In general, the decision of technologyadoption is a cognitive activity where an individual makes a positive (or negative) disposition (termed attitude in TPB) prior to making the choice of action (i.e., behavioural intention) . Engagement or dis-engagement of behaviour such as the decision for technologyadoption is also influenced by the perceived social pressure exerted on individuals created by the expectations of important referents (termed subjective norms in TPB) . In addition, behavioural intention is also influenced by the perceptions of one’s ability to perform a given behaviour (termed perceived behavioural control in TPB) . The decision-making in SMEs are influenced by three determinants; namely: (1) the decision-maker s’ cognitive disposition (attitude); (2) influence of competitors, regulatory bodies, the government, customers, vendors and employees (subjective norms); and (3) the perceptions of the key stakeholders (e.g., CEO, senior managers, employees) regarding their ability to utilize the technology effectively (perceived behavioural control). This ultimately defines the SME’s eventual choice of action (behavioural intention / intention to adopt). We recognize that the three determinants of behavioural intention are vital for the decision of cloud ERP adoption in SME’s. However, we argue here that a better comprehension of how the influence of such determinants varies during the adoption process (a multi- stage phenomenon) is even more important for the complete understanding of the adoption.
estimated using this ATE parametric model. For comparison purposes, we have also estimated a “classic” probit adoption mode which is a model of the determinants of joint exposure and adoption. The estimation was done in STATA using the Stata add-on adoption command developed by Diagne (2007) to automate the estimation of ATE adoption models and related statistical inference procedures (see Diagne, 2008). In the empirical estimation we also test the effect of a number of other factors reported in literature regarding the effect technologyadoption. For example, Feder and Umali (1993) and Cornejo and McBrid (2002) review factors that affect technologyadoption and report that technologyadoption is linked to resource endowment in terms of human, physical and financial capital as well as the characteristics of the technology itself. Conley and Udry (2003) cited in Phillips 2008 show that farmers adjust their activities in line with the successful experimentation of others, such that social networks are important for information sharing and consequently for adoption to occur. Related to the issue of information sharing, there is considerable literature discussing the role of formal and informal information sources in facilitating technology diffusion and adoption. We include such factors in our analysis in which we explore factors that affect technology awareness and those that affect technologyadoption. While we expect some factors to have a similar effect on both, some factors that affect awareness of the technology by the farmer may be different from those that affect the decision to adopt.
Nowadays, mobile banking technology is one of most important technologies in the banking sector. As a result many banks all over the world are adopting it and taking advantage of the technology. Ethiopia, on the other hand didn’t adopt the technology yet. Therefore, this research is conducted to identify the determinants of mobile technologyadoption of commercial banks in Ethiopia. Thus, the sampling technique used is complete enumeration whereby all commercial banks are considered. However among the seventeen actively operating banks, two banks could not be accessed. Hence the Information Technology managers and presidents of fifteen commercial banks are considered. Data has been collected from the target respondents using questionnaire and interview and analyzed using descriptive analysis. Accordingly technological and non-technological factors are identified. Technological factors are factors that are directly related with the attributes of the technology to be adopted. Such technological factors that are found to determine commercial banks mobile banking technologyadoption in Ethiopia includes relative advantage, compatibility, simplicity, observability and trialability. In addition non-technological factors also determine mobile technologyadoption of the Ethiopian commercial banks. They are external in nature; in that they are not directly related with the technology itself. These non-technological factors are economical capability, political issue, societal issues as well as organizational readiness. From the interview with the presidents of the banks, we have also found that the main problems that block banks from adopting mobile banking technology yet is the lateness of National Bank of Ethiopia to enact a law regarding mobile banking technologyadoption. Moreover, the intention of commercial banks is determined. The banks expressed that they have intention to adopt mobile banking technology.
There is exhaustive literature on technologyadoption rates and the relationship between technologyadoption and relevant socioeconomic and policy variables. Yet adoption estimates derived from the application of standard techniques such as the probit and tobit yield biased estimates. This paper applies the modern evaluation technique: the counterfactual outcome framework to data from about 400 households in Malawi to assess the patterns of diffusion and adoption of improved pigeonpea varieties and their determinants. We find the sample adoption rate of improved varieties to be 14 % while the potential adoption rate if the improved varieties were widely disseminated is estimated at 41 %. The adoption gap resulting from the incomplete exposure to the improved pigeonpea is 27 %. Moreover, adoption is also found to be high among female-headed households, older farmers and those with access to credit. The findings suggest that for increased adoption, there is need for increased involvement of extension workers is the dissemination of information about improved pigeonpea varieties, a robust pigeonpea seed system to increase seed availability to farmers as well as the need for improved access to credit.
each other. This phenomenon, also called the “bandwagon effect”, is not unusual and has been observed during the adoption of IT innovations such as e-business, EDI and organizational website, where the “adoption decisions may have more to do with interorganizational isomorphic processes than rational intraorganizational criteria such as efficiency” (p. 620-621). For the manager of Hardware & Technology Support Department at Al Othiam, “top management support will be the key of any RFID-enabled retail project”. As for the executive manager of Al Danube, he believed that RFID technology will facilitate the “data availability & integrity and quick data access” within the sector. In conclusion, it appears that the vast majority of the dimensions discussed by the respondents are in line with the relative advantage of RFID technology, the importance of the top management support, the technology competence during the adoption decision process, and the social issues and competitive pressure that may influence the adoption or non- adoption of the technology.
