Usage Behavior Research Articles

MENTAL HEALTH IMPACTS BY ELEVATED DIGITAL SCREEN TIME DURING COVID-19 PANDEMIC

Introduction – The length of time invested in and the various activities carried out utilizing digital devices are referred to as screen time. The COVID-19 pandemic's restrictions on social interactions exacerbated the overuse of digital gadgets for everyday tasks like socializing, education, shopping, working, meeting, and entertainment. Increased screen usage has been thought to have harmful impacts on psychiatric health in studies. Methods – Several journal articles were used as references, with the inclusion criteria of literature that discuss the mental health impacts of elevated digital screen time during the pandemic of COVID-19 and its recommended measures to minimize its adverse mental health outcomes and the promotion of healthy digital habits. Results – During the pandemic of COVID-19, the unprecedented digital life led to higher numbers of anxiety, sadness, confusion, and unpleasant feelings including irritation and violence. it is crucial to know how screen use time evolved during the COVID-19 period of crisis so that the design of health promotion and intervention may be planned and informed. This study aims to discuss the mental health impacts of elevated digital screen time on the pandemic of COVID-19 and strategies to minimize its adverse mental health outcomes, and the promotion of healthy digital habits. Discuss – Boosting collaborative research involving worldwide nations to tackle COVID-19-related public health concerns can improve global evidence and future policies. Increased screen usage has been displayed to have harmful impacts on psychiatric health in studies. Excessive digital use has been linked to attention deficit disorders, social and emotional cognitive impairment, social withdrawal, phantom vibration syndrome, and major psychiatric diseases such as depression, anxiety, and gaming addiction. Conclusion – It is critical to promote good digital practices and good applications of digital media to avoid the negative effects of increased screen time. To prevent hazardous screen usage and other behaviors that may influence the mental health and welfare of COVID-19-affected people, it is critical to harness the evidence available and adopt multimodal strategies. Keywords: mental health, screen time, COVID-19.

The Effect of IFMIS Symbolic Adoption on Post-Acceptance Use Behavior: Moderated Analysis

Integrated Financial Management Information System (IFMIS) is a a significant milestone in a country's budget reform agenda. The government of Indonesia has launched an IFMIS for work unit level called Sistem Aplikasi Keuangan Tingkat Instansi (SAKTI) in 2022. This research seeks to test the effect of TAM determinants on symbolic adoption and subsequent post-acceptance usage behaviour with individual absorptive capacity as moderator. This research is quantitative research, using PLS-SEM disjoint two-stage approach to assess symbolic adoption, a higher order construct consisting of four dimensions. 93 samples from ministry of public works and housing are analysed and found that both TAM determinants have a positive impact on symbolic adoption and symbolic adoption positively affect post-acceptance use behaviour. Individual absorptive capacity enhances the impact of symbolic adoption on explorative use, but does not moderate the effect on extended use. This research contributes to the field of symbolic adoption by demonstrating how individual absorptive capacity can shape the relationship between symbolic adoption and post-acceptance usage behaviour.

Analysis of The EwacsPro Auto Dispatch System in XYZ Company Using Technology Readiness Level (TRL) and Technology Acceptance Model 2 (TAM2) Adoption

Fleet Management System (FMS) is a system that quite good to manage the haul cycle and can be useful for reducing the idle time of the conveyance. The development of an FMS with an Auto Dispatch System (ADS) that utilizes digital information technology can make management easier. ADS is the system which has assignment logic systems for transport equipment, either directly suggesting to the operator through computerized systems or through dispatchers. XYZ Company developed its own ADS, called Early Warning & Control System Pro (EwacsPro), which has been tested on 1 of 4 total pits they have in ABC jobsite, using the LTE Communication network, which is the first in Indonesia. The problem behind this research is that the system has never been tested for readiness. This research aims to analyze, evaluate and explore the level of readiness of the EwacsPro system using the Technology Readiness Level (TRL) method and also analyze the level of operator acceptance of the use of the EwacsPro system adopting Technology Accetance Model 2 (TAM2) using Structural Equation Modeling (SEM) analysis. The research will provide recommendations to the XYZ management for improvements related to EwacsPro at ABC Jobsite so that it can provide benefits according to the technological design which has been made, using Business Model Canvas (BMC) tool. The EwacsPro system reached level 9 TRL with a fulfillment rate of 80%. There are 7 variables that significantly influence EwacsPro's Usage Behavior directly and 1 variable that significantly influences using 2 influencing moderator variables of the TAM2 analysis. The R-square test also shows that all values are above 0.75, which means that there is a strong influence from the measurement of the exogenous latent variable on the endogenous latent variable.

