technology adoption

This research aligns with EHR technology adoption theories; however, the findings concerning the assignment of E/M codes were still surprising. 21-27 Even in organizations using EHR technology to document patient care, more organizations report the clinician assigning the codes than the EHR assigning the codes. Regardless of the technology used for documentation, a higher percentage of the respondents reported the clinician assigning the codes than reported using technology to assign the E/M codes. This was unexpected given that the improved accuracy for coding E/M services was highly ranked as a desired characteristic by physician medical groups in survey results reported by Gans and colleagues in 2005. 28 Other barriers to HIT adoption such as increased work and decreased productivity are not considered in this situation since additional work on the part of clinicians is not needed for the implementation of

Finally, variables of household characteristics and subjective perceptions are included. We control for the number of household members to account for household size, the age of the household head, the household head’s num- ber of school years, and two dummy variables for whether a household head is from a scheduled or backward caste, mainly because previous research has shown that these variables affect technology adoption (Bandiera and Ra- sul, 2006; Gin´ e and Yang, 2009; Koundouri et al., 2006). Aside from these household characteristics, perceptional measures are added to some of our model specifications. Adesina and Zinnah (1993), for example, show that farmers’ perceptions of technology specific attributes are critical for under- standing adoption decisions of rice varieties in Sierra Leone. This is in line with psychological research, which also attests to the importance of atti- tudes towards technologies (Morris and Venkatesh, 2000). We hence control for lighting satisfaction and perceived benefits from solar power. These data come specifically from survey questions on (i) how satisfied the household is with its current lighting situation (5-point scale) and (ii) whether (or not) a household agrees to the statement that solar power can decrease monthly lighting expenditures (0-1 measure).

(1996) have succinctly put this argument as follows: ‘To the extent that the adoption and accumulation of technologies is important for convergence, the empirical convergence literature is misguided’ (p. 1037). As acknowledged by Abramovitz (1986), technological progress is driven not only by indigenous innovation but also by the process of technology absorption, and thus the ability of a regional economy to ‘catch - up’ may substantially depend on its capacity to imitate and adopt innovations developed in more technologically advanced regions. Although some attempts have been made to capture the impact of technology adoption (e.g. de la Fuente, 2000; Rogers, 2004) nevertheless the existing literature is limited to the extent that it only highlights specific aspects of technology adoption without offering a general model that captures its impacts on regional convergence. It is the purpose of this paper to develop a model capable to provide an appropriate framework to analyse some implications of technology adoption in the process of regional convergence.

The adoption of technologies is an engine of economic progress. Even in the more developed economies, the resources devoted to technology adoption are substantial relative to those to technology-creating activities or research [see Jovanovic (1995)]. Di¤erent patterns of technology adoption are also invoked as part of the explanation for observed disparities in economic performances. These include economic inequalities across countries, as well as across workers [Doms et al. (1997), Bartel and Litchenberg (1987), Parente and Prescott (1994)]. Broadly viewed, decisions in many spheres of life involve elements that resemble the choice of adopting new technologies. Examples include a government considering to push ahead with policy reforms, or the decision to change a job or career by a worker. Therefore, the study of the factors that shape the patterns of the technology adoption choices is important to understand a variety of interesting choice problems.

Di dalam era globalisasi, Technology Adoption membantu dari segi meningkatkan prestasi organisasi. Teknologi telah digunakan oleh syarikat yang besar tetapi untuk Industri Kecil Sederhana (IKS) , penggunaan teknologi masih lagi di peringkat awal. Kajian ini bertujuan untuk mencari teknologi yang sesuai yang boleh digunakan untuk meningkatkan prestasi pemasaran syarikat, faktor-faktor penting apabila mengguna-pakai teknologi dan mengenal pasti kesan penggunaan teknologi dalam pemasaran. Penyelidikan kuantitatif-penerokaan telah dipilih untuk mengkaji kajian ini. Persampelan rawak 125 IKS daripada pelbagai industri telah dipilih sebagai responden kajian ini. Sosial Teknologi Media telah menunjukkan bahawa ia memberi kesan yang tinggi terhadap prestasi pemasaran IKS.

