Asian Journal of Agricultural Extension, Economics & Sociology

Analysis of Consumer Preferences on Purchasing Decisions and Satisfaction toward Local Coffee in West Lombok Regency

2026-08-31T10:43:21+00:00

Aims: This Study Aimed To: (1) identify consumer preferences towards local coffee in West Lombok Regency; (2) analyse the influence of consumer preferences on purchasing decisions for local coffee in West Lombok Regency; and (3) analyse consumer satisfaction with local coffee purchases using the Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA).

Study Design: This study employed a quantitative research design with descriptive and causal approaches.

Place and Duration of Study: The study was conducted at five local small and medium-sized coffee enterprises (SMEs) in West Lombok Regency from April to June 2026.

Methodology: The study involved 100 respondents, with the sample size determined using the Lemeshow method and respondents selected through purposive sampling. Respondents were at least 17 years old and had experience purchasing or consuming local coffee from the selected SMEs. Data were collected through questionnaires administered directly to respondents. Data analysis included consumer preference analysis, binary logistic regression, Customer Satisfaction Index (CSI), and Importance Performance Analysis (IPA).

Results: The taste-and-aroma attribute was the most important consumer preference attribute, with a mean score of 3.19, followed by the brand-and-availability attribute (3.17), while price and promotion had the lowest and equal mean scores (3.09). Binary logistic regression showed that the price and brand-and-availability attributes had positive and significant effects on local coffee purchasing decisions, with brand and availability having the largest odds ratio (Exp(B) = 27.890). The taste-and-aroma and promotion attributes did not have significant partial effects. The CSI result indicated an overall consumer satisfaction level of 61.79%, categorised as satisfied. The IPA results showed that taste and aroma and brand and availability were located in Quadrant I, indicating the main priorities for improvement, while price and promotion were located in Quadrant IV, indicating attributes considered excessive.

Conclusion: Consumers place greater importance on taste and aroma as well as brand and availability when purchasing local coffee. Although overall consumer satisfaction was categorised as satisfied, improvements should prioritise taste and aroma and brand and availability. The price and brand-and-availability attributes were also significant determinants of purchasing decisions, indicating that product quality, availability, and appropriate pricing are important considerations for local coffee businesses in West Lombok Regency.

What Drives Institutional Arrangement Choice: Transaction Cost Barriers, Structural and Governance Exclusion among Smallholder Potato Farmers in Kenya

2026-08-31T10:49:28+00:00

In Elgeyo Marakwet County, Kenya's third-most potato-producing region, over 84% of smallholder transactions flow through broker intermediaries, not because farmers prefer this arrangement, but because institutional barriers systematically exclude them from higher-value governance structures. However, empirical studies on smallholder market participation across sub-Saharan Africa have two persistent limitations: most treat participation as binary, obscuring substantial heterogeneity across arrangement types, and almost none correct for the endogeneity of arrangement choice, because the transaction costs a farmer faces are themselves shaped by the arrangements in which they participate, rendering standard regression estimates directionally misleading. This study addresses the following research question: What institutional, socioeconomic, and structural factors determine smallholder potato farmers' participation in different clusters of institutional arrangements, after accounting for the endogeneity inherent in the choice of arrangements? Using cross-sectional data from 577 smallholder potato farmers sampled via multi-stage systematic random sampling, we apply a control-function multinomial logit (CF-MNL) model, instrumenting four endogenous transaction-cost and enforcement variables to address the simultaneity between observed transaction costs and arrangement choice, with ward-level historical contract prevalence and community-institution indices serving as instruments. First-stage F-statistics confirm instrument relevance and strength: transport costs (F = 167.30), information search costs (F = 17.08), negotiation costs (F = 10.55), and informal sanctions (F = 18.48). Endogeneity tests confirm severe bias in uncorrected estimates (χ² = 51.99 to 1,303.24, p < 0.001), and model fit improves from Pseudo-R² = 0.160 to 0.183 following correction (LR χ² = 27.54, p < 0.001). After correction, access to extension services emerges as a consistent positive determinant of higher-value arrangements. Farm size serves as a volume threshold, confining small-farm households to low-intensity broker arrangements regardless of motivation. Gender effects are large, persistent, and unexplained by observable endowments, consistent with the presence of gender-related barriers to institutional access that operate beyond the measured characteristics and that generic market development programmes may not adequately address. These findings are consistent with the interpretation that participation is not a matter of farmer preference but of institutional access. Drawing on comparative evidence from SSA countries, the study identifies extension reorientation, aggregation infrastructure, and gender-responsive instruments as the most actionable policy responses.

Governance Conditions and Local Barriers to Artificial Intelligence in Agricultural Extension and Rural Public Services in Yongding District, China

2026-09-03T11:14:51+00:00

Aims: This study examines how grassroots and local actors perceive AI-enabled agricultural extension and rural public services in Yongding District, Longyan City, Fujian Province, China, with particular attention to perceived opportunities, implementation barriers, and governance conditions.

Study Design: A qualitative case-study design was used, incorporating thematic analysis with descriptive frequency coding.

Place and Duration of Study: The primary study area was Yongding District, Longyan City, Fujian Province, China. Interview materials were collected between December 2025 and March 2026.

Methodology: The analysis included 30 anonymised interviews, comprising 25 Yongding-focused core interviews and 5 contextual reference interviews from other Chinese localities. A multi-label thematic coding procedure was used to identify recurring themes related to AI use, agricultural extension, rural public services, and grassroots governance. Theme frequencies were calculated descriptively and were not used for statistical inference.

Results: Policy diffusion, feedback channels, and institutional support formed the most frequent theme, appearing in 25 of 30 interviews (83.3%). Efficiency and administrative workload reduction appeared in 21 interviews (70.0%), followed by digital literacy, training, and human-capacity gaps in 15 interviews (50.0%), data security, privacy, ethics, and accountability risks in 14 interviews (46.7%), and local fit and practical implementation barriers in 13 interviews (43.3%). Agricultural extension and production support appeared in 8 interviews (26.7%). Participants generally viewed AI as useful for document preparation, information retrieval, data handling, agricultural advice, and routine service support, while emphasising constraints related to policy communication, training, cost, infrastructure, privacy, accountability, and local adaptation.

Conclusion: AI-enabled agricultural extension in Yongding District should be understood as a local governance and implementation challenge rather than solely as a technical issue. Effective adoption requires locally appropriate applications supported by training, feedback mechanisms, infrastructure, responsible data governance, and clear accountability arrangements.