Innovation Diffusion Model Research Articles

Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis

Hydroelectric power is one of the most important renewable energy sources in the world. It currently generates more electricity than all other renewable technologies combined and, according to the International Energy Agency, it is expected to remain the world’s largest source of renewable electricity generation into the 2030s. Thus, despite the increasing focus on more recent energy technologies, such as solar and wind power, it will continue to play a critical role in energy transition. The management of hydropower plants and future planning should be ensured through careful planning based on the suitable forecasting of the future of this energy source. Starting from these considerations, in this paper, we examine the evolution of hydropower with a forecasting analysis for a selected group of countries. We analyze the time-series data of hydropower generation from 1965 to 2023 and apply Innovation Diffusion Models, as well as other models such as Prophet and ARIMA, for comparison. The models are evaluated for different geographical regions, namely the North, South, and Central American countries, the European countries, and the Middle East with Asian countries, to determine their effectiveness in predicting trends in hydropower generation. The models’ accuracy is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Through this analysis, we find that, on average, the GGM outperforms the Prophet and ARIMA models, and is more accurate than the Bass model. This study underscores the critical role of precise forecasting in energy planning and suggests further research to validate these results and explore other factors influencing the future of hydroelectric generation.

The econophysical model of innovation diffusion

Analysis and evaluation of innovation efficiency require the development of tools to model their dissemination process within the industry. This paper presents a model of innovation diffusion based on physical approaches, describing stages of accelerating and decelerating growth. An exponential growth is described using a diffusion model, while a logarithmic one employs an electrical engineering model. The paper presents the correspondence of physical parameters with their economic counterpart: size of a company; characteristic of speed of information exchange between firms; company’s willingness to innovate; inter-firm influence and the breakthrough level of innovation. The theoretical model obtained was tested on historical data of innovation implementation in the fuel and energy complex, followed by adjustments of coefficients depending on the branch of innovation implementation. The developed model is applicable for describing the process of innovation dissemination in any industry in the country, as well as for investment and business planning in companies and decision-making on investments in innovation projects. When applied in industries with low levels of innovation activity, an increase in the level of high-tech production and the share of organizations implementing technological innovations is predicted. Using the example of Russia’s fuel and energy sector, rising in the technological level of enterprises and a decrease in import dependence are forecasted.

Forecasting of changes in electricity consumption due to EV diffusion in South Korea: Development of integrated model considering diffusion and macro-econometric model

Electrification is on the increase globally. The transportation sector is being electrified to reduce greenhouse gas emissions. South Korea aims to diffuse 8.3 million battery electric vehicles (BEVs) by 2040 to reduce emissions in the transportation sector. However, BEV diffusion policies have not been considered for BEVs' electricity consumption. Given that the rapid diffusion of BEVs significantly increases the electricity demand, forecasting BEVs' electricity consumption is necessary to inform electricity production plans. Furthermore, electricity is produced through various energy sources, and electricity prices and consumption are affected; therefore, forecasts that reflect the overall energy market are needed. This study presents a forecasting model for BEV demand and energy consumption by combining it with a macroeconometric model that reflects the overall energy market and socioeconomic impact using an innovation diffusion model. Incorporating electricity prices and renewable energy consumption derived from the macroeconometric model, annual BEV demand and electricity consumption are predicted. Moreover, BEV demand is more diffused and slower when forecasted using the Generalized Bass model with electricity prices and renewable energy consumption, compared to forecast without these factors, and the predictive power of BEVs is superior to that forecasted using the Bass Model alone.