Acceptance Of Accounting Information Systems For Non-Profit Organizations

This study aims to determine how much users accept accounting information systems when preparing financial reports. Quantitative methods with path analysis techniques are used in this study to facilitate analysis. The population and samples in this study were 30 respondents from users of the accounting information system of the Brayat Pinuji Foundation social organisation with ten social care units spread across the territory of Indonesia. The results obtained from this study are a significant positive effect of perceived convenience on perceived usefulness: perceived convenience on attitude of use, perceived usefulness on attitude of use, perceived convenience on the intensity of usage behaviour, perceived usefulness on the intensity of usage behaviour; usage attitude on the intensity of usage behaviour; and the intensity of usage behaviour on the real use of accounting information systems.

IoT Sensor Network Electricity Consumption Behaviour Using ClusterAnalysis Algorithm for Network Environment

The Internet of Things (IoT) sensor network plays a crucial role in monitoring electricity consumption behavior by collecting real-time data from various connected devices. Utilizing cluster analysis algorithms, researchers can effectively segment and analyze consumption patterns within a network environment. By grouping similar usage behaviors, these algorithms reveal insights into energy efficiency, peak usage times, and anomalies in consumption. The paper presents a novel methodology for analyzing and classifying users' electricity consumption behavior, utilizing the Clustering Behavior Analysis Weighted Classification (CBAWC) algorithm. By leveraging cluster analysis techniques and weighted classification, the proposed approach allows for the segmentation of consumers into distinct clusters based on their consumption patterns. Through the application of CBAWC, the study provides insights into the diverse behaviors exhibited by consumers, ranging from moderate to high consumption levels, varied peak hours, and peak days. The classification results demonstrate the effectiveness of the algorithm in accurately assigning users to their respective clusters, enabling stakeholders to better understand consumption trends and tailor energy management strategies accordingly. Through the application of CBAWC, consumers are segmented into distinct clusters based on their consumption patterns. For example, clusters exhibit varying average daily consumption levels, with Cluster 1 consuming around 300 kWh, Cluster 2 consuming approximately 450 kWh, and Cluster 3 consuming about 280 kWh on average. Additionally, clusters display different peak hours per day and peak days per month. The classification results demonstrate the algorithm's effectiveness, with high accuracy scores ranging from 0.86 to 1.00 across different user groups.

Adoption Drivers of Digital Platform for Coal Production Planning: an Extended UTAUT Model Using PLS-SEM Analysis

In 2022, the coal production industry encountered unprecedented challenges accompanied by a substantial global commodity price surge. The operational impact of this situation surpasses current technological capabilities of coal companies, particularly in optimizing coal blending scenarios. A pivotal aspect of digital transformation involves integration of new digital platform for production planning. This study employs the Unified Theory of Acceptance and Use of Technology in conjunction with decision theory to identify key factors influencing the platform adoption at a coal mining company. Structured questionnaires were utilized, followed by analysis using the SmartPLS 4.0.9.9 software. Findings reveal that both Performance Expectancy and Effort Expectancy positively influence users’ behavioral intention to adopt digital platform for production planning. Behavioral Intention, in turn, significantly impacts actual usage behavior. Unanticipated situational factors and others' attitudes were found to have negligible mediating effects, while variables such as age and experience showed no moderating influence on the pathways from behavioral intention to usage behavior. Companies are advised to improve digital platform performance through functionalities enhancements and pilot testing to reduce perceived effort and stimulate behavioral intention. Additionally, fostering a positive organizational mindset through routine motivational communications can further stimulate usage behavior.

