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The purpose of this paper is to examine the concepts of learning and diffusion within the context of urban development and sustainable active mobility. A model is proposed, which treats learning as a diffusion process in a dynamic way, so that key strategies and their effects are investigated over time. Specifically, the question being examined is how learning and information technology strategies can change the mindset of people to be more physically active in their mobility around a city. This is carried out by developing a system dynamics (SD) computer simulation model, where learning strategies that promote walking can be tested prior to implementation.

Based on the specific key stages of knowledge, persuasion, decision, implementation and confirmation, the proposed SD model incorporates individual citizen and group level learning and diffusion processes in a unified integrated approach. The SD model gives a clear picture on how learning and changing mindsets occur in the context of a city, whereby we can explore the main drivers to learn new sustainability related habits. A case study is used to contextualize and demostrate the active mobility issue, in order to develop practical scenarios for promoting a walking mindset. This is done in order to demonstrate the capabilities of the model and show its practicality. Through the model we investigate the relationships among variables such as awareness, contact, communication and adoption and evaluate their impact on city policies to promote a walking mindset.

The developed SD model exhibits important dimensions, such as social learning, motivation to change behaviour and an analysis of the effects of plausible strategies for promoting sustainable active mobility. The authors have proven that through simulation experimentation, we can significantly accelerate the shaping and diffusion of a walking mindset, by influencing individual and community learning via the introduction of Information and Communication Technology in conjunction with an awareness campaign strategy.

The diagrammatic representation and the quantification of the factors that contribute to active mobility give rise to better understanding of how policy makers could promote a walking culture in a city. The implications are that the proposed SD model could be used by city planning authorities as a framework for shaping mindsets towards active and sustainable mobility.

This paper contributes to the further understanding of learning, diffusion of new ideas and strategic planning within the city context. Through the proposed SD model, a number of strategies are formulated and tested using scenario planning, sensitivity analysis and optimization, in order to develop a walking mindset and an urban culture of sustainability.

The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems. The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations. In addition, the models are tested for nonlinear systems with different noise conditions. In a nutshell, the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies.

Population-based evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and cat swarm optimization (CSO) are implemented in a distributed form to address the system identification problem having distributed input data. Out of different distributed approaches mentioned in the literature, the study has considered incremental and diffusion strategies.

Performances of the proposed distributed learning-based algorithms are compared for different noise conditions. The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate, but incremental CSO is slightly superior to diffusion CSO.

This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems. Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.

Many good sustainability entrepreneurial ideas and projects fail to reach their objectives due to problems with feasibility. This chapter investigates the effectiveness of methods used to evaluate the feasibility of entrepreneurial ventures in the context of sustainable urban development. Traditional methods and tools based on cost–benefit analysis could provide some guidance for entrepreneurs and intrapreneurs when evaluating sustainability strategies. Yet, such methods rely on restrictive assumptions, which cast doubt on their suitability for real-world sustainability applications. Traditional methods are far from really enabling entrepreneurs to make informed optimal decisions. New integrated methods are necessary for drawing conclusions vis-a-vis the practicality of entrepreneurial ideas by quantifying and analyzing the benefits and costs of all options in a given scenario. This chapter evaluates the effectiveness of current feasibility study methods and their suitability for sustainable urban planning and development. It surmises that caution is advised concerning their reflection of real-life applications, given the complexity and dynamicity of solving sustainability-related problems. It is shown that such methods can arguably be a useful tool when evaluating the viability of investing in innovation and sustainability if they are enriched with advanced modelling techniques, such as system dynamics and optimization methods. For this purpose, an entrepreneurial venture for promoting sustainable mobility via information and communication technology (ICT) is used as a case study. The proposed integrated ‘Sustainability Entrepreneurship’ approach for evaluating feasibility can prove to be very useful for entrepreneurs when assessing the efficacy of complex sustainable-related ventures.

The aim of this study is to propose a governance model and key performance indicators on how policymakers can contribute to a more accessible, inclusive and sustainable mobility within and across smart cities to examine sustainable urban mobility grounded on the rational management of public transportation infrastructure.

This study employed desk research methodology grounded on secondary data from existing documents and previous research to develop a sustainable mobility governance model that explores key factors that influence future urban policy development. The collected secondary data was descriptively analyzed to provide initiatives and elements needed to achieve sustainable mobility services in smart cities.

Findings from this study provide evidence on how cities can benefit from the application of data from different sources to provide value-added services to promote integrated and sustainable mobility. Additionally, findings from this study discuss the role of smart mobility for sustainable services and the application for data-driven initiatives toward sustainable smart cities to enhance mobility interconnectivity, accessibility and multimodality. Findings from this study identify technical and non-technical factors that impact the sustainable mobility transition.