Achieving the United Nations sustainable development goals – innovation diffusion and business model innovations

Purpose The study aims to analyse the race towards green development and United Nations sustainable development goals (SDGs) in the cases of Huawei and Shell. Both companies are the leaders in their respective industries. Huawei is an example case study representing the information and communications technology (ICT) industry whereas Shell is an example case study representing the oil and gas industry. The research analyses of the races in achieving UN SDGs were undertaken based on the innovation diffusion framework with the use of machine learning algorithms trained to extract data on sustainability activities and initiatives. Design/methodology/approach The research analyses the two case studies of Huawei and Shell. The research was undertaken through the steps of training machine learning algorithms, industry benchmarking and evaluating the performance of the race. The analyses regarding the activities and initiatives of Huawei and Shell in contributing towards SDGs are based on the data in the past 10 years (Years 2010–2019) using machine learning to extract data on sustainability activities and initiatives. In the case of Huawei, 313 sustainability reports were fed to the unsupervised machine learning algorithms revealing 15,101 sustainability actions and initiatives related to UN SDGs in the ICT industry. In the case of Shell, 2,015 sustainability reports were fed to the unsupervised machine learning algorithms revealing 47,365 sustainability actions and initiatives related to UN SDGs in the oil and gas industry. Findings The analyses of findings revealed that Huawei and Shell performed very well in progressing towards the UN SDGs. Huawei had strong performance in the ICT industry with regard to SDGs No. 3, 4, 7, 8, 11, 12 and 16 while Shell had strong performance in the oil and gas industry with regard to SDGs No. 3, 4, 6, 7, 8, 12 and 16. Both companies had placed a focus on achieving SDG 12 responsible consumption and production, SDG 7 affordable and clean energy and SDG 4 quality education. The synthesised business model innovations of Huawei and Shell had shown their environmental, social and governance strategies – Huawei’s 2030 vision for green development and Shell’s 2050 vision for net zero emissions. Practical implications The five pillars of people, planet, prosperity, peace and partnership according to the UN 2030 agenda for sustainable development have shown the way a company operates to promote sustainable eco-systems. The extent to which both Huawei and Shell link corporate strategies to the UN SDGs has reflected their implementation progress. Furthermore, the business model innovations of Huawei and Shell provides a useful framework which can be applied to encourage other companies/organisations in various industries to undertake ESG activities in practice. Originality/value The main contribution of this research is the application of machine learning algorithms and the innovation diffusion model in analysing the SDGs performance. The study applies the innovation diffusion framework to explore strategic actions and initiatives of Huawei and Shell in transitioning towards sustainability. The use of machine learning algorithms has identified their sustainability approach in achieving the UN SDGs.

Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design.

Traditional computational methods for antibody design involved random mutagenesis followed by energy function assessment for candidate selection. Recently, diffusion models have garnered considerable attention as cutting-edge generative models, lauded for their remarkable performance. However, these methods often focus solely on the backbone or sequence, resulting in the incomplete depiction of the overall structure and necessitating additional techniques to predict the missing component. This study presents Antibody-SGM, an innovative joint structure-sequence diffusion model that addresses the limitations of existing protein backbone generation models. Unlike previous models, Antibody-SGM successfully integrates sequence-specific attributes and functional properties into the generation process. Our methodology generates full-atom native-like antibody heavy chains by refining the generation to create valid pairs of sequences and structures, starting with random sequences and structural properties. The versatility of our method is demonstrated through various applications, including the design of full-atom antibodies, antigen-specific CDR design, antibody heavy chains optimization, validation with Alphafold3, and the identification of crucial antibody sequences and structural features. Antibody-SGM also optimizes protein function through active inpainting learning, allowing simultaneous sequence and structure optimization. These improvements demonstrate the promise of our strategy for protein engineering and significantly increase the power of protein design models.

