green AI. It also develops practical simulation-based tools for detecting AI misalignment and optimizing resource allocation in agricultural management.
  Keywords: AI; Agriculture; resource allocation; decision-making.
  DOI: 10.1504/IJTIP.2026.10076838
   
  
• Navigating the digital frontier: catalysts and constraints for Indian dairy cooperatives  
  by K. Latha, Archana Petro, Remya Lathabhavan, E. Sulaiman 
  Abstract: This research identifies and assesses the critical factors that facilitate or impede the adoption of digital technology in the South Indian dairy cooperative sector. A comprehensive survey of dairy plants and dairy cooperative societies was conducted between 2022 and 2024, involving 873 senior managers and officials. Multivariate regression and confirmatory factor analyses were used to analyse the data. The findings indicate that while initial investment costs, limited/absence of in-house IT personnel, and insufficient technical expertise impede the adoption of digital technology, other factors that influence its adoption include perceived efficiency, benefits, competitive pressure, and information linkage. Unlike other studies that focus on selected dimensions of IT implementation in SMEs and large organisations, this paper discusses the multidimensional aspects of digitalisation in South Indian dairy cooperatives, including technological, institutional, social, personal, and informational dimensions.
  Keywords: digital frontier; digital transformation; digitalisation; catalysts; constraints; dairy cooperatives; Indian dairy cooperatives; India.
  
  
• Regional Digitalisation Level Promoting Rural Tourism Classification Model and Development Trend  
  by Xienjie Zhou, Jia Liu 
  Abstract: This paper explores how regional digitisation can promote the classification patterns and development trends of rural tourism, aiming to demonstrate the feasibility of digitalisation in rural tourism development from the perspectives of economy, ecology, and policy. This paper adopts a combination of quantitative and qualitative analysis methods to establish a hypothetical evaluation model for the sustainable development of rural tourism and the overall economic system, in order to obtain data. The experimental results show that with the improvement of regional digitalisation level, rural tourism has a significant positive impact on various aspects of rural areas. The integration of cultural resources for civilisation construction has increased by 2.3 percentage points, ecological environment protection has reached 8.2 percentage points, and the overall level of new rural construction has improved. The research on digital integration development in this paper provides a new path for the development of intelligent tourism infrastructure.
  Keywords: Regional Digital Level; Rural Tourism; Tourism Classification Model; Digital Tourism.
  DOI: 10.1504/IJTIP.2026.10078320
   
  
• Design of Company Environmental Social and Governance Performance Monitoring System Based on Internet of Things and Cloud Computing  
  by Shuliang Liu, Qianyi Zeng 
  Abstract: The reliability and accuracy of data are crucial for environmental, social, and governance (ESG) performance monitoring systems. Reliable datasets provide the foundation for accurate monitoring and effective decision-making, but these systems often face challenges such as incomplete or inaccurate data and reduced analysis and prediction accuracy. This study applies internet of things (IoT) technology to achieve real-time monitoring and collection of ESG data. This paper first deploys sensors and devices to transmit the collected data to the cloud for processing and analysis, then uses Z-score method and min max normalisation to clean, pre-process, and standardise the collected data, and uses chi square test and principal component analysis (PCA) for feature extraction. The results showed that the average prediction accuracy of the data samples that met the standards was 96.3%, and the average mean square error (MSE) was 0.105.
  Keywords: Internet of Things; Cloud Computing; Multiple Linear Regression Model; ESG Performance Monitoring System; Data Collection.