Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service

Adoption of mobile based agricultural extension services (AES) can serve as a tool for inclusive development in the rural farm land. This paper investigates the antecedents of behavioral intention in the context of mobile based AES. The aim of this study is to identify the important factors influencing the adoption of a mobile based AES in a rural context based on technology acceptance model (TAM). A survey of 327 respondents in rural areas was conducted. Structural equation modeling was employed to empirically test the complex causal relationship of perceived usefulness (PU), perceived ease of use (PEOU), social influence (SI), attitude (At), perceived economic wellbeing (PEWB) and behavioral intention (BI). All the six constructs are reliable and valid. The results show that social influence affects attitude, PEOU, PEWB and PU but not BI. Further, PEOU influences PU and attitude, while attitude and PU predicts BI. It also reveals PEOU and PEWB are antecedents to PU. The findings indicate that neither attitude nor BI is impacted by PEWB. A contribution of this research to the existing TAM literature is that perceived economic wellbeing is an antecedent to perceived usefulness. Finally we discuss the implication of these findings for agricultural extension services.

[1]  Matthew J. Kotchen,et al.  Random effects analysis , 2003 .

[2]  Kinshuk,et al.  Comparing the role of ICT literacy and anxiety in the adoption of mobile learning , 2014, Comput. Hum. Behav..

[3]  A. Adrian,et al.  Producers' perceptions and attitudes toward precision agriculture technologies , 2005 .

[4]  Shin-Yuan Hung,et al.  Critical factors of WAP services adoption: an empirical study , 2003, Electron. Commer. Res. Appl..

[5]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[6]  T. Ramayah,et al.  Green product purchase intention: Some insights from a developing country , 2010 .

[7]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

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

[9]  L. Festinger A Theory of Social Comparison Processes , 1954 .

[10]  I. Addai,et al.  Ethnicity and Economic Well-Being: The Case of Ghana , 2010 .

[11]  Keng-Boon Ooi,et al.  What catalyses mobile apps usage intention: an empirical analysis , 2015, Ind. Manag. Data Syst..

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

[13]  Douglas R. Vogel,et al.  Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning , 2013, Comput. Educ..

[14]  M. Browning,et al.  The distribution of financial well-being and income within the household , 2009 .

[15]  S. Mulaik,et al.  EVALUATION OF GOODNESS-OF-FIT INDICES FOR STRUCTURAL EQUATION MODELS , 1989 .

[16]  Sumeet Gupta,et al.  Value-based Adoption of Mobile Internet: An empirical investigation , 2007, Decis. Support Syst..

[17]  M. Beckett,et al.  Ethnicity, Language, and Economic Well-Being in Rural Guatemala , 2009 .

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

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

[20]  Sang-Chul Lee,et al.  Determinants of behavioral intention to mobile banking , 2009, Expert Syst. Appl..

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

[22]  M. Leonard,et al.  The human factor: the critical importance of effective teamwork and communication in providing safe care , 2004, Quality and Safety in Health Care.

[23]  Fatemeh Saghafi,et al.  Examining effective factors in initial acceptance of high-tech localized technologies: Xamin, Iranian localized operating system , 2017 .

[24]  Zixue Tai,et al.  Perceived ease of use in prior e‐commerce experiences: A hierarchical model for its motivational antecedents , 2010 .

[25]  I. Mansour,et al.  Consumers’ Attitude towards E-Banking Services in Islamic Banks: The Case of Sudan , 2016 .

[26]  C. K. Prahalad,et al.  Fortune at the bottom of the pyramid, the: eradicating poverty through profits , 2006 .

[27]  A. Lansink,et al.  Identifying psychological factors that determine cattle farmers' intention to use improved natural grassland , 2016 .

[28]  Kurosh Rezaei-Moghaddam,et al.  Agricultural specialists' intention toward precision agriculture technologies: Integrating innovation characteristics to technology acceptance model , 2010 .

[29]  Agata Gąsiorowska The relationship between objective and subjective wealth is moderated by financial control and mediated by money anxiety , 2014 .

[30]  Sung-Hee Jang,et al.  Effect of u-healthcare service quality on usage intention in a healthcare service , 2016 .

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

[32]  Abhay Jain,et al.  Factors Influencing Mobile Services Adoption in Rural India , 2007 .

