International Journal of Technology Intelligence and Planning (4 papers in press)

Regular Issues

• Proposal of a Framework for Assessing Data Quality for Technology Intelligence. Evidence from the Building Industry  
  by Marina Flamand, Patience Le Coustumer 
  Abstract: Assessing data quality is a prerequisite for using it effectively to inform decision-making. This general consensus regarding data quality has not been tested from the perspective of technology intelligence, which is an information-based activity to support strategic decision-making in innovation area. This article seeks to fill this gap by proposing an operational data quality assessment framework specifically tailored to the needs of technology intelligence experts. In addition to two traditional dimensions of data assessment (availability and reliability), we have integrated a third dimension: the utility for a technology intelligence approach. It is based on three more specific criteria: relevance, richness of information and the added value for analysing innovation. We demonstrate how to use this framework and illustrate its utility by assessing the quality of technical appraisals (ATec in French and below) to support technology intelligence in the building industry.
  Keywords: technology intelligence; decision-making; innovation; innovation indicator; criteria for data quality; assessment framework; operational framework; regulatory data; building industry.
  DOI: 10.1504/IJTIP.2023.10060651
   
  
• Mapping Intelligence Concepts & Positioning Technology Intelligence: A Structural Topic Modelling Approach  
  by Aynur Kirbac Yarga, Dilek Ozdemir Gungor, Serhat Burmaoglu, Ozcan Saritas 
  Abstract: Accelerating societal, technological, and economic changes with emerging challenges compel strategic managers to rely on scanning and intelligence tools to stay competitive. Numerous intelligence phrases have gained popularity in the last decade. This study aims to systematize and distinguish these intelligence phrases in scholarly publications, focusing on technology intelligence (TI). TI has garnered attention due to global technological change, increased R&D activities, external technology resource usage, and the growing complexity of technology development. We applied a qualitative approach for conceptual clarification, followed by a quantitative method, structural topic modelling, to position these terms in the literature. Our study identified two perspectives on intelligence: endogenous and exogenous. Clustering intelligence phrases based on these perspectives revealed that TI is part of the exogenous intelligence cluster. The quantitative analysis demonstrated that, unlike other intelligence approaches, TI is closely associated with emergence terms and technological change.
  Keywords: conceptual analysis; technological change; technology intelligence; technology emergence; topic modelling; structural topic modelling.
  DOI: 10.1504/IJTIP.2024.10065359
   
  
• Eliciting Food Waste Perceptions using an AI-driven Approach  
  by Kanwal Gul, Swapnil Morande 
  Abstract: Food waste is a pressing global issue threatening sustainability. This research uses a participatory photovoice methodology to explore students' perceptions of food waste in a developing nation. Students capture images of food waste in their surroundings, and an artificial intelligence engine conducts unbiased analysis of the visual and textual data. The study reveals insights into participants' awareness, attitudes, and emotions surrounding waste, with key themes emerging around waste consciousness, guilt, helplessness, and the influence of affluence and social norms. The research makes two significant contributions. First, the photovoice technique effectively elicits youth perspectives on socio-economic issues in an inclusive, bottom-up manner. Second, AI-powered analytics enables rigorous, objective interpretation of complex subjective data. The study offers a novel approach to understand multi-faceted food waste perceptions, facilitating the design of context-specific interventions. By mobilising youth and leveraging AI, this research aims to spark innovative solutions for reducing waste and building sustainable food systems.
  Keywords: Food Waste; Photovoice; Artificial Intelligence; Sustainability; Developing Countries.
  DOI: 10.1504/IJTIP.2024.10065415
   
  
• A Study on the Impact of Digital Transformation of Business Clusters on the Economic Performance of Innovation in the Context of the Digital Economy  
  by HaiKun Zhang  
  Abstract: This study focuses on evaluating the growth prospects of enterprises by analysing panel data of digitally listed companies. Principal component extraction is used to construct growth evaluation indicators, and the extreme gradient boosting (XGBoost) model is employed to predict enterprise growth. The study confirms the indicator system’s relevance through the Kaiser-Meyer-Olkin (KMO) test. In simulation experiments, the prediction performance of different classification algorithms was compared, and the prediction accuracy of XGBoost in the training dataset was 0.8366, which is higher than other algorithms under the same conditions. The proposed XGBoost model provides a more reliable and accurate method for financial status classification and growth prediction compared to traditional methods. This research aims to guide the ongoing development of enterprise innovation economy by offering an effective growth prediction method for digital cluster enterprises.
  Keywords: business clusters; digital economy; financial performance; XGBoost; growth.
  DOI: 10.1504/IJTIP.2024.10065489