In this study, descriptive statistics (percentage, frequency and mean) were mainly used. The descriptive analysis was conducted using Statistical Package for Social Science (SPPS). Binary logistic regression was incorporated to analyze relationships between a dichotomous dependent variable and independent variables. The logistic regression was fitted using method of rice production technologyadoption as dependent variable and the listed demographic and socioeconomic variables as explanatory variables which is assumed to determine practice of adoption of rice production technology. The response variable is binary, taking values of one if the farmer adopts and zero otherwise. However, the independent variables are categorical, continuous and dummy.
We measure foreign ownership by the percentage of the company owned by private foreign individuals, companies, or organisations. Companies which choose to have an international presence are plausibly more willing and able to manage new technologies than businesses that stay at home. The literature on international (typically aggregated) technology diffusion suggests that foreign direct investment can result in technology spillovers (Keller, 2004). Moreover, foreign owned companies have greater access to finance (Beck et al, 2006) and so greater ability to fund investment in technology, which is likely to be a particularly important factor on adoption in developing countries where institutional constraints on financing exist (Beck et al, 2006). We can reason in the same way as when a company is a subsidiary, so that initial adoption would be increased by foreign ownership and intensification would be left unchanged, except that initial adoption is perhaps even more strongly increased due to the presumed innovativeness of companies with overseas operations, and their access to finance.
Power outages are potentially endogenous with internet initial adoption or intensification, since companies may acquire electricity in order to get internet access (and so outage counts may only increase from zero as the internet is acquired). We could not find a strong instrument that was also exogenous, and so we initially ran the equations without any instrumentation on our full sample. Power outages exerted no effect on initial adoption, and were associated with an increase in intensification. This latter result is best explained by the reverse causality, so companies that use the internet have electrical power more often which breaks more often. We address the endogeneity by restricting the sample to companies that are highly likely to use electricity irrespective of their internet usage. As this procedure greatly reduces the sample size, we report the results in section six looking at extensions to our model. In section five we exclude power outages as a determinant variable.
With the intention of exploring further the robustness of the results presented, Table 4 shows also the estimate of the equation (3) with a term of error like that of expression (6) for the ROAE as the dependent variable, but with two other versions of the data base: a) not including the 7 main banks of the system, 21 and b) considering all the banks but only for the period 2007-2012. These two versions of the data base confirm the results obtained previously, even making them firmer. In the first place, the coefficient of lagged profitability is positive and significant to 1 percent, with values of between 0.38 and 0.42. Taken together, the findings from Tables 3 and 4 give a range of persistence values of between 0.38 and 0.48, but in seven of the eight regressions the coefficient is above 0.40. How does this value compare with that found in other studies? For example, Jara et al. (2011) found a coefficient of persistence of between 0.40 and 0.44 for a combination of 6 countries from Latin America and a number of banks from the U.S.A., which turned out to be very similar as that found for this study. 22 If we compare these results with others, Dietrich and Wanzenried (2014) found a coefficient of persistence for middle income countries (where Mexico may be placed) of 0.33, 23 which is lower than the figure estimated in the present study; and the coefficient they found for high income countries is considerably less (0.14). As for Ben Naceur and Omran (2011), they found an average persistence coefficient of 0.31 for 11 countries of the Middle East and North Africa.
the Information Economy report, UNCTAD stated that lack of confidentiality of data results, the decline of the confidence of users over the use of online platforms leads to reluctance on the usage of online facilities . Mostly, the fear of the customer is that their information can be used without their authorization and at times, the changing of the data. Customers need of touch and feel and the presence of a physical shop or office is a contributor to the decision of using e-commerce and other technological platforms too . Despite these challenges, there has been an upward trend of adoption of technology, considering that some banks have closed some of their branches, to serve their customers on the electronic platform.
The objective of this study was to evaluate the factor that influences adoption row planting wheat crop technology. Binary logit and cross-sectional survey data were employed to attain the objective of the study. The study applied cross sectional household level data collected in 2016/2017 cropping season from 187 samples farming household head. The main factors influencing adoption of row planting wheat crop technology are the age of household head, education level household head, family size, size of cultivated land, holding of livestock, use of credit services and extension services. Therefore, it is used to scaling up the best wheat crop row planting technology and practices of the adopters to other farmers can be considered as one option while introducing new agricultural practices and technologies is another option.