The model analyzes the relationship between financial innovation and technology adoption from the point of view of risk-sharing arrangements. There are other dimen- sions of technology that link financial arrangements to technology adoption. One ex- ample is asymmetric information. To the extent that technology forces shareholders to delegate on a manager the ability to run their firm given his greater expertise, contracts must be designed to convey adequate incentives to the manager. The literature on cor- porate finance addresses precisely the properties of such contracts. Yet another example is the presence of indivisibilities (investments that require the commitment of capital for long periods of time), as they require the matching of the different liquidity requirements of investors and savers over time. We next discuss two historical episodes where we be- lieve the relationship between financial innovation and growth suggested by our model is transparent. The Industrial Revolution is an example of the indivisibility/liquidity case, whereas the Chicago Board of Trade example is an instance of price risk.

In this paper, we model the farm household’s choice of improved inputs (certified seed and fertilizer) in the risky environment following a framework similar to that by Koundouri, Nauges, & Tzouvelekas (2006). This framework assumes that technology choice by farmers is influenced by the distribution of risky agricultural output. The output distribution in this model is represented by its first and higher-order central moments (Antle, 1983, 1987; Antle & Goodger, 1984). The approach adopted could be seen as an extension of that by Koundouri et al. (2006) in various aspects. First, we consider a multi-output framework, while these authors modeled the production risk for a single output. Our setting is preferred since farm households in developing countries are generally involved in several crop farming. Therefore, we assume that farmers decide to adopt technologies to maximize their overall farming returns. In addition, crop diversification is a risk management strategy for farmers (Di Falco & Chavas, 2009). Moreover, Antle (1987) and Kim & Chavas (2003) argue that strong assumptions are required to estimate any behavioral equation-based single farming activity. Second, we extended the single technology adoption to multiple technology adoption (two in this case). A similar approach was also adopted by Ogada et al. (2014) who studied the adoption of maize improved variety and inorganic fertilizer in Kenya. Third, we follow the risk-value model that is more general than the prospect theory or expected utility-based models. The latter are special cases of the risk-value model (Antle, 2010). This model assumes that the behavior of decision-makers is not the same in presence of negative or positive outcomes.

his paper studies technology adoption in a duopoly where the unbiased technological change improves production efficiency. Technological progress is exogenous and modeled as a jump process with a drift. There is always a Markov perfect equilibrium in which the firm with more efficient technology never preempts its rival. Also, a class of equilibria may exist that lead to a smaller industry surplus. In these equilibria either of the firms may preempt its rival in a set of technology efficiency values. The first investment does not necessarily happen at the boundary of this set due to the discrete nature of the technology progress. The set shrinks and eventually disappears when the difference between firms’ efficiencies increases.

In 1957, Griliches concluded that economic variables were the major determinants of technological change and adoption of innovations. In 1961, Mansfield also came to the conclusion that the adoption of innovations was determined by economics. The influence of economic thought on the adoption of innovations led Just and Zilberman (1983) to propose a theory of technology adoption under uncertainty using the expected utility framework. This model is an extension of the original Baron-Sandmo (1971) expected utility approach to producer behavior under uncertainty (Marra et al. 2003). This model contends that economic constraints, such as access to capital or land, significantly affect the adoption decision. Thus, the decisions of the farmer are derived from the maximization of expected utility (or profit) subject to his inputs (land availability, labor, credit, etc.). The expected utility model is the most commonly used model for adoption studies of agriculture and agroforestry technologies (for examples of studies using this model see Mercer and Pattanayak 2003; Negatu and Parikh 1999; Ayuk 1997; Baidu-Forson 1994 and 1999).

Research addressing this development includes a more recent attempt to unify eight of the more prominent models used to describe information technology adoption and diffusion. This resulted in what is called the Unified Theory of Acceptance and Use of Technology (UTAUT) model [47]. Theoretical models used in the formulation of this new model include the TRA, TAM and DOI mod- els, along with the motivational model, a model combin- ing TPB with TAM, the model of PC utilization and the social cognitive theory [47]. It is important to note that many of these theories have their roots in sociological studies dating back to the early 1960s.