Hybrid model for robust and accurate forecasting building electricity demand combining physical and data-driven methods

The high-precision prediction of building electrical demand is paramount significance for optimal control of building systems and the reduction of operational energy consumption. The pure physical simulation software struggles to depict buildings' energy usage behavior accurately; while purely data-driven methods lack the interpretability in real-world physical scenarios. To address the challenges existing in current models and algorithms, this paper proposes a hybrid model that combines physical mechanism simulation and machine learning to complement each other. Empirical Mode Decomposition (EMD) is utilized to decompose complex data into simple components in pre-processing (RMSE, MAE, and SMAPE all decreased by around 0.2–0.3). And K-means clustering algorithm is used to extract features of building electrical data and determine employee working states (R2 increased from 0.3 to 0.85). Shapley Additive explanations (SHAP) values and Pearson correlation coefficients are used for model interpretability analysis. Combining weather forecasts, the Monte Carlo method is employed to obtain prediction intervals. Furthermore, this study explores the use of transfer learning to save computational costs and reduce the amount of data needed for predicting, especially for high-rise buildings or floors with insufficient historical data. The hybrid model results indicate that the R2 can exceed 0.85, up to 0.9, while pure physical simulation can only achieve 0.3. MAE, MSE, and RMSE remain below 0.07, while SMAPE stays around 0.2. The R2 value of the transfer learning model can surpass 0.8 (MSE, MAE. MAE, MSE, and RMSE remain below 0.2, while SMAPE stays around 0.4). The model's residuals obey normal distribution, indicating the model has strong generalization capabilities.

Appliance utility in low-income housing: On the relation between socio-economic characteristics, thermal comfort and appliance usage pattern

This study examines the effect of behavioural aspects, dwelling characteristics, and socio-economic drivers, particularly cooling appliance usage patterns, on perceived thermal comfort and further estimates the utility of using different household appliances within low-income archetypes of Mumbai, India. Field-survey-derived 625 samples were drawn from slums and rehab housing and empirically examined using the Ordinal Logistic Regression and Fractional Multinomial Logit models. The results reveal a significant influence of household income and occupant behaviour on thermal satisfaction. Income is a significant barrier to the adoption and usage of thermal appliances. Residents in social housing drive higher utility from operating refrigerators and TVs than washing machines. Low-income quartiles, however, reveal a higher utility from using iron. A positive correlation exists between the proportion of working individuals and appliance usage like washing machine, refrigerator and iron. Surprisingly, larger households are less likely to use exhaust. Considerable heterogeneity thus exists in thermal comfort and appliance usage behaviour and derived energy services in the studied area. This research sheds light on the critical role of income and socio-cultural factors in shaping thermal comfort perceptions and appliance usage patterns. The findings have implications for policy interventions aimed at improving energy efficiency, indoor comfort, and access to essential appliances in low-income communities of developing nations.

Smartphone Usage Behavior And Technology Acceptance In Oil Palm Replanting

Replanting activities need support using information technology so that plantation business actors can access information related to plantations independently and accurately. This research analyzes smartphone usage behavior using a technology acceptance model approach among oil palm users. This research was conducted at the Karya Dharma III Village Unit Cooperative, Keranji Guguh Village, Koto Gasib District, Siak Regency. The sample taken in this research was 30 planters. The sampling technique in this research uses a snowball sampling technique, which is obtained through a rolling process based on the relationship between one respondent and another. Snowball sampling is a sampling method that uses initial illustration data to identify other illustrations that meet the criteria. Data analysis was carried out using a descriptive study based on a Likert scale. The final results of this research show that using smartphones among oil palm planters in Keranji Guguh Village as a medium to help search for and receive information for oil palm planters is classified as moderate, with an average score of 2.84. Smartphone use has become an alternative media planters use to search for information on supporting oil palm rejuvenation activities, land clearing, planting spacing patterns, using superior seeds, and plant maintenance replanting.

Perceptions and Satisfaction With the Use of Digital Medical Services in Urban Older Adults of China: Mixed Methods Study.