A predominantly qualitative methodology was used to interview travel agencies in the context of Jamaica. All firms which have similar characteristics in terms of ownership and management structure, in particular where owners are themselves the managers and provide leadership for the organization, were interviewed. The owner-managers of these firms were interviewed to gather deep perspectives from local industry experts on industry challenges, current technology involvement and future directions. Exploratory descriptive quantitative methods were used to analyze firm characteristics and their relationships to internet adoption for sales and marketing as well as the intention to use these technologies in firms, while a deeper exploration into owner-managers was achieved through qualitative enquiry. A pilot study and 2 phases of data collection were carried out. The findings indicate that the leadership role is more significant than has been previously posited.
According to the estimates obtained by Garza-Rodríguez (2000), both moderate and extreme poverty increased in Mexico during the 1994-1996 period, and both the depth as well as the severity of poverty also increased in the same period. Although the author did not decompose the poverty changes as due to decrease in income and the worsening of income distribution, it is possible that both factors played a role in the increase in poverty levels that occurred during the period. Thus, although the Gini coefficient declined during the period, indicating a reduction in income inequality, the Lorenz curves for the two years intersect in the lower percentiles of income, which indicates that the income share of the poorest of the poor decreased during the period.
The importance of food security has been addressed nationally and internationally. Food security is defined as the situation when all people, at all times, have physical and economic access to sufficient, safe, and nutritious food to meet their dietary needs and food preferences for a healthy and active life (FAO 1996). At a global level, the number of people suffering from hunger and poverty exceeds one billion, which represents one-seventh of the world ’ s population (FAO 2009). As for the situation in Mexico, in 2010 the proportion of population that suffered from any level of food insecurity was 44.3 %. In particular, 19.5 % of the Mexican population reported experiencing very low food insecurity, 14.0 % moderate food insecurity, and 10.8 % severe food insecurity. In terms of the number of persons, 49.9 million people in Mexico were experiencing some degree of food insecurity in 2010 (Consejo Nacional de Evaluación de la Política de Desarrollo Social CONEVAL 2011a). In 2008, the proportion of Mexican population under moderate food insecurity and severe food insecurity was 12.8 % and 8.9 %, re- spectively. This means that the two most severe levels of food insecurity in Mexico in- creased from 2008 to 2010 (Consejo Nacional de Evaluación de la Política de Desarrollo Social CONEVAL 2011a).
Since 1986, collaborative efforts between ICRISAT and the National Agricultural Research systems in Tanzania have seen development and release of short duration variety ICPL 87091 (released as Komboa in 1999); long duration variety ICEAP 00040 (released as Mali in 2002) and medium duration variety ICEAP 00068 (released as Tumia in 2003) (Kimani 2001 and Shiferaw et al 2005). Through the screening program for fusarium resistance initiated by ICRISAT in collaboration with partners Tanzanian, fusarium-resistant improved pigeonpea (FRIP) variety (ICEAP 00053), which embodies farmer and market-preferred traits was released for dissemination to farmers. Nonetheless, these research efforts do not seem to have produced desired adoption outcomes among the farming communities. Farmers still grow low-yielding, late-maturing landraces that take up to 11 months to mature in the field, while improved varieties are less common (Kimani, 2001 and Mergeai, 2001). For instance, the study by Shiferaw et al. (2005) in Babati, the main producing district, reported that while over 80 percent of pigeonpea farmers grew local varieties, only 32 percent of the farmers grew improved varieties. This paper explores some key impediments to the adoption of improved varieties and potential for scaling up the adoption of such varieties.
The table below (Table 1) provides descriptive statistics of the variables used in the model. From Table 1 it can be noted that the adoption of technology (both mechanization and improved seed) in Chókwè district is mainly associated with the farm size and market orientation. It is worth mentioning that there were included in the sample small and large holder farmers. Farmers who do not adopt any of the two technologies cultivate in average less than 2 hectares of land, while the adopters on the other hand cultivate an average of 16 hectares. These results are consistent with those of the CGAP report (2016). According to Mozambique Agricultural Development Strategy – PEDSA (2012) smallholder farmers in Mozambique represent more than 80% of the farming population and, in general practice rain- fed agriculture and use traditional varieties of crops and low-intensity fertilizer. Farming is mainly done without mechanization and productivity of the land is very low and the produce is for personal and family subsistence. On the other hand, farmers with larger plots are market-oriented and their produce is for commercial purposes.
Geografia e Informatica (INEGI), Mexico´s national institute of statistics. Although the most recent survey that has been carried out was for 1998, the micro data for this survey has not yet been made available to the public, so that the 1994 and 1996 surveys are the most recent surveys that have been published by INEGI. These surveys are directly comparable since they follow the same methodology, using the same conceptual framework, reference period, and sample design. The 1994 survey has 12,815 observations while the 1996
Furthermore, the dependent variable, Entrepreneurial Intention (EI), was modified and replaced with IT Adoption (IA), which was derived from the work of Venkatesh et al. (2003). As used in this study, IT adoption refers to the actual usage of a particular entrepreneurial technology (system) in business by a farm-based enterprise for entrepreneurial development (Higon, 2011; Saleh & Burgess, 2009). The use of a personal computer to make up a grocery sales invoice and the use of a website to place an order for raw beef and cows milk from a Dutch farm in Holland are good examples of IT use in agribusiness.