Factors influencing user acceptance of public sector big open data

Abstract In recent years Government departments and public/private organisations are becoming increasingly transparent with their data to establish the whole new paradigm of big open data. Increasing research interest arises from the claimed usability of big open data in improving public sector reforms, facilitating innovation, improving supplier and distribution networks and creating resilient supply chains that help improve the efficiency of public services. Despite the advantages of big open data for supply chain and operations management, there is severe shortage of empirical analyses in this field, especially with regard to its acceptance. To address this gap, in this paper we use an extended technology acceptance model to empirically examine the factors affecting users’ behavioural intentions towards public sector big open data. We outline the importance of our model for operations and supply chain managers, the limitations of the study, and future research directions.

[2]  Zahir Irani,et al.  Evaluating the use and impact of Web 2.0 technologies in local government , 2015, Gov. Inf. Q..

[3]  Sivaporn Wangpipatwong,et al.  Understanding Citizen’s Continuance Intention to Use e-Government Website: a Composite View of Technology Acceptance Model and Computer Self-Efficacy , 2008 .

[4]  Shumaila Y. Yousafzai A comparison of three models to explain internet banking behaviour , 2006 .

[5]  C. Fornell,et al.  Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics , 1981 .

[6]  C. Stein,et al.  Structural equation modeling. , 2012, Methods in molecular biology.

[7]  J. Ramalho-Santos,et al.  Cronbach's alpha: a tool for assessing the reliability of scales , 1999 .

[8]  J. Millard,et al.  Mapping Smart Cities in the EU , 2014 .

[9]  Matti Rossi,et al.  The Impact of Use Situation and Mobility on the Acceptance of Mobile Ticketing Services , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[10]  Jouni Markkula,et al.  Open Traffic Data for Future Service Innovation - Addressing the Privacy Challenges of Driving Data , 2014, J. Theor. Appl. Electron. Commer. Res..

[11]  R. Kline Principles and practice of structural equation modeling, 2nd ed. , 2005 .

[12]  R. Gonzalez Applied Multivariate Statistics for the Social Sciences , 2003 .

[13]  Hung-Pin Shih,et al.  Continued use of a Chinese online portal: an empirical study , 2008, Behav. Inf. Technol..

[14]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[15]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[16]  R. P. Sundarraj,et al.  Application of an Extended TAM Model for Online Banking Adoption: A Study at a Gulf-region University , 2011, Inf. Resour. Manag. J..

[17]  M. Walport,et al.  Science as a public enterprise: the case for open data , 2011, The Lancet.

[18]  J. Richardson,et al.  Technology Adoption in Cambodia: Measuring Factors Impacting Adoption Rates , 2011 .

[19]  Florian Daniel,et al.  Business Intelligence and the Web , 2013, Inf. Syst. Frontiers.

[20]  Traci J. Hess,et al.  Reliability Generalization of Perceived Ease of Use, Perceived Usefulness, and Behavioral Intentions , 2014, MIS Q..

[21]  E. Hultink,et al.  “Honey, Have You Seen Our Hamster?” Consumer Evaluations of Autonomous Domestic Products , 2003 .

[22]  Kwoting Fang,et al.  The use of a decomposed theory of planned behavior to study Internet banking in Taiwan , 2004, Internet Res..

[23]  Paul Jen-Hwa Hu,et al.  Information Technology Acceptance by Individual Professionals: A Model Comparison Approach , 2001, Decis. Sci..

[24]  Shahriar Akter,et al.  Guest editorial: information technology-enabled supply chain management , 2015 .

[25]  Edmund A. Mennis The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations , 2006 .

[26]  Sunil Choenni,et al.  On the barriers for local government releasing open data , 2014, Gov. Inf. Q..

[27]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[28]  Marijn Janssen,et al.  Open data policies, their implementation and impact: A framework for comparison , 2014, Gov. Inf. Q..

[29]  Diana Adler,et al.  Using Multivariate Statistics , 2016 .

[30]  Yogesh Kumar Dwivedi,et al.  RFID integrated systems in libraries: extending TAM model for empirically examining the use , 2014, J. Enterp. Inf. Manag..

[31]  Aidan O'Driscoll,et al.  The diffusion of microgeneration technologies – assessing the influence of perceived product characteristics on home owners' willingness to pay , 2011 .

[32]  Maria Teresa Borzacchiello,et al.  The impact on innovation of open access to spatial environmental information: a research strategy , 2012, Int. J. Technol. Manag..

[33]  Jong-Ho Lee,et al.  Understanding the Adoption of Convergent Services: The Case of IPTV , 2011, 2011 44th Hawaii International Conference on System Sciences.

[34]  Sinawong Sang,et al.  E‐government adoption in Cambodia: a partial least squares approach , 2010 .

[35]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[36]  Detmar W. Straub,et al.  Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs , 1999, MIS Q..

[37]  Kieron O'Hara,et al.  Transparent government, not transparent citizens: a report on privacy and transparency for the Cabinet Office , 2011 .