Practically, this study advocates for the use of smart mobility and data-driven services in smart cities to improve commuters' behavior aimed at long-term behavior change toward sustainable mobility by creating awareness on the society and supporting policymakers for informed decisions. Implications from this study provide information that supports policymakers and municipalities to implement data-driven mobility services.

This study provides implications toward behavioral change of individuals to adopt a more sustainable mode of travels, increase citizens’ quality of life, improve economic viability of business involved in providing mobility-related services and support decision-making for municipalities and policymakers during urban planning and design by incorporating the sustainability dimension into their present and future developments.

This paper explores how urban transportation can greatly reduce greenhouse gas emissions and provides implications for cities to improve accessibility and sustainability of public transportation, while simultaneously promoting the adoption of more environmentally friendly means of mobility within and across cities. Besides, this study provides a detailed discussion focusing on the potential opportunities and challenges faced in urban environment in achieving sustainable mobility. The governance model developed in this study can also be utilized by technology startups and transportation companies to assess the factors that they need to put in place or improve for the provision of sustainable mobility services.

The paper extends the organizational learning framework: Structural-Functional (SF)-single-loop or Conflictual-Radical (CR)-double-loop learning to the management accounting literature. The sociological approach of organizational learning is utilized to understand those contingent factors that can explain why management accounting innovations succeed or fail in organizations.

We view learning as enhancing an organization’s strategic competitive advantage by making it better able to adopt and diffuse innovation in respond to changes in its environment in order to manage improved performance. The success of management accounting innovations is contingent upon whether its learning process involves SF-single-loop or CR-double-loop learning to adopt and diffuse process innovation.

The paper suggests that the learning strategy that the organization chooses is the reason why some management accounting innovations are more successfully adopted than others and why some innovations are easily diffused in some organizations but not in others. We propose that the sociological approaches to learning provide an alternative framework with which to better understand the adoption and diffusion of process innovations in management accounting systems.

It has become evident that management accounting researchers need to pay particular attention to an organization’s approach to adoption and diffusion of innovation strategies, particularly when they are designing and implementing process innovation programs for an organization. According to Schulz (2001), there are two interrelated stages of the learning that can shape the outcome of the innovation process in an organization. The first stage is related to the acquisition/production (adoption) of knowledge that results in gathering information, codification, and exploration. This is followed by the second stage which is the distribution or dissemination (diffusion) processes. When these two stages – adoption and diffusion – are applied within an accounting context, they address issues that are commonly associated with the successes and/or failures of management accounting innovations.

Although innovation involves learning, the nature of the learning process does not completely describe the manner in which an innovation affects the organization. Accordingly, we suggest that the two interrelated organizational sociological dimensions of innovations processes, namely, (1) the adoption and diffusion theories of Rogers (1971 and 1995), to approach organizational learning, and (2) the SF (single loop) and CR (double loop) approaches to learning be used simultaneously to describe management accounting innovations.

When an innovation is implemented, it initially can be introduced as an incremental change, one that can be limited in both in its scope and its breadth of administrative changes. This means that situations which are most likely to benefit from its initiation can serve as the prototype for its adoption by the organization. If successful, this can be followed by systemic accounting innovations to instituting broader administrative changes within the existing accounting reporting and control systems.

The purpose of this paper is to apply the organizational learning framework to the management accounting literature to better understand why management accounting innovations succeed or fail in organizations.

A theoretical framework integrating diffusion and organization learning theories is developed. Diffusion theory is used to describe the process whereby the innovation is implemented. Argyris' and Argyris and Schon's theory of organizational learning is used to describe the type of learning – single loop or double loop – required by the innovation. Finally, the works of Attewell, and of Schulz relating to organizational learning, and of Rogers and of Sandberg relating to adoption and diffusion theories, were utilized to identify and understand the potential pitfalls faced by managements implementing an accounting innovation.

The paper advances the notion that an organization's approach to learning and innovation should be of interest to management accounting researchers. The single‐loop (incremental/organizational development (OD)) and the double‐loop (radical/organizational transformation (OT)) learning influences the adoption (stage one) and diffusion (stage two) strategies that are appropriate for the design and implementation of management accounting innovations.

The paper makes an important contribution to the behavioral accounting literature by integrating sociological diffusion and organizational learning behavior literatures and relating them to management accounting research.

The Internet of Things (IoT) provides exciting opportunities for the construction industry to solve its time and resource constraints and frequent defaults. This study seeks to identify and rank the perceived importance level of principal research areas associated with the IoT and the construction industry by utilising a scientific mapping tool (i.e. VOSviewer). Such knowledge would enable key drivers for successful adoption of the IoT and digitisation technologies to be outlined. An analysis of key drivers and research trends that facilitates the development of a roadmap for applying the IoT and digital technologies in the construction sector is therefore much needed.

