The adoption of lean product development (LPD) in knowledge intensive manufacturing contexts is bringing about profound consequences in terms of organizational forms and human resource management practices. Project-oriented units are increasingly overlapping with vertical arrangements and hierarchical distribution of work, which in the past rooted manufacturers in functionally oriented structures. In this paper we wish to address the issue of project manager competences in an LPD close-to-assembly line project, with the aim of understanding which competencies are considered as essential to get the job done, how they could be declined in a specific setting and what is the relationship between behavioural and technical competences. We locate our analysis in the automotive industry, since it represents one of the empirical settings where the discussion concerning the deployment of LPD and project competences occurs. We chose FCA, a multi-brand auto manufacturer whose product range covers different market segments, operating through companies located in 40 countries. Our research unfolds considering a single case study, as this approach was considered useful in gaining in-depth, holistic understanding of the phenomenon studied. Indeed, this method is more suitable for obtaining an in-depth understanding of complex phenomena, especially when the focus is on the social processes involved (Eisenhardt and Graebner, 2007), just as it is in our study. While in the traditional literature behavioural competences are essential and the technical ones are marginalised, this case shows how it is possible to look at the other way around. Behavioural competences are legitimized by and related to technical ones. Our results will show that while many organisations are stressing the behavioural dimension of competences, in certain contexts it is still fundamental to invest in terms of technical skills.

Although the number of gathering methods and volume of data and information from customers grow, it seems that companies are mostly measuring customer satisfaction (e.g. Fernández-González and Prado Prado, 2007) or gathering customer complaints (e.g. Yilmaz et al., 2016). Companies can unlock the full value of information from customers by using Customer Knowledge Management (CKM) that can provide them with a competitive advantage through product innovation by applying knowledge from customers (Taherparvar et al., 2014). Not only customers can benefit from such innovations, but product returns could be reduced (Mourtzis et al., 2016) which brings value to companies (and the environment). Therefore, this study attempts to conceptualise the already existing knowledge that investigates how knowledge (information) from customers is used to innovate products (services). This study presents partial results from an extensive systematic literature review. For this study, the seven most suitable papers from the sample of 138 were chosen and thoroughly coded. The whole sample was identified according to a systematic search in WOS and Scopus index databases. The analysis was conducted in Atlas.ti. No comprehensive synthesis or empirical research that would specifically focus on the ways and reasons of using knowledge from customers to product and service innovations and to reducing reverse flows was found. Therefore, this study is the first step that will lead to better understanding why, how, when, what and who is gathering the knowledge from customers and why, how, when, and by whom is the knowledge used (if at all). This research is unique in the context of CKM for two reasons. Firstly, it focuses holistically on the studied phenomenon instead of using the only context of one discipline (e.g. Quality Management or Customer Relationship Management). Secondly, it uses the perspective of reverse flows. The proposed conceptual model can be beneficial both for academicians and practitioners. The model will help researchers with future research attempts in the studied context. The practitioners could use the model as a reference that could help them when starting to design how the information and knowledge from customers will be gathered and what should be gathered.

The recent development of the more than two-decade-long research into reverse logistics has reflected the diverse economic, technological, environmental and societal concerns and challenges. The article presents an analysis between reverse logistics and process improvement and product design; more specifically it explores if and how the companies utilize reverse logistics and reverse flows (returned products and accompanied information flow) for improvement activities. To learn about customer expectation and experience with the product (i.e., to extend the customer knowledge) is an indirect benefit that reverse logistics can generate if managed properly. An exploratory (quantitative) analysis compares the companies that perceive themselves as successful and less successful in expanding customer knowledge by reverse logistics. Bivariate statistical methods were employed on ordinal (scale) data collected by interviews with the representatives of 148 companies in 2016 and 2017. The companies successful in gaining customer feedback as an indirect effect of revere logistics differ in their approach to innovations (the higher frequency of innovation, the strategic importance of reverse logistics, and in a higher formalization of management system). They also have the more developed knowledge management system at their disposal. Finally, the data show a positive relationship between reverse logistics- related feedback and general effectiveness. Several papers documented the link between knowledge management and effective reverse logistics; however, the empirical evidence about benefits of reverse logistics for (customer) knowledge management is scarce, and in this respect, the paper provides original insight. The results document that reverse flows and reverse logistics can generate more than just cost savings, which was perceived as its dominant function. More specifically, management of reverse flows can be a stable component of knowledge management system, the component that provides the companies with feedback about customer dissatisfaction (and other facts such as expectations, or user experience), which can be effectively employed in the innovation activities.