In an aging and information-driven society, older adults have distinct perceptions of and specific demands for digital medical services. It is essential for society to understand these needs and develop a more thoughtful approach to digital health care. This study aims to evaluate the behavioral intention and satisfaction of older adults with digital medical services by identifying the perceived factors and the pathways through which these factors influence their behavior. This study used a mixed methods approach, combining qualitative and quantitative analyses. A focus group interview was conducted with 30 randomly selected older adults, and the interviews were transcribed verbatim and coded using grounded theory. In addition, 876 valid questionnaires were collected to describe older adults' perceptions of and satisfaction with digital medical care. Then, t tests and ANOVA were used to explore differences among various demographic groups, while hierarchical multiple regression was conducted to identify the factors most closely related to satisfaction. Structural equation modeling was used to identify multiple mediating effects. The qualitative study identified the core category of "medical service relief and transformation paths for older adults in the context of digital reform." Quantitative analysis revealed that more than half of the older adults were satisfied with digital medical services, and behavioral intentions were higher among those with higher incomes and education levels. Structural equation modeling confirmed that external variables, such as digital skills training, positively influenced perceived ease of use (β=.594, P<.001), perceived usefulness (β=.544, P<.001), and promoted digital medical behavioral intentions (β=.256, P<.001), while also reducing perceived risk (β=-.295, P<.001). Additionally, perceived ease of use (β=.168, P<.001) and perceived usefulness (β=.508, P<.001) positively impacted behavioral intention, whereas perceived risk (β=-.05, P=.037) exerted a negative influence. Furthermore, behavioral intention (β=.641, P<.001) significantly and positively affected older adults' satisfaction with digital medical care. The mediation test identified 4 significant paths: (1) external variables → perceived ease of use → behavioral intention (effect size of 13.9%); (2) external variables → perceived usefulness → behavioral intention (effect size of 38.4%); (3) external variables → perceived ease of use → perceived usefulness → behavioral intention (effect size of 10.1%); and (4) a direct effect (35.5%) from external variables to behavioral intention. Based on the study's findings, addressing the needs of older adults and enhancing perceived usefulness are the most effective ways to encourage the use of digital health care devices. Community support plays a crucial role in helping older adults integrate into digital health care, and adapting the design of services and products to suit their needs improves their perceptions of digital health care. This, in turn, promotes usage behavior and satisfaction, while the negative impact of perceived risk remains minimal.

Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction.

The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. Our evaluation, utilizing a three-phase power state and daily electricity usage data provided by the State Grid Corporation of China, demonstrates the efficacy of our approach in accurately identifying theft modalities, including evasion, tampering, and data manipulation.

Effect of older adults willingness on telemedicine usage: an integrated approach based on technology acceptance and decomposed theory of planned behavior model

BackgroundTelemedicine, as a novel method of health management system, has demonstrated to have a significant impact on health levels. However, a challenge persists in the form of low usage rates and acceptance among older adults in China. There are accumulating evidence that willingness will affect the telemedicine usage among older adults. This study investigates factors influencing older users’ trust in adopting telemedicine technology, thereby promoting actual use.MethodsA questionnaire survey was conducted with 400 urban seniors aged 60 and above. Drawing from the Technology Acceptance Model (TAM) and the Decomposed Theory of Planned Behavior (DTPB), the author combines elements such as Perceived Usefulness, Perceived Ease of Use, Subjective Norms, Service Environment, Self-Efficacy, Behavioral Intention to Use, and Usage Behavior. The aim is to explore the interrelationships between these factors.ResultsPerceived Usefulness (PU) and Service Environment (SE) significantly and positively impact Behavioral Intention (BI) to use telemedicine, with Trust (TR) identified as a crucial mediating variable. Enhancing trust can substantially increase older adults’ intention to use telemedicine services. Furthermore, the study reveals a significant relationship between older adults’ trust in telemedicine and factors such as Perceived Usefulness (PU), Service Environment (SE), Subjective Norms (SR), as well as Emotional Risk (ER) and Cost Risk (CR), the latter two tending to decrease Trust(TR).ConclusionsThis paper constructs and validates a combined model based on TAM and DTPB, comprehensively exploring the potential factors influencing the older adults’ intention to use telemedicine. The findings suggest that telemedicine services for older adults should prioritize improving user perception and enhancing trust throughout the service process to effectively increase their willingness to use these services.

Object Affordances through the window of Verb Usage Patterns and Behavior

Object Affordances through the window of Verb Usage Patterns and Behavior

Understanding Data-Driven Analytic Decision Making on Air Quality Monitoring an Empirical Study

Air quality monitoring is increasingly relying on data-driven analytic decision-making tools to provide accurate and timely information, forming the background of this study. The objective is to understand the factors influencing the adoption and usage behavior of these tools using the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The method involves incorporating UTAUT2 constructs Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), and Habit (H), alongside external variables such as Considered Risk (CR) and Considered Trust (CT). Data from 287 respondents were analyzed to assess their impact on Behavior Intention (BI) and Usage Behavior (UB). The results demonstrate that both trust and risk considerations significantly affect user behavior, underscoring the need to address these factors to enhance the adoption of air quality monitoring systems. In conclusion, this research provides valuable insights for developers and policymakers on improving the implementation and acceptance of data-driven technologies in environmental monitoring, thereby contributing to more effective air quality management.