[38]  José F. Molina-Azorín,et al.  The whole relationship between environmental variables and firm performance: competitive advantage and firm resources as mediator variables. , 2009, Journal of environmental management.

[39]  Gillian Oliver,et al.  Value in the MASH: Exploring the Benefits, Barriers and Enablers of Open Data Apps , 2020, ECIS.

[40]  Chang Liu,et al.  Determinants of accepting wireless mobile data services in China , 2008, Inf. Manag..

[41]  Patricia Lucas,et al.  Can The Nintendo Wii(tm) Sports Game System Be Effectively Utilized In The Nursing Home Environment: A Feasibility Study? , 2012, J. Community Informatics.

[42]  Lian Duan,et al.  Big data analytics and business analytics , 2015 .

[43]  Thomas C. Kinnear,et al.  Exploring the Consumer Decision Process in the Adoption of Solar Energy Systems , 1981 .

[44]  Chin-Lung Hsu,et al.  Adoption of the mobile Internet: An empirical study of multimedia message service (MMS) , 2007 .

[45]  Sung Youl Park,et al.  University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model , 2012, Br. J. Educ. Technol..

[46]  JinKyu Lee,et al.  Task complexity and different decision criteria for online service acceptance: A comparison of two e-government compliance service domains , 2009, Decis. Support Syst..

[47]  B. Tabachnick,et al.  Using multivariate statistics, 5th ed. , 2007 .

[48]  Lori Rosenkopf,et al.  Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation , 1997 .

[49]  Francisco J. García-Peñalvo,et al.  ICTs integration in education: mobile learning and the technology acceptance model (TAM) , 2014, TEEM '14.

[50]  E. B. Andersen,et al.  Modern factor analysis , 1961 .

[51]  H. Harman Modern factor analysis , 1961 .

[52]  Bernd W. Wirtz,et al.  Understanding consumer acceptance of mobile payment services: An empirical analysis , 2010, Electron. Commer. Res. Appl..

[53]  E Tasmanian,et al.  Understanding and promoting adoption of conservation practices by rural landholders , 2006 .

[54]  L. G. Tornatzky,et al.  Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings , 1982, IEEE Transactions on Engineering Management.

[55]  Yogesh Kumar Dwivedi,et al.  Role of Innovation Attributes in Explaining the Adoption Intention for the Interbank Mobile Payment Service in an Indian Context , 2013, TDIT.

[56]  R. Little A Test of Missing Completely at Random for Multivariate Data with Missing Values , 1988 .

[57]  B. Price A First Course in Factor Analysis , 1993 .

[58]  E. Rogers Diffusion of Innovations , 1962 .

[59]  Michael R. Mullen,et al.  Structural equation modelling: guidelines for determining model fit , 2008 .

[60]  George H. Polychronopoulos,et al.  An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece , 2012 .

[61]  Outlier detection and missing data filling methods for coastal water temperature data , 2016 .

[62]  G. Graham,et al.  How smart cities will change supply chain management: a technical viewpoint , 2016 .

[63]  Chris J. Martin Barriers to the Open Government Data Agenda: Taking a Multi-Level Perspective , 2014 .

[64]  M. Janssen,et al.  Barriers and Development Directions for the Publication and Usage of Open Data: A Socio-Technical View , 2014 .

[65]  Yogesh Kumar Dwivedi,et al.  Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology , 2015, Gov. Inf. Q..

[66]  Shahriar Akter,et al.  How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study , 2015 .

[67]  Bastiaan van Loenen,et al.  Brave New Open Data World? , 2012, Int. J. Spatial Data Infrastructures Res..

[68]  Changsu Kim,et al.  An empirical investigation of factors affecting ubiquitous computing use and U-business value , 2009, Int. J. Inf. Manag..

[69]  Alejandro Sáez Martín,et al.  Open Government Data: A European Perspective , 2015 .

[70]  Steven C. Padgitt,et al.  Extension's Portfolio for the 21st Century: A Place for One-on-One Consultations , 1999 .

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

[72]  G. Bowker,et al.  An International Framework to Promote Access to Data , 2004, Science.

[73]  Yogesh Kumar Dwivedi,et al.  Understanding the adopters and non-adopters of broadband , 2009, CACM.

[74]  Alexander Hars,et al.  Web Based Knowledge Infrastructures for the Sciences: An Adaptive Document , 2000, Commun. Assoc. Inf. Syst..

[75]  Donghee Don Shin MVNO services: Policy implications for promoting MVNO diffusion , 2010 .

[76]  Sunil Choenni,et al.  Socio-technical Impediments of Open Data , 2012 .

[77]  Vishanth Weerakkody,et al.  E-government adoption: A cultural comparison , 2008, Inf. Syst. Frontiers.

[78]  Maxat Kassen,et al.  A promising phenomenon of open data: A case study of the Chicago open data project , 2013, Gov. Inf. Q..

[79]  Mila Gascó Hernández Open government : opportunities and challenges for public governance , 2014 .