This paper aims to explain the application of micro cases research strategy to investigate service employees’ innovative conduct. Essentially, the paper opts to outline the theoretical and practical implications and advantages realised through the application of this unconventional research strategy in the context of personal-interactive services; where the role of employees in initiating innovation is under-explored. The methods devised to realise the aims of this paper comprises of two approaches: 1) the review of relevant literature to highlight the special requirements to investigate service employees’ innovative conduct that requires closer assessment and an unconventional research design. 2) A practical explanation of conducting in-depth micro cases research based on a qualitative research of three case studies of service delivery systems. The main contribution of this paper would be in underlining the applicability of micro cases as a novel research strategy to investigate service employees’ innovative conduct which remains under-explored. The proposed research method would be specifically useful in the research of personal-interactive services subsector where employees’ input is perceived to be more influential. This paper proves the applicability of the micro cases research strategy when investigating service employees’ innovative behaviour. In essence, the arrangement of a micro case as a unit of analysis may particularly help management practice in precisely tracking the implications of progress or termination of innovative ideas emerging at the service delivery environment. Micro cases can also form a practical basis of manipulating innovative ideas development in idea generation software where it would be perfectly applicable to the software functions.

This study aims to reveal the expression and specificity of customer knowledge integration in the open innovation process in the health-tech sector. The collection method of data for this research is hermeneutic interview with top management or employees, which are directly responsible for innovation development in the organization. Only innovative organizations, which act in the health-tech sector and have high level of knowledge intensity, were selected. Interviews with 10 organizations had reached saturation of data. Data coding and analysis was facilitated by using MAXQDA 18. Open innovation development is intensive knowledge activity, which requires continuous knowledge updating. Open innovation activities include various collaborations of different kinds of knowledge sources, but the main value still has a customer. Therefore, for the success of open innovation with customers, there is needed effective utilization and adaptation of customer knowledge which is the result of customer knowledge integration in the open innovation process. In scientific literature there are identified different mechanisms, which help to integrate knowledge successfully. But usually these mechanisms are common for all kinds of knowledge. Therefore, it is needed to look into the specificity of context of customer knowledge, because it is the most complicated kind of all knowledge. Consequently nevertheless that numerous scholars have explained how implement open innovation and how to involve customers in the innovation process (Füller and Matzler, 2007), but there is a lack of insights about specificity of mechanisms of customer knowledge integration, which reflects integration of customer knowledge in the open innovation process and helps to do it more effectively. Therefore, this study contributes to the existing literature expansion. Moreover, the research reveals the expression and specificity of customer knowledge integration in the open innovation process in the health-tech sector of Lithuania. This sector is one of the most knowledge intensive sectors in Lithuania, which is characterized as fastest growing throughout Europe and pays a lot of attention and funding for the research and development. The results of research increase understanding and ability to integrate customer knowledge more effectively in the open innovation process in the organizations. Following it organizations become more flexible and gain sustainable competitive advantage.

The aim of this paper is to determine the influence of IT-based and personal interaction-based knowledge management (KM) practices on organizational learning (OL) and on relative customer value performance (i.e. performance against competitors), and to analyse the moderating role of technology level in these relationships. To test the above, we have used survey data from 180 Spanish companies and we have verified research hypotheses by means of structural equation modelling (SEM) based on partial least squares (PLS) using SmartPLS 3.2.7 software. The results demonstrate that technology level moderates the relationship between KM approaches, OL and relative customer value performance. Spanish low-tech firms base all their OL and customer value generation on personal interaction-based KM approaches, while high-tech firms show a more balanced combination of KM practices. To the best of our knowledge, there are not previous studies that compare the relevance of IT-based and personal interaction-based KM practices when it comes to enhancing OL and customer value, and neither those that consider technology intensity as a key contingent variable that could affect the effectiveness of different types of KM practices. The results obtained show managers how to shape their KM practices to promote OL and subsequent customer value depending on the technological context in which they operate.

