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The purpose of this paper is to measure technical efficiency of Indian thermal power sector employing the recent by-production approach.

The by-production approach is used in conjunction with data from the Central Electricity Authority (CEA) of India to compute the output-based Färe, Grosskopf, Lovell (FGL) efficiency index and its decomposition into productive and environmental efficiency indexes for the ITPPs

The authors show that given the aggregated nature of data on coal reported by CEA, CEA’s computation of CO₂ emissions through a deterministic linear formula that does not distinguish between different coal types and the tiny share of oil in coal-based power plants, the computed output-based environmental efficiency indexes are no longer informative. Meaningful measurement of environmental efficiency using CEA data is possible only along the dimension of the coal input. Productive efficiency is positively associated with the engineering concept of thermodynamic/energy efficiency and is also high for power plants with high operating availabilities reflecting better management and O&M practices. Both these factors are high for private and centrally owned as opposed to state-owned power-generating companies. The example of Sipat demonstrates the importance of (ultra)supercritical technologies in increasing productive and thermodynamic efficiencies of the ITPPs, while also reducing CO₂ emitted per unit of the net electricity generated.

This paper uses the by-production approach for the first time to measure technical efficiency of ITPPs and highlights how the nature of the Indian data impacts on efficiency measurement.

Briefly reviews previous literature by the author before presenting an original 12 step system integration protocol designed to ensure the success of companies or countries in their efforts to develop and market new products. Looks at the issues from different strategic levels such as corporate, international, military and economic. Presents 31 case studies, including the success of Japan in microchips to the failure of Xerox to sell its invention of the Alto personal computer 3 years before Apple: from the success in DNA and Superconductor research to the success of Sunbeam in inventing and marketing food processors: and from the daring invention and production of atomic energy for survival to the successes of sewing machine inventor Howe in co‐operating on patents to compete in markets. Includes 306 questions and answers in order to qualify concepts introduced.

To answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?

This paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.

Results show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.

The findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.

This paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.

The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables.

A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.

The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots.

The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education.

T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.

This study examines the effect of foreign direct investment (FDI) on employment and economic growth in Ghana and examines the role of technology in these relationships.

This study applied the autoregressive distributed lag (ARDL) bounds testing approach to cointegration and Granger causality tests to data from 1995 to 2017.

Based on the empirical analysis, the key findings are as follows: FDI does not affect economic growth or employment in Ghana. However, technology moderates the relationship between FDI and economic growth and FDI and employment in the short run. The study also finds that technology exerts a positive effect on economic growth in both short and long run, whereas trade has a significantly negative effect on economic growth in Ghana.

The greatest constraint that faced the authors is the nonavailability of data,.

The transfer of technology agreement enshrined in the GIPC Act should be made more robust and unambiguous, to make it a strict requirement for MNEs to be allowed to operate in Ghana. This increases Ghana's gains from FDI inflow.

The GIPC should tighten its monitoring regime so that MNEs do not exceed their expatriate employment quotas. This will ease the burden of unemployment among the youth in Ghana.

This study adds a new dimension to the literature on the impact of FDI on emerging economies by examining the role of technology in the association between FDI and growth, and FDI and employment.

This study examines the differences in the economic stimulus effects, transmission mechanisms, and output multipliers of government consumption, government traditional investment, and government science and technology investment.

This study constructs and estimates a New Keynesian model of endogenous technological progress embedded in the research and development (R&D) and technology transfer sectors. Using Chinese macroeconomic time series data from 1996 to 2019, this study calibrates and estimates the model and analyzes the impulse response function and a counterfactual simulation of expenditure structure adjustment.

The results show that compared with the traditional dynamic stochastic general equilibrium (DSGE) model, the endogenous process of technological progress amplifies the impact of government consumption shock and traditional government investment shock on the macroeconomy, leading to greater economic cycle fluctuations. As government investment in science and technology has positive external spillover effects on firm R&D activities and the application of innovation achievements, it can promote more sustainable economic growth than government consumption and traditional investment in the long run.