272 Relationships of salivary cortisol and automated brush system usage in freestall housed dairy cattle

Abstract The dairy industry has seen a significant shift in animal welfare practices, with growing emphasis on providing optimal living conditions for indoor-housed animals. Intensive management of dairy cows has become increasingly common, offering a controlled setting that ensures the well-being and productivity of the cow. Enrichment strategies such as automated brushes are proposed as a means for intensively managed dairy cows to improve their physical, mental, and emotional well-being. These behavioral benefits can contribute to an enriched living environment. Additionally, automated brushes have been shown to decrease cortisol concentrations which can lead to impacts such as heightened immune systems and increased productivity. With this known, the objective of this study was to evaluate the usage of an automated brush systems influence on cortisol concentrations of lactating dairy cows housed in a freestall setting. Cows (n = 29) over three trials (20 d each) were evaluated for duration of brush activity utilizing scan sampling of video recordings (0500 h-0900 h and 1700 h-2100 h; post milking) totaling 8 h/d throughout each trial. Animals were managed in accordance with standard operating procedures of the dairy, and all data was passively collected without influencing behavioral changes. Video capture was performed using a four camera closed-circuit media recording system. Cameras were placed in the corners of the pen, allowing full view of animals interacting with the brush system. Video images were scored using NOLDUS Observer XT. Saliva collection was performed during milking sessions using Salimetrics SalivaBio Children’s Swab kits. Salivary samples were analyzed using commercially available cortisol ELISA kits. Results indicate that cows exhibited greater cortisol concentrations in the mornings compared with the evenings (P = 0.018) and showed increased brush usage in the evenings compared with the mornings (P = 0.019). These findings suggest a pattern where cows tend to have increased cortisol concentrations in the mornings and engage in more brush usage behavior during the evenings. An analysis was conducted to examine relationships between brush usage and cortisol concentration, specifically focusing on the effects of automated brush usage on cortisol levels. Cortisol concentration was only slightly related to brush usage (R2 = 0.03; P ≤ 0.01). Partitioning the trial in halves provided insight of continuous brush usage and its relation to cortisol. Results illustrate (d 1 to 10), brush usage and cortisol were slightly correlated (R2 = 0.13; P ≤ 0.01) during the first half of the study, while there was no relationship noted during the second half (R2 = 0.002; P = 0.22). In conclusion, this study provides valuable insights into the relationship between automated brush systems and cortisol in lactating dairy cows housed in a freestall setting. Though these findings contrast those in literature, this details the need for additional research to elucidate brush usage impact on dairy cow hormonal status.

Video-On-Demand Streaming Services Subscription Antecedents and Consequences: The Uses and Gratifications Theory Approach

Introduction: This study aims to examine the impact of affective gratification, tension release, cognitive gratification, perceived risk, and viewing addiction on subscription behavior as antecedents and its consequence namely customer engagement. Background Problems: The need for satisfaction is a trigger for media use. As time passes, research on media use through use and audience gratification approaches will always be needed, so this should be carried out in the context of streaming services. Novelty: This research focuses on streaming services using the uses and gratifications theory approach. Similar research has focused on the theoretical implications of usage attitudes, but this research has implications for actual usage behavior, namely subscription behavior. Research Methods: Data have been collected using an online survey with purposive sampling and then analyzed using partial least squares structural equation modeling. Findings: The results suggest that only five of the 13 hypotheses proposed are supported. Companionship and cognitive gratification do not affect subscription behavior, while perceived risk did not impact subscription behavior negatively. Conclusion: The results of this study indicate that streaming service subscribers are more concerned with functional benefits, such as service usability, variety of content, entertainment, and relaxation media. Furthermore, the implications of this research provide benefits both theoretically and practically

Explore the driving factors of designers’ AIGC usage behavior based on SOR framework