The purpose of this paper is to provide multidimensional understaning of the management of feedback from customer product returns (warranty claism and complaints specifically) as well as of the management practices that enable the emergence of product returns in the context of knowledge management. Characteristics of product returns and their management indicate that to manage product returns requires specific knowledge management as the nature of their management is rather complex. Customer product returns are bearers of potentially rich feedback about some gaps, mistakes or problems in performance of forward value creating and delivering processes in forward supply chains. Jayraman and Luo (2007) label this potential hidden in product returns as a wealth of information that can be returned into the wealth of knowledge if knowledge management is applied. Understanding of feedback concept for the purpose of product returns avoidance management and its linkages with knowledge management through the theoretical review was performed in the first stage of research. Second, Soft Systems Methodology (SSM) to analyse the very complex situation, as the product returns do present, provided the conceptual framework for empirical research (McKay and Marshall, 2011). Research itself was realized in the small printing company where product returns do not lead to any extremely serious problems in the financial performance but – according to the words of CEO – managers would like to find the way how to reduce them. One of the reason is that there are several secondary effects these returns have and the environmental urge is perceived to be still stronger. Principles and best practices of SSM were followed and the analysis of documents (related to product returns, product design, quality, production, supplier and customer relationship management and financial situation) together with the theoretical knowledge of feedback, customer product returns and knowledge management served as the basis for the action research with managers, employees, customers, one supplier and local community actors. The paper is the first attempt to apply current knowledge of feedback construct into the knowledge and product returns management. It is also the first attempt to apply SSM to product returns management. SSM is the systemic approach that helps to uncover multidimensional character of any problematic situation in any social system through the involvement of different actors and their different views and to bring some solutions. Paper presents how this methodology can be used when dealing with the origin and disposal of customer product returns to utilize feedback from product returns and to turn tacit knowledge into the explicit one to minimise or reduce these returns within and across the boundaries of a company.

The exploitation Information and Communication Technologies (ICTs) in knowledge transfer from and to external partners is a nontrivial management issue (Awazu et al., 2009; Cui et al., 2015). How ICT platforms could enable connectivity and collaboration is a compelling scientific topic in the next OI research agenda (Bogers et al., 2017). Accordingly, this manuscript aims to explore the role and the potential of ICT in backing the outbound OI process up. From a managerial standpoint, this paper could provide guidance to the design of an ICT-based integrated platform for outbound OI, with a focus on the preliminary and conceptual phase. Through a literature review approach on OI, we defined and critically discussed the three main activities of the outbound OI process: Exploration of Markets and Technologies, Assessment of Technological Portfolio, Technology Transfer. We structured an outbound OI framework by means of two activities. First, we leveraged the knowledge-intensive nature of such process, and the similarities between it and Knowledge Management process. Second, we analysed ICT platforms for OI for merging evidences from the scientific literature with empirical insight. This framework paves the way for a contextualized borrowing of the ICT tools from KM stream to the OI one. The literature on OI is far from being conclusive in understanding both the structure of the outbound OI process and the relationship between OI and the digital transformation. Specifically, the role of digital technologies and platforms in enabling the connectivity and collaboration between actors is neglected when dealing with the outbound OI process, and felt in the scientific community as breeding ground for further research on OI (Bogers et al., 2017). The conceptual design is essential to allow the development of the ICT platform for outbound OI. The preliminary findings it could be useful for setting direction to approaching the outbound OI process, as well as for software developers: both can be inspired and build on the advanced propositions. The current digital transformation trend could frame the knowledge-intensive processes, i.e. the outbound OI, as a source of collaborative solutions within the Supply Chain Management scope (cf. Cerchione and Esposito, 2016). This could efficiently support the collaborative decision-making (Liu et al., 2013), leading to an enhanced, knowledge-based firm performance (Wong and Wong, 2011).