VCC-DiffNet: Visual Conditional Control Diffusion Network for Remote Sensing Image Captioning

Pioneering remote sensing image captioning (RSIC) works use autoregressive decoding for fluent and coherent sentences but suffer from high latency and high computation costs. In contrast, non-autoregressive approaches improve inference speed by predicting multiple tokens simultaneously, though at the cost of performance due to a lack of sequential dependencies. Recently, diffusion model-based non-autoregressive decoding has shown promise in natural image captioning with iterative refinement, but its effectiveness is limited by the intrinsic characteristics of remote sensing images, which complicate robust input construction and affect the description accuracy. To overcome these challenges, we propose an innovative diffusion model for RSIC, named the Visual Conditional Control Diffusion Network (VCC-DiffNet). Specifically, we propose a Refined Multi-scale Feature Extraction (RMFE) module to extract the discernible visual context features of RSIs as input of the diffusion model-based non-autoregressive decoder to conditionally control a multi-step denoising process. Furthermore, we propose an Interactive Enhanced Decoder (IE-Decoder) utilizing dual image–description interactions to generate descriptions finely aligned with the image content. Experiments conducted on four representative RSIC datasets demonstrate that our non-autoregressive VCC-DiffNet performs comparably to, or even better than, popular autoregressive baselines in classic metrics, achieving around an 8.22× speedup in Sydney-Captions, an 11.61× speedup in UCM-Captions, a 15.20× speedup in RSICD, and an 8.13× speedup in NWPU-Captions.

A qualitative approach to assess the opinion of physicians about the challenges and prospects of pharmacogenomic testing implementation in clinical practice in Greece

BackgroundPharmacogenomics (PGx) constitutes an important part of personalized medicine and has several clinical applications. PGx role in clinical practice is known, however, it has not been widely adopted yet. In this study, we aim to investigate the perspectives of Greek physicians regarding the implementation of PGx testing in clinical practice and the key issues associated with it.MethodsFourteen interviews were conducted with physicians of various specialties for which PGx applications are available. A semi-structured interview guide was utilized based on the Consolidated Framework for Implementation Research (CFIR) context and the Diffusion of Innovation model. Transcripts were coded independently and compared by two members of the research team. Descriptive statistics were generated using Microsoft Excel.ResultsSix main themes emerged: awareness and use of PGx testing; source of information; key stakeholders of the PGx supply chain, their interactions and change agents; clinical benefit and significance of PGx testing; barriers and lack of reimbursement; and recommendations to boost the PGx adoption rate. Most respondents were aware of PGx applications, but only three had already recommended PGx testing. Peer-reviewed journals along with clinical guidelines were regarded as the most used source of information while stakeholders of the PGx supply chain were discussed. PGx was considered that promote patient-centered care, enhance medication clinical effectiveness, decrease the risk of side effects, and reduce healthcare costs. Lack of reimbursement, scarcity of resources, and high PGx cost were the foremost barriers affecting PGx adoption.ConclusionsIt was concluded that if case PGx testing is reimbursed and physicians’ training is reinforced, PGx implementation will be boosted and improved shortly.

Decision making for people living with HIV/AIDS using the theory of innovation diffusion model at the Western Provident Association Turen Foundation.

To describe how people living with HIV/AIDS (PLWHA) make decisions using the diffusion of innovation theory model. Decisions occur when individual decision makers engage in activities that guide choices to adopt or reject a particular innovation. This is a descriptive analysis research using a survey method. Data collection was carried out using a decision making questionnaire. The subjects in this research were HIV/AIDS sufferers (PLWHA) who lived in Turen, Indonesia. The number of research subjects was 36 respondents obtained using the purposive sampling technique on January 2023. The research design used the correlation method with a cross sectional approach and the Spearman correlation coefficient statistical test. The research results show significance (2-tailed) of 0.934 (p>0.05). The correlation coefficient results are negative. Where the direction of the correlation produces showed a very weak relationship with a value of 0.014 and the results of the analysis between variables are not the same. This is because the persuasion stage was not tested in the analysis. Research shows that 8 (22.2%) patients rejected the decision making of PLWHA using the diffusion of innovation theory model at the Western Provident Association Turen Foundation, Turen, Indonesia, while 28 (77.7%) patients accepted it. It can be concluded that the majority of decisions made by HIV/AIDS patients at the ADIS Turen Peduli Warga Foundation are accepted. Knowledge of HIV/AIDS sufferers is at a good level, their decision making is mostly accepted and there is a meaningful relationship between knowledge and decision making in using a chatbot innovation. The suggestion from the research is that this chatbot innovation can be a source of further research and help provide education for PLWHA patients in everyday life.