[80]  Hugh Glaser,et al.  Linked Open Government Data: Lessons from Data.gov.uk , 2012, IEEE Intelligent Systems.

[81]  Sylvain Sénécal,et al.  Measuring Perceived Website Usability , 2007 .

[82]  Shu-Sheng Liaw,et al.  Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments , 2013, Comput. Educ..

[83]  J. Hair Multivariate data analysis : a global perspective , 2010 .

[84]  Yogesh Kumar Dwivedi,et al.  State-of-the-art in open data research: Insights from existing literature and a research agenda , 2016, J. Organ. Comput. Electron. Commer..

[85]  N. Huijboom,et al.  Open data: An International comparison of strategies , 2011 .

[86]  Michael Boretsky,et al.  The Role of Innovation , 1980 .

[87]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[88]  Erin V. Lehman,et al.  Development of an Instrument , 2005 .

[89]  Harry Bouwman,et al.  An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models , 2008, Inf. Manag..

[90]  Thompson S. H. Teo,et al.  Adoption of WAP-enabled mobile phones among Internet users , 2003 .

[91]  M. Avital,et al.  The Value of Open Government Data: A Strategic Analysis Framework , 2012 .

[92]  Yannis Charalabidis,et al.  Evaluating Second Generation Open Government Data Infrastructures Using Value Models , 2014, 2014 47th Hawaii International Conference on System Sciences.

[93]  Beat Estermann,et al.  Diffusion of Open Data and Crowdsourcing among Heritage Institutions: Results of a Pilot Survey in Switzerland , 2014, J. Theor. Appl. Electron. Commer. Res..

[94]  Yannis Charalabidis,et al.  Benefits, Adoption Barriers and Myths of Open Data and Open Government , 2012, Inf. Syst. Manag..

[95]  Sai S. Nudurupati,et al.  Contemporary performance measurement and management (PMM) in digital economies , 2016 .

[96]  Sunil Choenni,et al.  Reconciling Contradictions of Open Data Regarding Transparency, Privacy, Security and Trust , 2014, J. Theor. Appl. Electron. Commer. Res..

[97]  Howard B. Lee,et al.  A First Course in Factor Analysis 2nd Ed , 1973 .

[98]  R. Ozaki Adopting sustainable innovation: what makes consumers sign up to green electricity? , 2011 .

[99]  Bhuvaneswari Raman The Rhetoric of Transparency and its Reality: Transparent Territories, Opaque Power and Empowerment , 2012, J. Community Informatics.

[100]  Lluís Esteve Casellas Serra,et al.  The mapping, selecting and opening of data , 2014 .

[101]  Marijn Janssen,et al.  A Coordination Theory Perspective to Improve the Use of Open Data in Policy-Making , 2013, EGOV.

[102]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[103]  Shahriar Akter,et al.  Big data analytics and firm performance: Effects of dynamic capabilities , 2017 .

[104]  Michael G. Morris,et al.  User Acceptance of Information Technology: Theories and Models , 1996 .

[105]  Paul T. Jaeger,et al.  Big data, open government and e-government: Issues, policies and recommendations , 2014, Inf. Polity.

[106]  Nathalia Purnawirawan,et al.  Balance and Sequence in Online Reviews: How Perceived Usefulness Affects Attitudes and Intentions , 2012 .

[107]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[108]  Parthasarati Dileepan,et al.  A SWOT analysis of big data , 2016 .

[109]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[110]  James Surowiecki The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations Doubleday Books. , 2004 .

[111]  Xiang Fang,et al.  An empirical study of web site navigation structures' impacts on web site usability , 2007, Decis. Support Syst..

[112]  Randy K. Chiu,et al.  Ethical Judgment and Whistleblowing Intention: Examining the Moderating Role of Locus of Control , 2003 .

[113]  Katrin Braunschweig,et al.  The State of Open Data Limits of Current Open Data Platforms , 2012 .

[114]  Barbara Ubaldi,et al.  Open Government Data , 2019, Government at a Glance: Latin America and the Caribbean 2020.

[115]  Gregory A. Porumbescu,et al.  Does Transparency Improve Citizens’ Perceptions of Government Performance? Evidence From Seoul, South Korea , 2017 .

[116]  Anne Fleur van Veenstra,et al.  Opening Moves - Drivers, Enablers and Barriers of Open Data in a Semi-public Organization , 2013, EGOV.

[117]  MinQingfei,et al.  A meta-analysis of Mobile Commerce Research in China (2002 2006) , 2008 .

[118]  Lei da Chen,et al.  A model of consumer acceptance of mobile payment , 2008, Int. J. Mob. Commun..

[119]  James H. Steiger,et al.  Understanding the limitations of global fit assessment in structural equation modeling , 2007 .

[120]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[121]  Sung Youl Park,et al.  An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning , 2009, J. Educ. Technol. Soc..

[122]  G. Marshall,et al.  Understanding and promoting adoption of conservation practices by rural landholders , 2006 .