An interpretivist philosophical lens was adopted to analyse published work as secondary data, where each publication represented a unit of analysis. A total of 417 peer-reviewed journal review articles covering the IoT within the construction domain were systematically reviewed using a mixed-methods approach, utilising qualitative-scientometric analyses techniques.

The results revealed a field of study in a fledgling stage, with a limited number of experts operating somewhat in isolation and offering single-point solutions instead of taking an integrated “holistic” approach. Key publication outlets were identified and the main focus of research undertaken being in the technical areas of smart buildings, smart construction objects and environmental sustainability. The major effects of adopting the IoT within the construction industry were identified as high-speed reporting, complete process control, data explosion leading to deep data analytics, strict ethical and legal expectations. Key drivers of the IoT adoption were outlined: interoperability; data privacy and security; flexible governance structures; proper business planning and models.

The study benefits researchers and industry practitioners alike. For researchers, the identified gaps reveal areas of high priority in future research. For construction companies, particularly small to medium-sized businesses, the study raises awareness of the latest developments and potential applicability of the IoT in the industry. For government agencies and policymakers, this study offers a point of reference in directing the adoption of the IoT smoothly in the construction sector and provides guidelines and standards for maximising the potential benefits.

The study is the first scientometric review of the existing body of knowledge in the context of application of the IoT in the construction industry. Findings expose knowledge gaps in contemporary research, specifically, a broader consideration of organisational adjustments needed to accommodate the IoT usage, economic analyses and impediments to wider acceptance.

Researchers in the social sciences have studied the process by which new ideas are adopted (implemented) and how acceptance is generated among those charged with accepting and implementing an innovation. Sociology, in particular, has developed an extensive literature on diffusion analysis which examines how innovations are diffused (see Coleman, Katz, & Menzel, 1966; Leagans & Loomis, 1971; Rogers, 1971; Rogers & Shoemaker, 1971). While many of these studies dealt with the adoption and diffusion of a new product, for example, seed corn or drugs, the same analysis has been applied to process innovations, that is, system and organizational change.

The purpose of this paper is to identify and examine the antecedents that enable or constrain the adoption and diffusion of eLearning in higher education (HE). The key focus of the study is on the examination of how the organisation's diffusion structures, systems or processes influence the individual adoption of eLearning. The findings from this literature review contribute to practice through providing a better understanding of the issues associated with institutional diffusion mechanisms that aid the adoption of learning technologies.

An extensive search of the literature was conducted. The selected references were analysed into a number of categories; macro-level studies examining HE context of eLearning, micro-level studies focusing on individual and social factors and articles focusing on management issues of adoption and diffusion of technological innovations. Finally, over 300 articles were used to compile the findings of this paper.

The paper argues that future research studies should not model the adoption and diffusion of eLearning based primarily on either an individualist (Micro) or structuralist (Macro) perspective, but by using a more interactive approach to examine the complexity and multiple levels and dimensions of social reality.

A significant exclusion and one which clearly calls for further research, is the aspect of institutional structures such as library systems, virtual learning environments, administrative support systems and other technical systems such as enrolment, registration, assessment and students, with respect to the adoption of eLearning. Future studies may want to explore the interplay between these structures and agency.

The study findings contribute to practice through providing a better understanding of the issues associated with institutional diffusion mechanisms that aid the adoption of learning technologies. Considering the slow and often disappointing adoption of eLearning within higher education institutions (HEIs), the study reveals the nature of adoption that may inform the development of institutional eLearning diffusion structures.

The paper identifies that the importance of individual factors influencing the adoption of eLearning has been acknowledged by the above studies, and the underlying message has emerged that levels of eLearning adoption would be higher if strategic managers recognised the social dimensions of eLearning innovation and diffusion, such as: academic and professional goals, interests and needs; technology interests; patterns of work; sources of support; and social networks. The argument is that currently eLearning is geared towards technically “literate” and innovative staff, and this strategy reduces the likelihood of mainstream faculty actually adopting instructional technology for their own teaching.

A review of the eLearning literature shows that there only a few substantive theoretical accounts which adequately integrate multiple levels of analysis and explain adoption and diffusion of eLearning in terms of the interplay between structural influences and individual action. The paper argues for future research to be integrated in a general analytical framework.

We use collective learning theory to explain social movement strategic outcomes. Three movement strategies are conceptualized: insider, outsider, and generalist strategies. Generalist strategies are a combination of insider and outsider tactics. Movements learn in three main ways: retention of existing knowledge, adaptation based on past experiences, and via diffusion processes. Utilizing available data about the use of insider and outsider tactics in the state-level fight for woman suffrage, we find that state suffrage movements learned through retention of previously used strategies, adaptation in the face of major defeat, and through the diffusion of outsider tactics. Social movements exhibit structural inertia. Movement activists stick to what they know, unless they face a major defeat. Movement strategies are more complex and more flexible than suggested by the current focus in the social movement literature, suggesting the need to rethink the insider–outsider dichotomy.