Innovations for Building Implementation Science Capacity among Researchers and Policymakers: The Depth and Diffusion Model

BackgroundWhile many interventions have reduced transmission of HIV or increased access to HIV care, fewer have been scaled-up or sustained. Implementation science (IS) maximizes the population-level impact of interventions, but globally, knowledge about IS among researchers and policymakers is limited, particularly in low and middle-income countries. This gap can be addressed through an innovative IS training model, the Depth and Diffusion Model, that can be used to build implementation science capacity to increase equity in the distribution of global implementation science training programs.MethodsIn the Depth and Diffusion Model, depth creates an in-country institution as a leader in IS research and training; diffusion ensures the spread of fundamental IS knowledge across universities and governmental agencies throughout the country. We are using the Depth and Diffusion Model in the Vietnam Implementation Science Advancement Program (VISA), a case study of applying this model in practice. VISA’s goals are to: (1) initiate the Hanoi Medical University Implementation Science Program; (2) develop IS knowledge and research capacity among four universities in Vietnam; and (3) cultivate IS capacity among Vietnamese policymakers and health program leaders.DiscussionThe Depth and Diffusion Model is being used to efficiently scale-up IS knowledge among researchers and policymakers, while fostering collaboration to increase impact of research and policies. By the end of VISA, HIV-related IS capacity will be well-established in Vietnam, demonstrating how the Depth and Diffusion Model can be a blueprint for adoption of IS research and practice in a country with limited previous IS capacity.

An innovation diffusion model with two innovations under two simultaneous effects in a competitive market: co-existence through optimal control

An innovation diffusion model with two innovations under two simultaneous effects in a competitive market: co-existence through optimal control

Adopting, implementing and assimilating coproduced health and social care innovations involving structurally vulnerable populations: findings from a longitudinal, multiple case study design in Canada, Scotland and Sweden

BackgroundInnovations in coproduction are shaping public service reform in diverse contexts around the world. Although many innovations are local, others have expanded and evolved over time. We know very little, however, about the process of implementation and evolution of coproduction. The purpose of this study was to explore the adoption, implementation and assimilation of three approaches to the coproduction of public services with structurally vulnerable groups.MethodsWe conducted a 4 year longitudinal multiple case study (2019–2023) of three coproduced public service innovations involving vulnerable populations: ESTHER in Jönköping Region, Sweden involving people with multiple complex needs (Case 1); Making Recovery Real in Dundee, Scotland with people who have serious mental illness (Case 2); and Learning Centres in Manitoba, Canada (Case 3), also involving people with serious mental illness. Data sources included 14 interviews with strategic decision-makers and a document analysis to understand the history and contextual factors relating to each case. Three frameworks informed the case study protocol, semi-structured interview guides, data extraction, deductive coding and analysis: the Consolidated Framework for Implementation Research, the Diffusion of Innovation model and Lozeau’s Compatibility Gaps to understand assimilation.ResultsThe adoption of coproduction involving structurally vulnerable populations was a notable evolution of existing improvement efforts in Cases 1 and 3, while impetus by an external change agency, existing collaborative efforts among community organizations, and the opportunity to inform a new municipal mental health policy sparked adoption in Case 2. In all cases, coproduced innovation centred around a central philosophy that valued lived experience on an equal basis with professional knowledge in coproduction processes. This philosophical orientation offered flexibility and adaptability to local contexts, thereby facilitating implementation when compared with more defined programming. According to the informants, efforts to avoid co-optation risks were successful, resulting in the assimilation of new mindsets and coproduction processes, with examples of how this had led to transformative change.ConclusionsIn exploring innovations in coproduction with structurally vulnerable groups, our findings suggest several additional considerations when applying existing theoretical frameworks. These include the philosophical nature of the innovation, the need to study the evolution of the innovation itself as it emerges over time, greater attention to partnered processes as disruptors to existing power structures and an emphasis on driving transformational change in organizational cultures.