Despite the widespread recognition of artificial intelligence’s advantages, it cannot replace human independent thinking and creativity, especially in fields such as artistic design that require creativity. Previous studies often examined its development trends from the perspective of technical advantages or application processes. This study explores the attitudes and acceptance of creative industry practitioners towards Artificial Intelligence Generated Content (AIGC) from the perspective of user behavior modification. Utilizing the Stimulus-Organism-Response Model (SOR) as the theoretical background, this research integrates the Technology Acceptance Model, Theory of Planned Behavior, and Self-Efficacy to form the research framework. By employing a mixed-method approach combining quantitative and qualitative analyses, data from 226 designers were explored, and structural equation modeling was used to verify the correlations between endogenous factors. The results indicate that users’ facilitating conditions significantly influence self-efficacy, which in turn determines their intention to adopt AIGC. Additionally, semi-structured interviews revealed that factors hindering the widespread application of AIGC mainly encompass legal security, ethical risks, and fairness. This study extends the application scope of the Stimulus-Organism-Response Model (SOR), enriches the Technology Acceptance Model, and provides a new research framework for the application of AIGC in the creative industry, detailing the responsibilities, processes, and content of designers in the Artificial Intelligence Generated Design (AIGD) process.

The Correlation Between The Duration of Playing Gadgets in Young Children and Parental Anxiety Levels in Sidodadi Village

This study aims to analyze the correlation between the duration of gadget play in early childhood and the level of parental anxiety in Sidodadi Village. This study uses an associative method with a quantitative approach. The sampling technique used in this study is the saturated sampling technique found in Non-Probability Sampling. The data collection technique was a questionnaire, the duration of playing gadgets, and the level of parental anxiety. Meanwhile, the analysis technique used is using Pearson Product Moment using SPSS software. The results of the study show that the level of duration of playing gadgets in children in Sidodadi Village, Penarik District, Mukomuko Regency, Bengkulu Province tends to be high. A total of 46.67% of respondents were included in the category of high gadget playing duration, 36.67% were in the medium category, and 16.67% were classified as low. This shows that the use of gadgets has become a dominant activity among children in the village. The level of anxiety of parents related to the use of gadgets by their children also tends to be high. The results showed that 30% of respondents had very high levels of anxiety, 26.67% high, 25% moderate, 15% low, and 3.33% very low. The dominance of the category is very high and high, indicating significant concerns among parents regarding the impact of gadget use on their children There is a significant and positive relationship between the duration of gadget play and parental anxiety levels, with a correlation coefficient of 0.589 This suggests that the higher the duration of gadget play in children, the higher the level of anxiety experienced by parents This relationship indicates a close connection between children's gadget usage behavior and parental psychological responses.

Exploring the impact of internet usage on individuals’ social status discordance: evidence from China

This study examines the impact of internet usage frequency and behavior on social status discordance using CSS2021 data, and adopts a causal random forest model to test the heterogeneity of the impact of internet usage on social status discordance. The results reveal a clear inconsistency between objective and subjective social status among Chinese residents. In addition, increasing internet usage frequency can make actors more inclined to underestimate their social status. Moreover, capital enhancement through the internet significantly reduces the likelihood of status inflation. Finally, individuals with higher social status are less likely to have an upward bias in status cognition than those with lower social status when they use the internet frequently and use it for capital enhancement. These findings advance our understanding of social status identification among Chinese residents and provide different perspectives for studying social stratification in the digital age.

Examining Farmers’ Usage Behavior on Agricultural E-Markets: Integrating UTUAT2 and Swift Guanxi Factors

The agricultural sector recognizes the potential of electronic marketplaces (e-markets) in enhancing efficiency, market access, and pricing over traditional markets. Despite this acknowledgment, farmers’ adoption of these platforms in developing countries remains limited. This study examines the determinants of farmers’ usage behavior on agricultural e-market platforms by integrating the UTUAT2 and swift guanxi factors. The study employed a self-administered questionnaire to gather data from 330 farmers in India who had exposure to agricultural e-markets and utilized PLS-SEM to analyze the data collected through purposive sampling. The study found that UTAUT2 and swift guanxi factors significantly influence farmers’ intentions to adopt agricultural e-markets, with the exception of effort expectancy. The outcomes of the multigroup analyses indicate that both educational qualifications and crop type significantly influence farmers’ usage behavior. This study contributes to practical applications and academic literature by integrating the UTAUT2 framework and swift guanxi concepts in the context of the agricultural e-market. Notably, while all dimensions prove significant, the negative impact of effort expectancy on usage intention stands out, a departure from previous e-commerce studies. This study enriches the technology adoption literature by uncovering the pivotal factors shaping farmers’ adoption of e-market platforms in agriculture.