This paper proposes a decision-making tool for both supply firms and policy makers to evaluate the alignment between supplier’s knowledge and knowledge management systems (KMSs), and identify the level of efficiency and effectiveness of KMSs. Starting from these indices of efficiency and effectiveness, a taxonomy able to encompass the possible typologies of suppliers was established. A fuzzy logic-based decision-making tool is proposed to evaluate the efficiency and effectiveness of knowledge management systems in relation to a supplier’s knowledge. The proposed methodology is divided into three phases. The first phase consists in mapping supplier’s knowledge according to the two perspectives of analysis proposed by Nonaka in 1994. The second phase consists in mapping the KMSs used by suppliers by means of a Delphi panel involving two senior IT consultants and two researchers. Finally, the indices of efficiency and effectiveness are defined in the third phase. The paper evidences that suppliers adopt a large variety of KMSs to support the process of knowledge management. It seems to be of less concern that suppliers have scarce human and financial resources to invest in the area of knowledge management. This could be the result of the process of innovation in the field of information and communication technologies (ICTs) that are increasingly offering suppliers new low-cost opportunities (not requiring significant financial investments) and ease-of-use (needing no specific skills). The results of this paper suggest that the problem lies in the misalignment of the supplier’s knowledge with the KMSs adopted. In the end, the problem does not lie in difficulties in investing in human and financial resources, but in the ability to invest in the specific KMSs better able to support the KM process. This paper provides an operational tool for both suppliers and policy makers. Concerning the suppliers themselves, the proposed methodology may be used as a decision-making tool to suggest suitable changes in the adoption of KM-Tools and KM-Practices in order to increase the level of alignment with knowledge and thus improve the process of knowledge management. For policy makers, however, the methodology proposed here may be of help in identifying the weaknesses of the suppliers and identifying specific policies to support the competitiveness of supply systems by improving their management processes and the circulation of knowledge in a supply chain facing digital transformation.

Big Data has been heralded as a key agent of the third industrial revolution, and currently represents a promising area for value creation and frontier research. The potential to extract actionable insights from Big Data has gained increasing attention of both academics and practitioners operating in several industrial sectors. However, the adoption of Big Data solutions does not always generate effective value for the adopters. Therefore, the gap existing between the potential of value creation embedded in the Big Data paradigm and the current limited exploitation of this value represents an area of investigation that this paper aims to explore. In particular the present study aims at investigating the following research question: “Which are the multiple value directions that the Big Data paradigm can generate for organizations?”. In this vein, the article presents the result of a systematic literature review aimed at defining a framework that identifies the possible dimensions of value creation for an organization that may decide to adopt the Big Data paradigm. The research methodology adopted in this study is based on a Systematic Literature Review, and consists in four key steps such as search, selection, analysis, and synthesis. The result of the search process was a list of 481 resources. Then, by adopting the exclusion criteria, a subset of 91 resources was obtained. The analysis allowed to highlight the different types of value created through the adoption of Big Data paradigm within the organizations. Through the synthesis step, 12 value directions were defined and grouped into 5 key value dimensions. This paper introduces and describes a framework highlighting the possible value creation dimensions associated to the adoption of the Big Data paradigm by the organizations. By adopting a systematic literature review, the framework leverages and extends a previous contribution of Wamba et al. (2015) on the same topic. A further element of originality is related to the introduction of a further phase in the analysis, consisting in the application of text mining algorithms to analyse the selected resources, in the final aim to highlight possible terms and concepts remained hidden or latent in the human-led analysis. From a research perspective, the proposed framework aims at providing a twofold contribution: the more general one consists in the development of the debate on how organization can generate value by adopting the Big Data paradigm; the more specific one refers to the extension of the framework previously proposed by Wamba et al. (2015) on the same topic. From a practitioner point of view, the proposed framework may support managers and executives to understand better and define the strategic perspective of innovative projects based on the Big Data paradigm, which can be promoted and sponsored by the organizations.

The paper focuses on the Supply Chain Data Science (SCDS) by proposing System Dynamics as useful approach for managing the complexity of Big Data and optimizing the decision making process. The paper proposes the adoption of System Dynamics for the optimization of logistics of people, as pivotal test for a larger replication in the context of the Supply Chain. The analysis has been conducted for dynamically evaluating the different modal solutions available for different segments of demand. An element of originality can be identified into the application of a System Dynamics approach to the optimization of decision related to the logistics. By allowing to testing the usefulness of a System Dynamics approach for the decision making, the paper discloses several potential applications of Big Data in all the phases of a Supply Chain as well as for the larger customization of products and services.