Examining The Acceptance of Tarannum Smart Learning Application Using Diffusion of Innovation Model

This paper presents a study aimed at developing a realistic model for understanding users' intention to use Smart Learning Application (SLA). SLA provides users with an intelligent learning platform that enables effective learning management, user-centered networking of intelligent devices, and rapid learning from any location. However, despite the advantages of SLA, its use is not widely accepted. The study used a quantitative approach, and data was collected from 229 respondents using a survey. SmartPLS (v. 3.2.8) was used to examine the relationships between the variables in the proposed model. The study developed a comprehensive model based on DOI that addressed users' intention to use SLA. The findings indicated that the direct influence of Relative Advantage, Compatibility, and Complexity on users' intention to use Tarannum Smart Learning Application (TSLA) was not significant, and hypotheses H1, H2, and H3 were not supported. However, hypothesis H4, which postulated a significant relationship between Observability and Intention to Use, was supported. The proposed model can be used as a basis for improving the learning of various types of tarannum by teachers teaching the holy Quran. The study can be expanded to other Smart Learning Applications to validate the findings and identify more opportunities. TSLA, by design, promotes human-oriented networking of smart devices and applications, making it a suitable tool for encouraging active users. Longitudinal data may be necessary to investigate the variables that impact people's acceptance level of TSLA. The technical implementation of TSLA from a policy maker's viewpoint can be explored in future studies.

Using Consumer-Generated Social Media Posts to Improve Forecasts of Television Premiere Viewership: Extending Diffusion of Innovation Theory

This research investigated how social media can be quantified and used as an input to improve audience forecasts for television show premieres. There were two key findings. First, Twitter activity (volume of tweets and retweets) drives viewership of shows that are unscripted (i.e., reality or competition shows). From a practical perspective, Twitter activity improved prediction accuracy beyond that of forecasting inputs typically employed by the industry. The second key finding was the interaction of media attention given to a show and audience size of the show leading into (i.e., preceding) the premiere. One practical implication for network scheduling and promotion efforts is to use strong lead-in shows along with large mass media efforts for a television season’s “flagship” series that are most important for a network’s success. This research also extends Diffusion of Innovation theory (Rogers, 2003) and diffusion modeling to television entertainment consumption. Diffusion of Innovation theory predicts the importance of information dispersion across heterophilous groups (i.e., groups that are diverse). The Bass (1969) diffusion model, a statistical model introduced to describe the diffusion of innovation, also predicts that media presence (an external factor) in conjunction with consumer eWOM (an internal factor) drive premiere ratings.

Exploring potential diffusion pathways of biorefinery innovations—An agent‐based simulation approach for facilitating shared value creation

AbstractIn many European countries with plentiful forest resources, novel forest‐based businesses play a key role in the transition from our current fossil‐based economy towards a circular bioeconomy. For example, kraft lignin, a by‐product from the pulping industry, is produced in large amounts globally. To date, however, it is still only offered on the market by a small number of pulping companies. The successful innovation diffusion of related new technologies and businesses requires establishing a collective effort among multiple societal actors to motivate the sharing of value creation processes. In this paper, potential innovation diffusion pathways are modeled and simulated by means of an agent‐based approach (Biorefinery Products Innovation Diffusion model, BioPID). The paper investigates the conditions needed to encourage the diffusion of kraft lignin innovations as a (partial) replacement for fossil‐based feedstock in selected applications. The results reveal the basic mechanisms behind potential innovation diffusion pathways. The major barriers were found to be the high level of uncertainty surrounding the additional costs arising in lignin processing, the small number of lignin providers, and the presence of relatively homogeneous pricing strategies based on opportunity and basic preparation costs. The analysis of two product categories revealed different patterns in terms of innovation diffusion and potential greenhouse gas emissions. A novelty of BioPID is that it allows for iterative technology evaluation and technology foresight analysis of biorefinery projects (e.g., by combining techno‐economic, socio‐technical, and environmental aspects). This produces knowledge for diverse stakeholders involved in the lignin innovation ecosystem, thus enabling better communication on shared values and furthering innovation diffusion.

