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

Regular Issues

• Identifying Sustainable Drivers of the Leadership Profit Chain through ISMMICMAC Analysis: Transforming the Service Profit Chain Model  
  by Amit Kumar, Shuchita Singh 
  Abstract: The study extends Heskett et al.s (1994) service profit chain (SPC) framework by incorporating transformational leadership and additional employee and customer-related variables. While the original SPC linked internal service quality, employee satisfaction, customer loyalty, and profitability, it ignored the role of leadership and other employee and customer drivers. To Address this gap, this study proposes a 14-factor leadership profit chain model incorporating transformational leadership, employee well-being, employee engagement, employee work passion, customer perceived quality, customer devotion and organisational viability. Interpretive structural modelling (ISM) and Matrice dImpacts Croises-Multiplication Appliquee a un Classement (MICMAC) analysis were used to analyse factors using expert inputs. MICMAC determined the driving and dependence power of factors. Findings suggest an interconnected mechanism having transformational leadership and internal service quality as dominant drivers influencing employee variables, customer outcomes, profitability, revenue growth, and organisational viability. This study provides a systematic framework to extend SPC model, contributing to the literature.
  Keywords: Service Profit Chain; Transformational Leadership; ISM–MICMAC Analysis; Employee Engagement; Employee Work Passion; Customer Perceived Quality; Customer Devotion; Organizational Viability.
  DOI: 10.1504/IJTIP.2026.10080754
   
  
• Adoption of Private Blockchains and its Challenges: A Sentiment Analysis Using Twitter and Expert Opinions  
  by Navneet Gera, Amit Kumar, Sardar M. N. Islam 
  Abstract: This study aims to deliver a comprehensive review, concentrating on the significance and potential applications of private blockchain technology in the banking and finance industries. This study examines the challenges and adoption of private blockchain technology in financial institutions through interviews with key stakeholders, including industry officials, blockchain professionals, and experts. Additionally, the authors investigated the sentiments of various stakeholders, such as financial institutions and investors, using NVIVO 12 for sentiment analysis. Consequently, this study aims to present a detailed sentiment analysis of individuals engaged in blockchain technology. The findings of this study indicate that the government and regulatory bodies have initiated measures, and financial institutions are optimistic about secure transactions through private blockchains; however, these efforts are insufficient. This study offers valuable insights into the role of government and regulatory initiatives in promoting private blockchain technology.
  Keywords: Private Blockchain; Decentralization; Distributed ledger; Immutable; Government Initiative; adoption of blockchain; Challenges of Blockchain.
  DOI: 10.1504/IJTIP.2026.10081722
   
  
• Human Capital Redundancy and Opportunity Recognition in AI Agent Enabled Entrepreneurial Teams  
  by Yanan Zou, Yuli Guo, Xiaojuan Wang, Jingping Li 
  Abstract: The rapid advancement of artificial intelligence is reshaping entrepreneurial activities and human capital management within entrepreneurial teams. Drawing on resource orchestration theory and entrepreneurial cognition theory, this study examines how human capital redundancy influences entrepreneurial opportunity recognition, incorporating team learning as a mediating mechanism and AI Agent embedding level as a moderating condition. Survey data were collected from 387 technology-oriented entrepreneurial teams in China and analyzed using hierarchical regression, Bootstrap mediation tests, and moderation analysis. The results indicate that both skill redundancy and cognitive redundancy positively affect entrepreneurial opportunity recognition, with cognitive redundancy exerting a stronger influence. Team learning partially mediates the relationship between human capital redundancy and opportunity recognition, while AI Agent embedding amplifies the value realization of redundant human capital. This study extends research on human capital redundancy by revealing its entrepreneurial value in AI-enabled contexts and offers insights into team design and digital transformation under humanmachine collaboration.
  Keywords: AI Agent Digital Employees; Human Capital Redundancy; Entrepreneurial Opportunity Recognition; Team Learning; Human–Machine Collaboration.
  DOI: 10.1504/IJTIP.2026.10081818
   
  
• Attribution Analysis and Counterfactual Evaluation of Corporate Financial Performance Based on Quantitative Causal Inference Strategies  
  by Yufen He 
  Abstract: This study integrates Staggered DID with Double Machine Learning (DML) to address estimation biases from staggered policy adoption and high-dimensional confounders. Using an event-study design for dynamic treatment effects and DML with Lasso, RF, and XGBoost learners, we orthogonalize confounders via cross-fitting. The framework estimates counterfactual trajectories, computes causal effects, and performs Shapley decomposition. Based on 1,238 non-financial listed firms in China (20122023), the cumulative net effect during e0e3 is 1.95 percentage points (p=0.001). Robustness checks sustain effects between 1.851.98 pp. Shapley attribution shows quantitative strategies contribute 1.35 pp (69.3%). Prediction performance: m-RMSE = 1.18%, p-RMSE = 0.062. The approach improves causal robustness and offers a replicable path for evaluating corporate decision effects.
  Keywords: Staggered DID; Quantitative Strategy; Corporate Financial Performance; Attribution Analysis; Counterfactual Evaluation.
  DOI: 10.1504/IJTIP.2026.10081825
   
  
• Construction of an IoT-Driven Cross-Border Logistics Information Collaboration Platform  
  by Juan Yang 
  Abstract: The lack of standardised heterogeneous data interaction among multiple stakeholders in cross-border logistics and limited real-time sensing capabilities of the internet of things have led to severe information lag and collaborative blind spots within the cross-border supply chain. This paper proposes a multi-agent deep reinforcement learning collaborative model based on asynchronous advantage actor-critic (A3C). By deploying heterogeneous feature alignment operators at the IoT sensing edge layer, a unified tensor-quantised representation of cross-border multi-source sensing information is achieved. Furthermore, an asynchronous parallel optimisation mechanism is constructed, where a global evaluation network (critic) guides the local execution network (actor). By utilising the advantage function to remove background noise from state values, robust policy gradient updates and high synchronisation of information flow among all participants are ensured under dynamic constraints. Experimental results show that the signal fidelity F remains at 0.994 when the noise variance coefficient is 0.14. As the number of agents n increases from 1 to 32, the system throughput increases linearly from 5,750 to 29,000 transactions per second. After 201300 training cycles, the median overall reward rt reaches 92.5 with a significantly narrowed distribution
  Keywords: Internet of Things; Cross-Border Logistics; Information Synergy Platform; Asynchronous Advantage Actor-Critic; Dynamic Resource Allocation.
  DOI: 10.1504/IJTIP.2027.10081830