Due to the increasing value of customized solutions, the entrepreneurial procedures from the customer order to the distribution getting more dynamic and complex. A way to handle this issue is an interdisciplinary value chain comprising corporations with different core competences. This type of overarching cooperation requires functional communication as driving source for every component of the production network. For this purpose, explorative research is needed on potential communication barriers in the exisiting organisational structures. We propose a descriptive model for company-wide communication including suppliers, distributors and customers of a value chain. After identifying the partners of a production network, we lowered the degree of detail from the structure of an organization to general communications methods in corporations. To analyse the whole system with its parameters on the different scales, we generalised it by way of so-called “communication paths”. These communication paths contain all information of communication between employees; they give a starting point for the evaluation of barriers and success factors of communication. The model is to uncover obstacles in communication structures in companies and production networks. Derivative communication designs will help to remove existing barriers and avoid the emergence of new ones, as well as to increase success factors. By enhancing the communication, operational processes can become more economical not just for enterprises, but for complete value chains. The research targets at a tool for architects, factory planners and entrepreneurs supporting the communication analysis of existing production network. The tool will show barriers within the value-chain-system and make proposals for overcoming them. The development of the descriptive model will be carried out within the DFG-funded research project InterKom (“Communication Design in Multi-Company Production Networks”).

The paper aims at providing a procedural and instrumental framework for enabling massive citizen participation in urban planning projects via digital collaboration and communication tools. Derived from a structured analysis of existing formal and informal participation processes in urban development we developed a comprehensive process supported by specific digital tools for massive participation. This so-called Minimal Viable Process fulfils a) good practice standards for participation while b) utilising as few as possible technical means and procedural steps. For this purpose, several workshops with authorities and professional designers have been performed to estimate the advantages of the different strategies. In a next step, the necessary tools and their interlinkages were developed and tested with a test-run of the process and with mockup-tools (Dummy Testbed). Going beyond existing participation processes in urban development, the newly developed procedure ensures proper citizen involvement from the very start of a project up to design selection, plus a lean and feasible conduct of the overall campaign. The framework involves all necessary stakeholder groups, who are addressed by specific tools and algorithms. The test-run of the process and its tools (“Dummy Test Bed”) was performed in Spring 2018 under laboratory conditions, utilising a real world project case but within a controlled experimental environment. The results indicate the general applicability of the process and the tools, and provided insight for the further technical development of the IT tools and the procedure itself within the H2020 project “U_CODE Urban Collective Design Environment”.

Exploring data-driven business models in the context of smart or intelligent cities, this paper introduces a methodology how a strategic exploitation of urban data can lead to an iterative, controlled and sustainable development of innovative services or products for local entrepreneurs, companies or municipalities. Our key idea is that comprehensive and transparent knowledge about the behaviour, feelings and needs of people in their daily living environment, as well as about the urban context, business environment and local networks can support and flourish the emergence or improvement of businesses, services and products in future neighbourhoods. Our approach connects urban data and local business models. We consider the needs of the citizen – who are “key consumers” of cities – as major impeller for successful urban ventures. In order to comprehensively summarize those needs, a combination of qualitative (constantly pursued surveys) and quantitative measurements (sensor infrastructures) are requisite. The process of qualifying existing local business models with the detected local needs, and reviewing them on the basis of locally collected data, we have termed “Urban Data Business Modelling”. Detected needs may hint at yet unidentified deficits in the local economy, thus leading to new business opportunities and to future cities with increasing life quality . The approach presented here targets at evidence-based design of business models. As it argues for the generation of local business models spurred by local data, it contributes to discussions on hyperlocality. Using advanced data analytics in order to identifying demand patterns within urban data may provide an entirely new basis for the generation of business models. Within a project funded by the European Social Fund, TU Dresden Laboratory of Knowledge Architecture and IT company Spectos GmbH are currently developing a platform prototype “Data4City” that combines goal-orientated urban data harvesting with Urban Data Business Modelling. The outcome of the project will be an advisory software supported by data banks of various urban business models, and a guidance system for the exploration of local business models.