An improved model of the diffusion of Bass innovations for predicting changes in public opinion during the implementation of a narrative in social networks

During the writing of the article, the complex dynamics of forecasting public opinion based on the implementation of narratives in social networks were analyzed using Bass's innovation diffusion model. Recognizing the important role of narratives in the formation of collective views provides a basis for using Bass's innovation diffusion model to predict and understand changes in public attitudes during the introduction and implementation of narratives in social networks. On the basis of the above, the research task was set: improvement of the Bass innovation diffusion model for predicting changes in public opinion in social networks, which will directly contribute to the development of quality content by determined forces and means to create favorable conditions during the use of troops (forces) and in peacetime due to entering the coefficient of coverage of the target audience. During the writing of the article, theoretical methods were applied, namely, the analysis of research and publications on the topic of development in relation to the use of diffusion of innovations, which describes the process of how innovations are accepted by the population, the analysis of statistical data of public opinion, comparisons to identify development trends in the consumption of information in social networks, as well as synthesis to achieve the goal of the study. The article presents a mathematical apparatus that incorporates the Bass diffusion model into network behavior for modeling the spread of narratives and predicting changes in public opinion. For the first time, an approach to simulation modeling of the introduction and implementation of a narrative in a social network based on the Bass diffusion model was substantiated, which made it possible to investigate the consumer behavior of social network users under the influence of information and interpersonal communication in a social network. Integrating concepts from Bass's model, such as innovation adoption and imitative behavior, the proposed model aims to predict the dynamics of public attitudes in response to the spread of narratives.

METHODOLOGY FOR ASSESSING THE IMPACT OF INFORMATION THREATS IN THE CONDITIONS OF INFORMATION CONFRONTATION

The problem of information confrontation as rivalry in the information sphere with the aim of influencing various aspects of social relations is extremely important in the period of hybrid war. The object of the study was to the models of the dissemination of information and models of information confrontation. The goal of the work is the determine the features of the dissemination of informational threats in the conditions of informational and psychological conflict using a mathematical model based on modern achievements of social psychology, and to find optimal modes of informational dissemination and means of neutralizing informational threats. Mathematical models are proposed that describe the processes of the dissemination of informational threats in the conditions of informational and psychological conflict based on the innovation diffusion model and rivalry models. It is assumed that two flows of information dissemination in society so that each individual can become a supporter of one or the other side. The peculiarities of the methods of choosing behavioral strategies by subjects of influence in the social network during information confrontations are analyzed. The conditions under which one of the parties to the conflict can achieve an advantage by systematically varying the characteristics of the process have been studied. Based on the analysis of mathematical models of the dissemination of information threats, meaningful characteristics are defined, the management of which will reduce or neutralize the impact of the flow of negative information. The presented mathematical models allow to make for both quantitative and qualitative analysis of the main characteristics of the dynamics of information confrontation. The prospects for further research may consist in the generalization of the proposed models for a larger number of information flows and taking into account the dynamics of changes over time in the intensity of information dissemination and other characteristics.

Revving towards sustainability: Environmentalism impact on electric motorcycle adoption

Recognising hydrocarbon vehicles as significant greenhouse gas emitters, mainly CO2, driving climate change underscores the urgent need to transition to sustainable, eco-friendly transportation. Existing literature has explored electric vehicles extensively, but there needs to be more research on electric motorcycles, especially in the Ecuadorian context. This study aims to fill this gap by investigating how environmentalism, a second-order construct formed by awareness, concern, and ecological value, influences the intention to buy electric motorcycles. Using the diffusion of innovation model, mediating variables like observability, complexity, relative advantage, compatibility, and attitude towards electric motorcycles are studied. A survey of 634 e-motorbikes is used to conduct the research, and the PLS-SEM technique is employed to analyse the results. The findings reveal that environmentalism significantly influences attitudes and purchase intention, indicating environmental concern's pivotal role in shaping electric motorcycles' attitudes and purchase intentions.Additionally, compatibility and observability have relevant impacts on attitude and purchase intention. In contrast, complexity and relative advantage do not significantly affect the endogenous variables, suggesting that observability effectively covers considerations of electric motorcycle advantages. The study's robust predictive capacity highlights the connections between environmentalism, innovation diffusion variables, attitude, and purchase intentions for electric motorcycles.

