CHAPTER TWO

LITERATURE REVIEW

2.1 Theoretical review

The Unified Theory of Acceptance and Use of Technology (UTAUT) can be used to explain how web based information system on agricultural marketing. UTAUT identifies four key constructs, Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions (Uba, 2023). This construct refers to the degree to which an individual believes that using a particular technology will help them attain gains in job performance, for agricultural exporters, non-financial resources such as human Capital, Motivation resources, and Social resources, can enhance performance expectancy. If exporters believe that social media marketing can lead to increased market reach, better customer engagement, and ultimately higher exports, they are more likely to be ready to adopt these tools. Effort expectancy is the degree of ease associated with the use of the technology. Non-financial resources that reduce the perceived effort required to adopt social media marketing include access to training programs, user-friendly social media platforms, and technical support.  Application to Agricultural Exporters; Exporters need training programs that enhance their understanding of how to use social media effectively. Workshops, online courses, and resources provided by industry bodies can be crucial in this regard, Access to technical support and user-friendly social media tools can lower the barriers to entry for agricultural exporters. Success stories and case studies from other exporters who have benefited from social media marketing can serve as powerful motivators. Reliable internet access and digital marketing tools provided by industry associations or government bodies can create a supportive environment for adoption (Donmez-Turan, 2020).

2.2 Current challenges faced by farmers and traders in accessing agricultural market information.

In many African developing countries, agricultural sector is regarded as the engine of industrial development. In Tanzania, agriculture contributes greatly to GDP, export earnings and employs over 82% of the workforce (Adam et al., 2012; Manda, 2002; Mkenda and Campenhout, 2011). Majorities of citizens who are engaged in agricultural sector are small-scale farmers living in rural areas whose main source of cash income is the selling of agricultural products (Eskola, 2005; Mkenda and Campenhout, 2011). The sector is very important in generating demand for industrial goods and services. Further, the sector plays a key role in ensuring national food security, and in the process, national security as well. In general, the agricultural sector has a strong multiplier effects across the economy. There is increasing consensus that, in a globalizing economy, a long-term economic growth agenda in developing countries would be feasible only if it has agricultural development that raises rural incomes as its central concern. Dorward et al. (2004) stresses that agricultural development and productivity gains can stimulate and sustain economic transition as countries shift away from being primarily agricultural towards a broader base of manufacturing and services.

2.2 Key features and functionalities required in a web-based agricultural marketing information system

A fundamental feature of AMIS is the provision of up-to-date market prices for various agricultural commodities (Kabbiri et al., 2018). Studies emphasize the need for automated data collection from multiple sources, including wholesale markets, retail markets, and online trading platforms (Baumüller, 2018). Real-time pricing helps farmers negotiate better deals and avoid exploitation by middlemen. An effective AMIS should integrate supply chain tracking, allowing stakeholders to monitor commodity movements from farm to market (Gondwe et al., 2020). Features such as warehouse storage data, transportation logistics, and demand forecasting enhance transparency and reduce post-harvest losses.

Since many farmers in developing regions have limited digital literacy, the system must have a simple, multilingual, and mobile-friendly interface (Aker, 2011). SMS-based alerts and voice-enabled services can improve accessibility for users with low internet connectivity (Fafchamps & Minten, 2012). Incorporating weather forecasts and climate advisory services helps farmers plan planting and harvesting schedules (Rao et al., 2019). Integration with meteorological databases ensures timely alerts on droughts, floods, or pest outbreaks. Predictive analytics tools that analyze historical price trends and demand patterns enable farmers to make informed decisions (Kamilaris et al., 2017). Machine learning models can forecast price fluctuations, helping farmers decide the best time to sell.

2.3 User knowledge and skills on adopting web based information systems.

User knowledge is a fundamental determinant of WBIS adoption. According to the Technology Acceptance Model (TAM) (Davis, 1989), perceived usefulness and ease of use influence user acceptance, both of which are shaped by the user’s familiarity with the system. Studies indicate that individuals with higher digital literacy are more likely to adopt WBIS effectively (Venkatesh & Davis, 2000), Rogers’ Diffusion of Innovation Theory (2003) suggests that early adopters of WBIS typically possess higher technical knowledge, while late adopters may require additional training. Research by Alshamaila et al. (2013) found that insufficient user knowledge leads to resistance, whereas proper training enhances adoption rates.

Technical skills play a crucial role in WBIS adoption. Digital competence, including the ability to navigate web interfaces, interpret system feedback, and troubleshoot issues, significantly affects user engagement (Eshet-Alkalai, 2004). A study by Wang et al. (2018) revealed that users with strong problem-solving skills adapted faster to new WBIS compared to those lacking such skills.

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Research design

The study will use a descriptive research. Descriptive designs are commonly used in social sciences, education, psychology, and other fields to gather information about the current state of affairs, establish baselines, or explore relationships between variables, (Creswell, 2013; Patton, 2002) descriptive research design has been adopted because it is excellent for capturing data as it naturally occurs in real-life settings, The study will use quantitative research approach.

3.2 Area of Study

The study will be carried out in in areas of kabala district.

3.3 data analysis

The study will analyze using SPSS software to analyze data this will be after getting questionnaire information. The researcher will use statistical software to analyze the data and draw relationships between the study variables. The statistical analysis will include; Correlations, regressions and descriptive analysis.

3.4 Data collection methods

The study will use the following data collection methods;

• Questionnaire
• Interviews