The urban environment that we share today is going through complex processes of planning, re-design and refurbishment. Public involvement is largely scaled down to unidirectional informative announcements where fundamental strategic decisions and investments in conceptual design have already been made. Citizens are apparently split into people who are more than ever willing to engage in decision making processes regarding their built environment and others who seem to show disinterest. However, both groups do not hesitate to go out in protest and stop construction work that they do not approve of. This often turns into a nightmare for public administrations and investors, because a lot of money is at stake. Lack of continuous public involvement early on spurs civil resistance in realization phases caused by the feeling of being ignored and confronted with accomplished facts. Authorities, even if aware of the fact that conventional procedures are suitable no more, do not have the appropriate tools at hands, to pursue a dialog with the public on complex topics, at a large scale and with many, often disputing, stakeholders involved. Urban planning of a larger scale sometimes degenerates into a sharpened political discussion in which factual arguments do not take precedence. Public opinion then depends on random events and a mutually balanced decision-making process is far from likely. Misinformation can hardly be balanced out by content-focused information. A roundtable discussion involving all stakeholders such as citizens, experts, public authorities, investors and politicians is not likely to be convened only until the discussion has run already out of control. In addition, technical capabilities to make complex technical and planning content accessible and understandable to a larger audience are not available at present. Urban planning takes place in a complex context of regulations, expectations and opportunities which are prioritized differently in each of the groups involved. Success in communication between those groups depends heavily on media and formats used, as well as sentiments and trust. The language of an expert needs to be translated into terms understandable for citizen laymen. The budgetary options and limitations need to be explained to tax payers and other stakeholders. While the communication and data exchange needed for decision making and project management in construction planning resembles those of a BIM-project collaboration platform the exchange between experts and the public does not. Tailor-made areas i.e. for the laymen-to-expert dialogue, the different levels of co-creation between experts and citizens or the analysis and aggregation of bulk citizen feedback into a format consumable for experts are clearly an add-on. The developed framework and open platform prototype for mass participation will be a basis for an extended exchange of knowledge between citizens and decision makers in urban planning projects, making the public a part of the decision finding cycle. It leverages on the process management capabilities of the underlying Common Data Environment (CDE) Oracle + aconex connected BIM. The integration of the public in urban planning will further enhance the quality and acceptance of urban projects.

This paper describes a method and a toolset for capturing and analysing public opinions in social media in order to support citizen participation in urban development projects. This method was developed and is currently applied in the EU Horizon 2020 project “U_CODE” in order to inform professional urban planners about the public opinion and its different variations. In contrast to other projects our approach explicitly takes into account the dialogical nature of discourse in social media in order to capture the dynamics of online discourse and to get deeper insights into the public debate. We use a method called Discourse Structure Analysis to analyse the often complex message threads in social media. This method is able to detect dialogue sequences in order to find intensive discussions and calculate the amount of participation in relation to different topics. By analysing the reply patterns we derive the social network structures of the participants and find sub-groups and citizen experts. In combination with topic and sentiment analysis we can create structures which represent the connections between topics, sentiments and people and visualize these in different ways in order to support the cooperation between the public and the professional planners. Topic and sentiment analysis are very popular in the field of market research and arose as a helpful tool to gain useful insights from Social Media data. State of the art approaches usually share the method of assigning a numeric score between -1 and 1 to a target word referring to a product, a company or other developments to be evaluated. However, a single score which indicates whether sentiments are positive, negative or neutral, do not offer many insights for adjustment. Therefore, a combined approach of discourse, text and sentiment analysis for target based opinion retrieval is better suited to detect wishes, concerns, fear and similar emotions but also problems and ideas which are related to an urban planning project. The outcomes of the application are the following: Automatic capturing of online discourse across different social media channels. Mapping of public opinion structures, social networks and their interplay. Visualization of online debates in order to derive and summarize core argumentation structures. Feedback to all stakeholders of the discussion (participants, planners, decision makers) by adapted analysis results and visualizations.