This study constructs an extended New Keynesian model with different types of government spending, which includes endogenous technological progress within the R&D and technology transfer sectors, thereby linking fiscal policy, business cycle fluctuations and long-term economic growth. This model can study the macroeconomic impact of fiscal expenditure structure adjustment when fiscal expansion is limited. In the Bayesian estimation of model parameters, this study not only uses macroeconomic variables but also adds a sequence of private R&D investment.

The aim of this paper is to study the factors affecting the customer’s “Intention to Adopt TFS” (I-TFS), and a conceptual model has been proposed, while most previous studies have focused on the study of self-service technology (SST). Interactions between customer and service provider during delivery of a service is termed as “service-encounter”. The technology that enables service delivery without customer having a face-to-face service-encounter is known as “self-service technology” (SST). Froehle and Roth described five different ways in which technology can be used in service-encounter. One of the ways, known as technology-facilitated service (TFS), requires the simultaneous existence of three entities – customer, technology and service provider – during a service-encounter. Unlike SST, in TFS customer, technology and service provider must co-exist for the completion of the service.

The factors affecting I-TFS can be divided in two categories: human – technology interaction (H-TI) and human–human interaction (H-HI). Although, existing literature has dealt with factors related to H-T I, e.g. “ease-of-use” and “perceived-usefulness”, the author tries to draw attention to H-H I variables, e.g. “facilitating-conditions”, which are potentially significant but have remained fairly untouched. For the study, participants were drawn from a target market where a TFS was operational. A scientifically developed survey was used to collect data from 222 participants. Structural equation modelling (SEM) was used to analyze the conceptual model.

The results strongly suggest that while H-T I factors are important, H-H I factors are equally critical during service delivery. H-H I factors become especially more relevant than H-T I in developing countries.

The study strongly suggests that attitude towards the human element, i.e. service provider/front-line employee is an important factor that impacts the customer’s I-TFS. In the context of emerging economies where organisations provide innovative technology services to suit the needs of the respective populations human representatives are a must to support the service. We conducted this research within one TFS context. Additional studies with more diverse TFS with other consumer groups should be conducted to provide additional support and increase the generalisation of both the research framework and findings.

The findings of the study are useful to all those firms that are considering the implementation of other TFS such as tele-medicine or distance education programmes. By investigating the main causal variables that have an impact of adoption of TFS, we provide an actionable set of factors to help firms understand and influence TFS adoption behaviour.

Research on factors affecting adoption of services has traditionally focused either on interpersonal interactions between customers and service providers (H-HI) or non-interpersonal interactions of customer with technology (H-TI). However, very few have studied dimensions of H-HI and H-TI together to understand their impact on customer’s adoption of a service. Considering the need for more research, this study examines the relationships between H-HI, H-TI and their simultaneous impact on consumer adoption of services.

The author argues that we must stop and take a look at what our insistence on human labour as the basis of our society is doing to us, and begin to search for possible alternatives. We need the vision and the courage to aim for the highest level of technology attainable for the widest possible use in both industry and services. We need financial arrangements that will encourage people to invent themselves out of work. Our goal, the article argues, must be the reduction of human labour to the greatest extent possible, to free people for more enjoyable, creative, human activities.

The purpose of the paper is to present an integrated approach concerning intertemporal choices and the location of economic activity under a simple endogenous growth model. The idea is that time analysis concerning the choices about present and future consumption and the choices on the allocation of scientific resources should be combined with a space analysis regarding the dissemination of economic activity through geographical locations.

Two optimal control problems are considered. These relate to a standard utility maximization set‐up, in which spatial considerations are incorporated, and to a problem of allocation of scientific/technological resources. Steady states and transitional dynamics are addressed.

It was found that the long‐run steady state does not have to be a state of unchangeable geography – consumption, production conditions and technological progress determine not only long‐term growth but also the long‐term tendency for the economy to geographically concentrate or disperse.

In its essence, the model is just a conventional Ramsey growth model, sophisticated in order to include endogenous location decisions and endogenous technology choices. Further insights can be gained by readdressing the model (e.g. by assuming a monopolistic competition environment instead of a purely competitive set‐up).

The determinants of growth are, on the one hand, the decisions about how to allocate technological resources and, on the other hand, the strength with which productive activities can agglomerate in order to generate increasing returns to scale.