Farmers under the Agriculture Information and Communication Centers (AICCs) in Rangpur region containing 20 upazilas of Panchagarh, Dinajpur, Nilphamari, Rangpur, Lalmonirhat and Kurigram districts constituted the population of this study. There are about 2000 farmers are members of the above mentions AICCs. Through multistage random sampling procedure, 20 AICCs were selected and from each center, ten AICC farmers were selected randomly resulting a total of 200 AICC farmers as the sample of the study. An interview schedule containing both open and closed form questions was prepared for collection of data and was pre-tested among 12 AICC farmers of Chirirbandar Upazila of the Dinajpur district. In addition, two focus group discussions (FGDs) (with AICC farmers) were performed for getting collective view on constraints and expectations of the respondents regarding ICT based agricultural extension services. Data were collected from September 2017 to April 2018. Eight characteristics of the respondents namely age, level of education, family size, annual income, farming experience, ICT training received, ICT utilization in agriculture, attitude towards ICTs constituted the selected characteristics and the effectiveness of ICT enablers as perceived by the farmers was the focus issue of the study. The focus issue was measured by computing a composite effectiveness score based on each of the four dimensions, are: (i) frequency of use, (ii) importance of using in agriculture, (iii) ease to use and (iv) maintenance and operational cost. Each of the dimensions was measured against ten ICT enablers (such as mobile phone, computer, internet, multimedia projector, digital camera, CD/DVD player, TV, radio, printer and public address system) and was put against a 4-point rating scale. For frequency dimension the scores against the enablers were ‘not at all’ is 0 ‘seldom’ is 2, ‘weekly’ is 3, and ‘monthly is 4. For importance dimension the scores against the enablers were ‘not important’ is 0 ‘moderately important’ is 2, ‘important’ is 3, and ‘highly important’ is 4. For ease to use dimension the scores against the enablers were ‘difficult’ is 0 ‘moderately easy’ is 2, ‘easy’ is 3, and ‘very easy’ is 4. For maintenance and operational cost dimension the scores against the enablers was ‘very expensive’ is 0 ‘average’ is 2, ‘cheap’ is 3, and ‘very cheap’ is 4. Thus, the score of each dimension could range from 0 to 30. This possible range was divided into three categories for classification of each dimension. Finally, the composite effectiveness score was calculated by addition of the scores for all of the four dimensions of a respondent. The composite effectiveness score could range from 0 to 120. This possible range was divided into three equal categories for categorization of effectiveness. The constraints faced by the farmers in using ICTs in agricultural activates was measured by using four-point rating scale such as high, medium, low and not at all with a score of 3, 2, 1, and 0, respectively. The scale contained 12 constraint items related to use of ICTs in agricultural activates by the farmers which were identified during pre-testing of the interview schedule. Comparative severity of the constraints was determined by item-wise ranking of constraints through computation of the Constraint Facing Index using the following formula: CFI = (Ch×3)+(Cm×2)+(Cl×1)+(Cn×0)
Where, CFI = Constraints Facing Index, Ch = Percentage of respondents having high constraints, Cm = Percentage of respondents having medium constraints, Cl = Percentage of respondents having low constraints and Cn= Percentage of respondents having no constraints.
Attempts were also made to find out suggestions from the respondents to overcome the identified constraints. Ranking of the suggestions based on percentage of citations for each suggestion was done in this regard. Moreover, various descriptive statistical measures were used for categorization and describing the variables. Pearson’s Product Moment Correlation Coefficient was used for testing the relationships between the concerned variables. SPSS computer package was used for analysis of data.