In the context of digital spatial analysis and modeling urban space and processes, this article presents a methodology to update and operationalize Jan Gehl’s traditional observations on activities people engage in public urban space. We aim to show how shared (big) data can help to understand contemporary urban processes and retool urban planning and management for the common good. The article details how newly computed analyses, such as Shannon-Wiener Index of complexity of activities as well as gravity and centrality indexes, can be implemented to study the experiential qualities of public spaces and development opportunities of urban spaces and neighborhoods. The proposed method is tested in the city of Turku in Finland, where an interactive interface called Turku Open Platform is used by developers and stakeholders, integrating these analytics to decision-making and public discussions. The so-called human behavior or city social dynamics or practices are not exclusively determined by the morphology of the place or its function, but they have an anthropological basis. Social needs (need for security, for openness, of play, for isolation and encounter, etc.) are anthropological requirements generated and developed socially. In this context, structure, function and form are not sufficient for the generation of social relations, but they can only favor it. By measuring these social needs stored in online social media servers, a new layer of the city is defined and thus, it is available for analysis and eventually intervention. This whole process constitutes the city as a hybrid space that can only be fully comprehended by analyzing the layers of information beyond the spatial form. A great part of this information is registered in online servers, and it is rated and reviewed by apps and social media users. This could be understood as a sample of human behavior or social dynamics and practices to which one can access by mining API data. Re-organising both location-based social media data, statistical sources and configurational spatial analysis, the presented method unearths emerging activity patterns across scales from local to regional, shifting focus from the traditional functional analysis of urban space towards understanding activities and, thus, the human perspective of use, practices and new agencies.

Content of the article is an evaluation of the first results and observations in the framework of the EU-funded innovation project TRAILS. TRAILS is a new training and service format that offers innovation trainings on a mobile basis in rural regions. These are covered in rapid time by demographic change. Rural areas in the narrower sense occupy about 58 percent of the federal territory. Here live a quarter of the population. Central and regional centers, small urban areas, assume many cultural, economic and social functions for rural areas. Lack of work, educational and leisure opportunities make these regions unattractive for young people and often without prospects. The research aims at expanding the concept of ‘spacial usability’, `innovation adoption` and `technology acceptance` to the specific processes and determining factors and mechanisms in limited special work forms. A theoretical-qualitative approach was identified as an appropriate method of analysis, since the processes and factors at stake can be explained by a mechanism oriented strategy. The basic idea of TRAILS is to provide students and SME employees in rural and often economically disadvantaged regions access to new technologies such as maker spaces, hackathons and start up weekends, which are otherwise only available in urban areas. In addition, the innovation workshops on offer convey methods and techniques for their use as well as innovative thinking and acting. Companies such as Google use these formats worldwide to initiate innovations and founding ideas as well as to develop successful business models and at the same time secure the next generation of employees. Studies show that rural areas such as the project region have properties that, according to the regional scientific theory approaches, are considered enriching for innovation production. These include a strong cooperation tradition, close trusting network relationships based on long-term acquaintances, and occasionally close economic-political ties.

This research aims to evaluate different design and research methods for their applicability to the design of smart environments. It focuses on methods that are able to capture human emotions, feelings and interaction with their environments. The article will focus on a comparison of different design methods in the light of smart buildings and will further explore these comparisons by using two types of buildings (housing for older adults and schools). This article is a literature study on design methods, comparing literature on different types of design (such as user-centred design or co-design). Afterwards these insights are applied to two types of buildings, specifically smart housing for older adults and smart schools. This research shows the importance of designing with empathy in the design and research into smart buildings. Furthermore, it examines different design methods for different stages of the design. Lastly, it applies these methodological insights to two building types. For designing of and research on smart environments it is important that designers and researchers develop a sense of empathy for the users. It helps to give a more complete view of the user in terms of how they make sense of their environment and the interaction with their environment. This article contributes specifically to how this can be applied in the case of smart schools and smart buildings for older adults.

The purpose of this paper is to map supporting and hampering factors of innovation in emerging countries through conducting a systematic literature review. The research is based on a systematic literature review (SLR) approach. The review was conducted in several steps; determination of keywords and strings, selection of data bases and inclusion and exclusion considered search in chosen databases. The analysis includes 160 papers. This review provides an insight into the current body of knowledge on the topic which researchers can benefit from constructing an in-depth understanding of the state of research as well as innovation activities in emerging countries although our initial understanding presents a limited research on this topic. Based on these findings, future research avenues will be proposed and shown to both researchers for conducting future research to fill the gaps in research in innovation activities in emerging countries, and also practitioners to better understand the possible supporting and hampering factors towards innovation activities in emerging countries and therefore, build a better organisational structure for their firm or organisation.