Financial markets' imperfections and technology adoption Katrin Tinn The London School of ECOllOllllC i and Political Scicllce A thesis sUbluit t ('d in part ial fulfihnent of the requireIl1Pllh of th[.]

This study analyzed the irrigation technology adoption in in Kwara State, Nigeria. A multistage sampling procedure was employed for the selection of respondents for the study. The first stage involved the purposive sampling of Oke-Oyi and Songa irrigation scheme. The second stage involved random sampling of villages and communities where farmers that were involved in the irrigation scheme are located. The third stage involved the random and representative selection of irrigation farmers (treatment) and non-irrigation farmers (control). Farm-level and household-level primary data were obtained with the use of well-structured questionnaire and interview schedule from 348 respondents, from villages and communities covering a total of five (5) Local Government Areas. Data were subjected to descriptive statistics and two sampled t-test. About fifty six percent adopted the selected irrigation technology, while 44.25% did not adopt it. The adopter were more educated than the non-adopters, and they also have bigger farm sizes of okra, pepper, maize and sorghum. It was concluded that, education was vital to the adoption of irrigation technology. Adopters of

Park and Ertmer (2008) who found, in their study of the barriers that middle- school teachers faced when implementing technology-enhanced problem-based learning, that one of the differences between typical and expert teachers was collaboration with other teachers. These conclusions also support the research reported by Redmann and Kotrlik (2004) in which technology adoption was related to barriers to technology integration and technology anxiety; however, technology availability did not contribute to the explanation of variance in technology adoption in their study. These conclusions partially support the research by Smerdon, et al. (2000), in which they found that the major issues in integrating technology into instruction included access to technology and barriers to the integration of technology.

This study utilized econometric models to analyze the determinants of productivity changes. An empirical association of technology adoption and consequent changes in productivity may be identified by measuring productivity changes. If there is a difference, we find that the application of the particular technology is associated with changes in productivity or efficiency (i.e., the level of efficiency changed by technology applications). We considered serial correlations because the dependent variable in econometric models is measured using DEA. When the productivities are measured by DEA in the first step and regressed on explanatory variables in the second step, the productivity measures calculated by DEA are likely to be serially correlated (Simar and Wilson, 2007). Guan and Oude Lansink (2006) suggest the use of a dynamic generalized method of moments (GMM) model with a two-year lag to analyze TFP measured by DEA to eliminate the serial correlation problem 3 .

Adam Copeland and Adam Shapiro Impact of Competition on Technology Adoption: An Apples-to-PCs Analysis.[r]

While TPB and NAT have been successfully applied in the pro-environmental area, integrating the two theories into a theoretical framework increases the need for building relationships among the variables from TPB and NAT. Therefore, we planned to integrate the TPB and NAT to explain the formation of an organizational decision maker’s intention to adopt Green IT in the organization, rather than using the well-established individual technology adoption models, such as technology acceptance model (TAM) and diffusion of innovation (DoI) theory or unified theory of acceptance and use of technology (UTAUT), since Green IT is different from traditional IT. Individual technology adoption theories such as TAM, UTAUT and DOI are generalized theories which are not precisely designed for explaining an explicit type of technology. They supposed that the adoption of IT is based on the evaluation of cost- benefit. For instance, in TAM, a person’s intention to adopt a specific technology is affected by the perceived usefulness (benefit) and the ease of use (cost) of adopting the technology (Davis, 1989). Green IT is not only a group of technologies, but also a set of pro-environmental practices. Meanwhile the pro-environmental practices implementation include value judgments, and it is not best predicted by cost-benefit evaluation (Minton and Rose, 1997). Well-established individual technology adoption models are not the best applicant to clarify the Green IT adoption in organizations. We believe that NAM and TPB will be a better theoretical model for explaining the organizational Green IT adoption.