[33]  Muhammad Bakhsh,et al.  Examination of factors influencing students and faculty behavior towards m-learning acceptance , 2017 .

[34]  Åke Grönlund,et al.  Bangladesh calling: farmers' technology use practices as a driver for development , 2011, Inf. Technol. Dev..

[35]  Fred D. Davis,et al.  The Accuracy of Behavioral Intention Versus Behavioral Expectation for Predicting Behavioral Goals , 1985 .

[36]  Chulmo Koo,et al.  Examining the eco-technological knowledge of Smart Green IT adoption behavior: A self-determination perspective , 2014 .

[37]  Pin Luarn,et al.  AIS Electronic Library (AISeL) , 2017 .

[38]  Rajesh Veeraraghavan,et al.  Digital Green: Participatory Video and Mediated Instruction for Agricultural Extension , 2009 .

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

[40]  Patricia Bamwine Factors that influence the farmers' perceived usefulness of information for conservation decisions in the Darby Creek watershed / , 1997 .

[41]  Jian Deng,et al.  Analysis of the ecological conservation behavior of farmers in payment for ecosystem service programs in eco-environmentally fragile areas using social psychology models. , 2016, The Science of the total environment.

[42]  A. Karraker “Feeling Poor” , 2014, Journal of aging and health.

[43]  Qian Guo,et al.  Utility of the theory of reasoned action and theory of planned behavior for predicting Chinese adolescent smoking. , 2007, Addictive behaviors.

[44]  Xiaolan Fu,et al.  The Impact of Mobile Phone Technology on Agricultural Extension Services Delivery: Evidence from India , 2016 .

[45]  John Ingham,et al.  Why do people use information technology? A critical review of the technology acceptance model , 2003, Inf. Manag..

[46]  Jane S. Webster,et al.  Rational and social theories as complementary explanations of communication media choices: two polic , 1995 .

[47]  M. Wilhelm,et al.  Modeling Perceived Economic Well-Being In A Family Setting: A Gender Perspective , 1998 .

[48]  D. Wu,et al.  The effect of reducing risk and improving personal motivation on the adoption of knowledge repository system , 2010 .

[49]  Julian C. Stanley,et al.  Differential Weighting: A Review of Methods and Empirical Studies1 , 1970 .

[50]  Fred D. Davis User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts , 1993, Int. J. Man Mach. Stud..

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

[52]  Hsi-Peng Lu,et al.  Predicting mobile social network acceptance based on mobile value and social influence , 2015, Internet Res..

[53]  Jan Noyes,et al.  Exploring attitudes towards computer use among pre‐service teachers from Singapore and the UK , 2010 .

[54]  Viswanath Venkatesh,et al.  Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model , 2000, Inf. Syst. Res..

[55]  Stephen Lwasa,et al.  Awareness of ICT-Based Projects and the Intensity of Use of Mobile Phones Among Smallholder Farmers in Uganda: The Case of Mayuge and Apac Districts , 2011, Int. J. ICT Res. Dev. Afr..

[56]  Alain Yee-Loong Chong,et al.  Predicting m-commerce adoption determinants: A neural network approach , 2013, Expert Syst. Appl..

[57]  H. Kelman Compliance, identification, and internalization three processes of attitude change , 1958 .

[58]  Multigenerational organisations: A challenge for technology and social change , 2014 .

[59]  W. M. Rivera,et al.  Agricultural and rural extension worldwide: options for institutional reform in the developing countries. , 2002 .

[60]  Sebastian Gurtner,et al.  Designing mobile business applications for different age groups , 2014 .

[61]  Garry Wei-Han Tan,et al.  Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach , 2013, Expert Syst. Appl..

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

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

[64]  Fan-Yun Pai,et al.  Applying the Technology Acceptance Model to the introduction of healthcare information systems , 2011 .

[65]  E. Yang,et al.  Rural America and the extension service : a history and critique of the cooperative agricultural and home economics extension service , 1949 .

[66]  Olusegun Folorunso,et al.  Applying an Enhanced Technology Acceptance Model to Knowledge Management in Agricultural Extension Services , 2008, Data Sci. J..

[67]  P. D. D. Dominic,et al.  User acceptance of online system: a study of banking and airline sector , 2014, Int. J. Bus. Inf. Syst..

