Predicting adoption of biological control among Iranian rice farmers: An application of the extended technology acceptance model (TAM2)

Abstract Biological control (BC) strategies have bolstered the ability and motivation of farmers to control various types of pests, but better understanding of farmers' behaviour for BC acceptance is still needed. This study attempted for the first time to implement an extended version of the technology acceptance model (TAM) that combined innovation diffusion theory (IDT), perceived self-efficacy, and facilitating conditions to investigate factors affecting farmers' acceptance and use of BC for the control of rice stem borer [ Chilo suppressalis (Walker)] in rice fields of Mazandaran province in northern Iran. The proposed model (widely known as TAM2) was empirically tested using data collected from a survey of 179 rice farmers. The TAM2 explained 78% of the variance in behavioural intention of farmers to use BC and 82% of the variance in actual use. With the TAM2, adoption and use of BC could be adequately predicted from farmers' intentions, which were affected by perceived self-efficacy, facilitating conditions, compatibility, and perceived usefulness as the original TAM suggests. Therefore, with the TAM2 we successfully extended the TAM (in terms of model fit) by including three external factors relevant to BC, namely, perceived self-efficacy, facilitating conditions, and compatibility as antecedents of behavioural intention to use. However, the predicted direct and indirect effects of perceived ease of use on farmers' behavioural intention were not supported. The TAM2 outperformed the original TAM in predicting farmer's intention to use BC in farm practices and consequently actual use. To increase acceptance of BC, agricultural authorities should take care of creating a favorable environment which will support and encourage the use of BC at the farm level. Providing facilitating conditions, such as extension services and training, can help farmers to increase their knowledge about BC. However, more emphasis should be given on compatibility and usefulness of this technology and less on the ease of use.

[1]  J. Bruwer,et al.  Understanding farmers’ adaptation intention to climate change: A structural equation modelling study in the Mekong Delta, Vietnam , 2014 .

[2]  Susan Wiedenbeck,et al.  Self-efficacy and mental models in learning to program , 2004, ITiCSE '04.

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

[4]  M. Sharifzadeh,et al.  Motivations for adopting biological control among Iranian rice farmers , 2016 .

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

[6]  Bernadette Szajna,et al.  Empirical evaluation of the revised technology acceptance model , 1996 .

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

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

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

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

[11]  C. A. Damalas,et al.  Perceptions of the beneficial and harmful effects of pesticides among Iranian rice farmers influence the adoption of biological control , 2015 .

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

[13]  Richard D. Johnson,et al.  The Evolving Nature of the Computer Self-Efficacy Construct: An Empirical Investigation of Measurement Construction, Validity, Reliability and Stability Over Time , 2007, J. Assoc. Inf. Syst..

[14]  P. Nuthall,et al.  A Behavioural Approach to Understanding Semi-subsistence Farmers' Technology Adoption Decisions: The Case of Improved Paddy-Prawn System in Indonesia , 2010 .

[15]  Tom Cox,et al.  Exploratory Factor Analysis: A Users’Guide , 1993 .

[16]  M. Allahyari,et al.  Factors Influencing the Adoption of Biological Control of Rice stem borer (Chilo Suppressalis) in Talesh Region, Iran , 2010 .

[17]  A. Bandura.  Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychological review.

[18]  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..

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

[20]  Jian Zhang,et al.  Farmers' Information Usage Intention in China Based on the Technology Acceptance Model , 2008, CCTA.

[21]  G H Abdollahzadeh,et al.  BARRIERS TO USE OF BIOLOGICAL CONTROL IN RICE FARMS OF SARI COUNTY (CASE OF TRICHOGRAMMA SPP TO CONTROL STEM BORER) , 2015 .

[22]  Jane M. Howell,et al.  Personal Computing: Toward a Conceptual Model of Utilization , 1991, MIS Q..

[23]  Christos A. Damalas,et al.  Understanding benefits and risks of pesticide use , 2009 .

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

[25]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[26]  Jen-Her Wu,et al.  What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model , 2005, Inf. Manag..

[27]  H. S. Fami,et al.  SOCIAL FACTORS INFLUENCING ADOPTION OF INTEGRATED PEST MANAGEMENT (IPM) TECHNOLOGIES BY PADDY FARMERS , 2013 .

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

[29]  Benoit Aubert,et al.  IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology , 2012, Decis. Support Syst..

[30]  Haleh Shafiee Sabet,et al.  Acceptances of Biological control of rice stem borer and its effect in reducing pesticide , 2012 .

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

[32]  Naihua Duan,et al.  Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research , 2015, Administration and Policy in Mental Health and Mental Health Services Research.

[33]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..

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

[35]  J. Primo,et al.  Mating disruption to control the striped rice stem borer: Pheromone blend, dispensing technology and number of releasing points , 2016 .

[36]  Christos A. Damalas,et al.  Selecting strategies for rice stem borer management using the Analytic Hierarchy Process (AHP) , 2016 .

[37]  I. Eleftherohorinos,et al.  Pesticide Exposure, Safety Issues, and Risk Assessment Indicators , 2011, International journal of environmental research and public health.

[38]  Fred D. Davis,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1 , 1992 .