Eminent authors have been illustrated the main obstacles that lead to weaken the process of technology adoption. For instance, Brinkerhoff (2006) illustrates that teachers often are not able to build on technology‟s instructional potential. This matter of fact relates to barriers such as institutional and administrative support, training and experience, attitudinal or personality factors, and resources as well. Thus, these obstacles are defined as ". . . any factor that prevents or restricts teachers‟ use of technology in the classroom". In relation to this issue, the British Educational Communications and Technology Agency (BECTA, 2003, 1) states that teacher-level barriers consist of the following factors: lack of time, lack of necessary knowledge, as well as the lack of self-confidence in using technology. However, barriers that surround the administrative level are the lack of: technical support, access to equipment, availability of up to-date software, and institutional support too. On the first hand, BECTA ,2003, (Redmann and Kotrlik, 2004, and Mumtaz, 2000)clarify that technology unavailability comes to be marked as an important element deterring the use of technology by teachers. On the second hand, (Park andErtmer,2008) adds that". . . a lack of a clear, shared vision was the primary barrier. Hence, other barriers may include the lack of sufficient knowledge and skills, unclear expectations, and insufficient feedback".

Results in columns (1) and (4) of Table 1 report destination market size elasticities of extensive import margins for the full sample of 35 reporter-countries, implicitly assuming that fixed costs of trade and adoption restrict import countries uniformly. Once there are additional potential restrictions on import margins, this need not be the case. Specifically, Frensch and Gaucaite Wittich (2009) rather underline labour force skills to constrain new technology adoption in the form of increasing capital goods imports along the extensive margin. Their measures of labour force skills are length of education data from the Barro and Lee dataset (Barro and Lee, 2000), available only at five-year intervals, which does not go well with my yearly panel data. As an alternative way of testing whether particularly small countries are actively constrained by market size – rather than by labour force skills or other potential constraints –, I add small market size dummies, interacted with market size, to my regression equation, thus explicitly allowing for different destination market size elasticities of extensive import margins for very small versus larger markets. The first small size dummy, SmallGDP1, equals one for the eight smallest markets in my sample (i.e., for Malta, Iceland, Albania, Macedonia, Estonia, Cyprus, Latvia, Lithuania), the second, SmallGDP2, is reserved to the four smallest markets (Malta, Iceland, Albania, Macedonia).

ICRISAT carries out research with its partners in the NARES to develop technological innovations in the areas of genetic enhancement and natural resources management. Pilot studies carried out earlier have clearly established linkages between social capital and technology adoption, and their impact on outcomes, especially in terms of poverty reduction. Based on a pilot study, the ICRISAT GPT has been selected as the focus technology for this study. The GPT was specifically developed for cultivation of groundnuts in dry areas, especially to promote cultivation in summer using an improved package of practices, including improved cultivars, and soil, water, and nutrient management options. Appendix 2 provides a history of GPT. Findings from the pilot survey indicated that the GPT would be a good case to understand the technology adoption–poverty alleviation linkage, and at the same time assist researchers to explore the role of intervening factors in the adoption and impact of complex technologies in difficult environments. In view of the earlier gender analysis conducted on this technology, it was also concurred that GPT would provide a good case of the gender implications and impacts of a NRM technology.

Mindfulness is an important emerging concept in society. This research posits that the user’s mindful state when adopting a technology is a crucial factor that determines how the technology will fit the task context at the post-adoption stage and thus has profound influence on user adoption and continued use of technology. Based on the mindfulness literature, this research conceives of a new concept, namely mindfulness of technology adoption (MTA), as a multi- faceted reflective high-order factor. An MTA-TTF (task-technology fit) framework is then developed and integrated into the Cognitive Change Model to develop a research model that delineates the mechanisms through which MTA influences user adoption and continued use of technology. The model was examined by a longitudinal study of students’ use of wiki systems. The results suggest that a mindful adopter is more likely to perceive a technology to be useful and to choose a technology that turns out to fit his/her tasks. Hence, mindful adopters are likely to have high disconfirmation, perceived usefulness, and satisfaction at the post-adoption stage. The findings have significant implications for IS research and practices.