[68]  C. Morosan,et al.  When tradition meets the new technology: an examination of the antecedents of attitudes and intentions to use mobile devices in private clubs. , 2014 .

[69]  Jengchung V. Chen,et al.  Acceptance and adoption of the innovative use of smartphone , 2007, Ind. Manag. Data Syst..

[70]  Elena Karahanna,et al.  Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage , 2000, MIS Q..

[71]  Muris Cicic,et al.  Changes in beliefs, satisfaction and information system continuance intention of experienced users , 2015, Int. J. Bus. Inf. Syst..

[72]  Jia-Lun Tsai,et al.  Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM , 2017, Future Gener. Comput. Syst..

[73]  Mei‐Fang Chen,et al.  Developing an extended Theory of Planned Behavior model to predict consumers’ intention to visit green hotels , 2014 .

[74]  A. Bhavnani,et al.  The role of mobile phones in sustainable rural poverty reduction , 2008 .

[75]  Christopher B. Barrett,et al.  Measuring Social Networks' Effects on Agricultural Technology Adoption , 2013 .

[76]  D. Posel Self-assessed well-being and economic rank in South Africa , 2014 .

[77]  Glenda A. Gunter,et al.  Students' Perceived Ease of Use of an Elearning Management System: An Exogenous or Endogenous Variable? , 2005 .

[78]  H. Kehr,et al.  Prediction of attitude and behavioural intentions in retail banking , 2007 .

[79]  Blair Kidwell,et al.  An examination of college student money management tendencies , 2004 .

[80]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[81]  Lei-da Chen,et al.  Enticing online consumers: an extended technology acceptance perspective , 2002, Inf. Manag..

[82]  Francisco Muñoz-Leiva,et al.  Determinants of Intention to Use the Mobile Banking Apps: An Extension of the Classic TAM Model , 2017 .

[83]  Shiu-Wan Hung,et al.  Exploring the intention to continue using social networking sites: : The case of Facebook , 2015 .

[84]  Nikolaos Stylos,et al.  The effects of online social networking on retail consumer dynamics in the attractions industry: The case of ‘E-da’ theme park, Taiwan , 2017 .

[85]  Kenneth A. Bollen,et al.  Structural Equations with Latent Variables , 1989 .

[86]  S. Ozkan,et al.  The Role of Gender in Pharmacists Attitudes Towards E-pharmacy Application , 2013 .

[87]  Pamela R. Turner,et al.  Discriminating the number of credit cards held by college students using credit and money attitudes , 1999 .

[88]  Hong Joo Lee,et al.  The influence of national culture on the attitude towards mobile recommender systems , 2014 .

[89]  Harry Bouwman,et al.  Ubiquitous use of mobile social network services , 2014, Telematics Informatics.

[90]  Debasish Roy A framework and propositions for adoption of mobile applications in rural India , 2013 .

[91]  Jyh-Shen Chiou,et al.  The impact of perceived ease of use on Internet service adoption: The moderating effects of temporal distance and perceived risk , 2010, Comput. Hum. Behav..

[92]  Kim K. P. Johnson,et al.  Consumer adoption of smart in-store technology: assessing the predictive value of attitude versus beliefs in the technology acceptance model , 2017 .

[93]  R. Smyth,et al.  Perceptions of Subjective Economic Well-Being and Support for Market Reform among China's Urban Population , 2005 .

[94]  Fred D. Davis,et al.  A Model of the Antecedents of Perceived Ease of Use: Development and Test† , 1996 .

[95]  Ken R. McNaught,et al.  Comparing a simulation model with various analytic models of the international diffusion of consumer technology , 2015 .

[96]  Sajad Rezaei,et al.  User satisfaction with mobile websites: the impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust , 2014 .

[97]  A. Birch,et al.  Preservice teachers’ acceptance of ICT integration in the classroom: applying the UTAUT model , 2009 .

[98]  Geoffrey S. Hubona,et al.  Evaluating system design features , 1996, Int. J. Hum. Comput. Stud..

[99]  Kenneth C. C. Yang,et al.  Factors affecting consumers' responses to mobile advertising from a social norm theoretical perspective , 2010, Telematics Informatics.