Understanding behavioural intention to use information technology: Insights from humanitarian practitioners

We investigate the factors that influence IT adoption in humanitarian organizations.A combined model of UTAUT, personal innovation specific to IT domain and trust in IT was employed.The constructs of UTAUT, performance expectancy and effort expectancy have positive and significant effect on behavioural intention to adopt IT. The contemporary research in the area of individual technology adoption mainly focuses on commercial supply chains. However, limited research focuses on the context of humanitarian supply chains. This calls to develop structural models that can scrutinize the technology adoption behaviour of the users in the humanitarian context. Therefore, this study is an attempt to empirically examine the technology adoption behaviour of humanitarian organizations. It extends the unified theory of the acceptance and use of technology (UTAUT) model by integrating personal innovativeness and trust in technology with the behavioural intention to adopt technology in the humanitarian context. Data from 192 humanitarian practitioners, who have experienced a large number of disasters, is utilized to empirically validate the conceptual model. The structural equation modelling results show that - out of four constructs namely performance expectancy, effort expectancy, social influence and facilitating conditions under UTAUT - performance expectancy and effort expectancy significantly affect the IT adoption. Contrary to expectations, trust and personal innovation do not affect the behavioural intention. Also, personal innovation does not moderate the relationship between performance expectancy and effort expectancy. This underlines the need to foster a learning culture within these organizations. The efforts made by involved humanitarian organizations may be directed towards improving the level of education, skills and facilitating them with other resources such as appropriate IT and data mining training, so that the technology adoption becomes an integral part of their daily activities. Finally, detailed implications for humanitarian organizations are discussed.

[1]  David F. Larcker,et al.  Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics: , 1981 .

[2]  Guoxin Li,et al.  The Role of Product Originality, Usefulness and Motivated Consumer Innovativeness in New Product Adoption Intentions , 2015 .

[3]  Dara G. Schniederjans,et al.  Humanitarian supply chain use of cloud computing , 2016 .

[4]  Tao Yao,et al.  Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains , 2011 .

[5]  Caroline C. Krejci,et al.  Coordination in humanitarian relief chains: Practices, challenges and opportunities , 2010 .

[6]  R. Frank Falk,et al.  A Primer for Soft Modeling , 1992 .

[7]  Raj Prasanna,et al.  Information Systems for Supporting Fire Emergency Response , 2010 .

[8]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[9]  S. Chauhan,et al.  Determinants of acceptance of ERP software training in business schools: Empirical investigation using UTAUT model , 2016 .

[10]  Gülay Barbarosoglu,et al.  A two-echelon stochastic facility location model for humanitarian relief logistics , 2012, Optim. Lett..

[11]  Daniel S. J. Costa,et al.  Testing complex models with small sample sizes: A historical overview and empirical demonstration of what Partial Least Squares (PLS) can offer differential psychology , 2015 .

[12]  Elsadig Musa Ahmed,et al.  Exploring consumer doubt towards local new products innovation and purchase intention , 2016 .

[13]  D. DavisFred,et al.  User Acceptance of Computer Technology , 1989 .

[14]  Naim Kapucu,et al.  Information Communication Technology Utilization for Effective Emergency Management Networks , 2016 .

[15]  G. Kovács,et al.  Humanitarian logistics in disaster relief operations , 2007 .

[16]  John Warren,et al.  Healthcare Technology Self-Efficacy (HTSE) and its influence on individual attitude: An empirical study , 2016, Comput. Hum. Behav..

[17]  Keng Siau,et al.  Developing an instrument to measure the adoption of mobile services , 2011 .

[18]  L. Comfort.  Crisis Management in Hindsight: Cognition, Communication, Coordination, and Control , 2007 .

[19]  Lingling Gao,et al.  A unified perspective on the factors influencing consumer acceptance of internet of things technology , 2014 .

[20]  J. Hair.  Multivariate data analysis , 1972 .

[21]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[22]  Fritz Gehbauer,et al.  Optimized resource allocation for emergency response after earthquake disasters , 2000 .

[23]  Carleen F. Maitland,et al.  Exploring Barriers to Coordination between Humanitarian NGOs: A Comparative Case Study of two NGO's Information Technology Coordination Bodies , 2011, Int. J. Inf. Syst. Soc. Chang..

[24]  Gaurav Kabra,et al.  Identification and prioritization of coordination barriers in humanitarian supply chain management , 2015 .

[25]  Gaurav Kabra,et al.  Analyzing ICT Issues in Humanitarian Supply Chain Management: A SAP-LAP Linkages Framework , 2015, Global Journal of Flexible Systems Management.

[26]  Muhammad Z. I. Lallmahomed,et al.  Factors influencing the adoption of e-Government services in Mauritius , 2017, Telematics Informatics.

[27]  Leslie Lenert,et al.  Information technology and emergency medical care during disasters. , 2004, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[28]  Theresa L. Jefferson,et al.  Evaluating the role of information technology in crisis and emergency management , 2006 .

[29]  M. Haselkorn,et al.  Challenges to effective information and communication systems in humanitarian relief organizations , 2005, IPCC 2005. Proceedings. International Professional Communication Conference, 2005..

[30]  Wynne W. Chin,et al.  Structural equation modeling analysis with small samples using partial least squares , 1999 .

[31]  Pervaiz Akhtar,et al.  Essential Micro‐Foundations for Contemporary Business Operations: Top Management Tangible Competencies, Relationship‐Based Business Networks and Environmental Sustainability , 2018 .