How do bottom-up innovations spread between schools and districts? An actor-container model of educational innovation diffusion

AbstractThe present paper deals with a research methodology issue. After an introductory literature review, it presents a novel model developed to study the diffusion of educational innovations. This model does not focus on the time course, phases, or characteristics of diffusion, which can be described by various innovation indicators, but on persistent structural elements such as the actors, their relationships, and the territorial, organizational, or other entities that host the actors. The latter, which separate the actors from each other or even constitute a common space for them are called containers. The presented actor-container model (ACM) was developed to help interpret empirical data in the context of a larger research, named Innova project, dealing with the emergence, diffusion, and system-shaping impact of bottom-up innovations initiated by teachers or other local actors in the education sector. In this paper, we demonstrate the application of ACM by analyzing the responses of educational institutions (organizations) at different levels of public and higher education in Hungary, based on the 2018 online questionnaire survey database (N = 2042). The examples presented show that ACM provides a unique perspective for research on innovation diffusion by shedding new light on actors and containers, opening up new possibilities for data analysis and results interpretation. We believe that ACM can be applied not only in the context of educational innovations but also in other innovation fields.

A Conceptual Framework of Mobile Payment System Adoption and Use in Southeast Asia

The research aims to investigate the factors affecting consumers' adoption and use of Mobile Payment Systems (MPS) in Southeast Asia. Extant theoretical models need to be extended to cover the unique features of mobile payment technology. A Systematic Literature Review (SLR) is conducted to analyse theoretical models utilized to predict the adoption and use of mobile payment. A total of 60 studies about adoption and use of MPS is analysed. The results of the investigations employ the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Diffusion of Innovation model (IDT). It reveals that there are inconsistencies in determinants of behavioral intention to adopt and use MPS. Among them are performance expectancy, effort expectancy, and social influence. The review also unveils the use of other determinants to predict behavioral intention to adopt and use MPS, such as perceived security, perceived risks, perceived trust, attitude, and financial incentives to lure new users. The researchers propose a conceptual framework for MPS adoption and use that includes four moderators: gender, age, educational level, and income level. The research contributes to the theory and practice by explicating relevant factors predicting behavioral intention to adopt and use mobile payment in the ASEAN region. Moreover, the SLR offers opportunities for future investigations.

New Model for Coal Gas Diffusion Based on the Fractal Tree-Like Bifurcation Network Structure.

To investigate the nonstationary diffusion and transport law of gas in coal, an innovative fractal diffusion model based on fractal theory and a treelike bifurcation network under different diffusion modes were built. In addition, the quantitative relationships among the diffusion coefficient, temperature, pressure, and structural parameters were determined. The model considers the effect of pore tortuosity and connectivity on gas diffusion, which renders it more realistic than the previously presented single-pore diffusion models. Moreover, each parameter in the model has a definite physical meaning and does not contain any empirical constants. The applicability of the new model was verified by experimental data and other model. Subsequently, a sensitivity analysis of the gas diffusion coefficient was performed to study the effect of the microstructural parameters on gas diffusion. Finally, the dispersion degree of the diffusion coefficients at different temperatures and pressures was analyzed to determine the main influencing factors of gas diffusion at different diffusion stages in coal and study their evolution. The results show that the gas diffusion state is more sensitive to pressure variations. The diffusion behavior of gases in coal-based porous media is more influenced by the temperature and pressure at the beginning of the diffusion process.