[32]  Benjamin T. Hazen,et al.  Inter‐organizational IT use, cooperative attitude, and inter‐organizational collaboration as antecedents to contingency planning effectiveness , 2012 .

[33]  Anugamini Priya Srivastava,et al.  Technology leadership and predicting travel agent performance , 2016 .

[34]  Elisabeth Wilson-Evered,et al.  Predicting uptake of technology innovations in online family dispute resolution services: An application and extension of the UTAUT , 2012, Comput. Hum. Behav..

[35]  Shaligram Pokharel,et al.  Optimization models in emergency logistics: A literature review , 2012 .

[36]  Paul Benjamin Lowry,et al.  Partial Least Squares (PLS) Structural Equation Modeling (SEM) for Building and Testing Behavioral Causal Theory: When to Choose It and How to Use It , 2014, IEEE Transactions on Professional Communication.

[37]  E. Abu-Shanab.  Antecedents of trust in e-government services: an empirical test in Jordan: , 2014 .

[38]  B. Joseph,et al.  Concurrent validity of a measure of innovative cognitive style , 1984 .

[39]  Mark A. Fuller,et al.  Clarifying the Integration of Trust and TAM in E-Commerce Environments: Implications for Systems Design and Management , 2010, IEEE Transactions on Engineering Management.

[40]  Pervaiz Akhtar,et al.  The Internet of Things, dynamic data and information processing capabilities, and operational agility , 2017, Technological Forecasting and Social Change.

[41]  Atreyi Kankanhalli,et al.  Investigating user resistance to information systems implementation: a status quo bias perspective , 2009 .

[42]  Nicolae Nistor,et al.  Participation in virtual academic communities of practice under the influence of technology acceptance and community factors. A learning analytics application , 2014, Comput. Hum. Behav..

[43]  Andrew May,et al.  A user centred design evaluation of the potential benefits of advanced wireless sensor networks for fire-in-tunnel emergency response , 2014 .

[44]  Gilbert A. Churchill.  A Paradigm for Developing Better Measures of Marketing Constructs , 1979 .

[45]  Christopher W. Zobel,et al.  An optimization model for volunteer assignments in humanitarian organizations , 2012 .

[46]  Earl L. Taylor,et al.  The Diffusion of Innovation , 2013 .

[47]  Theresa L. Jefferson,et al.  Using the internet to communicate during a crisis , 2006 .

[48]  Pervaiz Akhtar,et al.  Coordination in humanitarian relief chains: chain coordinators , 2012 .

[49]  Simon A. Andrew,et al.  Determinants of Emergency Management Decision Support Software Technology: An Empirical Analysis of Social Influence in Technology Adoption , 2015 .

[50]  Tina Wakolbinger,et al.  The funding—Humanitarian supply chain interface , 2016 .

[51]  L. Comfort.  RISK, SECURITY, AND DISASTER MANAGEMENT , 2005 .

[52]  T. S. Robertson,et al.  A Propositional Inventory for New Diffusion Research , 1985 .

[53]  Luk N. Van Wassenhove,et al.  Humanitarian aid logistics: supply chain management in high gear , 2006, J. Oper. Res. Soc..

[54]  Mun Y. Yi,et al.  Understanding information technology acceptance by individual professionals: Toward an integrative view , 2006, Inf. Manag..

[55]  Guoxin Li,et al.  The Role of Product Originality and Usefulness on New Product Adoption Intention: Does Motivated Consumer Innovativeness Influence It? , 2015 .

[56]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

[57]  Hsiu-Fen Lin,et al.  An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust , 2011, Int. J. Inf. Manag..

[58]  Anatoliy A. Gruzd,et al.  Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model , 2012, Comput. Hum. Behav..

[59]  Murray E. Jennex,et al.  Knowledge Management and Hurricane Katrina Response , 2006, Int. J. Knowl. Manag..

[60]  Eduardo Esteva Armida.  Adoption process for VOIP: The influence of trust in the UTAUT model , 2008 .

[61]  Walid Chaouali,et al.  Once a user, always a user: Enablers and inhibitors of continuance intention of mobile social networking sites , 2016, Telematics Informatics.

[62]  Raj Prasanna,et al.  Factors affecting the acceptance of information systems supporting emergency operations centres , 2016, Comput. Hum. Behav..

[63]  Marios Koufaris,et al.  The development of initial trust in an online company by new customers , 2004, Inf. Manag..

[64]  R. Bagozzi,et al.  An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors , 2002 .

[65]  Christos Douligeris,et al.  An information management system for the emergency management of hurricane disasters , 2001 .

[66]  Rafael Wittek,et al.  The impact of humanitarian context conditions and individual characteristics on aid worker retention. , 2015, Disasters.

[67]  W. Dullaert,et al.  Humanitarian–business partnerships in managing humanitarian logistics , 2017 .

[68]  Golam Sorwar,et al.  Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model , 2017, Int. J. Medical Informatics.

[69]  Paul A. Pavlou,et al.  Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model , 2003, Int. J. Electron. Commer..

[70]  François-Xavier Delmonteil,et al.  The role of satellite technologies in relief logistics , 2017 .

[71]  Tiago Oliveira,et al.  Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators , 2015, Comput. Hum. Behav..

[72]  Christopher Sandwell,et al.  A qualitative study exploring the challenges of humanitarian organisations , 2011 .

[73]  Irina S. Dolinskaya,et al.  Decentralized Approaches to Logistics Coordi- nation in Humanitarian Relief , 2011 .

[74]  J Brian Houston,et al.  Social media and disasters: a functional framework for social media use in disaster planning, response, and research. , 2015, Disasters.