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The purpose of this paper is to present research in the area of the applications of knowledge‐based and constraint programming (CP)‐driven methodology in production planning and development of decision‐making software supporting scheduling of multi‐robot in a multi‐product job shop, taking into account imprecise (fuzzy) activity specification, and resource sharing by some industrial processes that simultaneously produce different products.

Applications of the knowledge‐based, logic‐algebraic and CP‐driven approach for multi‐robot task allocation problem and generating of fuzzy plan/schedule of production activities for a given period of time.

This paper illustrates the useful information that can be obtained from fuzzy and crispy‐like schedule describing production activities in a multi‐product job shop.

The use of knowledge‐based and CP‐driven methodology for production planning in a multi‐product job shop was a very effective method dedicated to solve typical decision problems in the area of project‐driven production flow management applied in make‐to‐order manufacturing.

The methodology discussed in the paper can be used to design fuzzy Gantt diagrams, which define admissible schedule of production orders for a given period of time.

The paper's contribution covers various issues of decision making while employing the knowledge‐ and CP‐based framework. The proposed approach provides the framework allowing one to take into account distinct (pointed), and imprecise (fuzzy) data, in a unified way and treat it in a unified form of a discrete, constraint satisfaction problem.

The theory of macroeconomic policy that has dominated thought and policy‐making since the later 1930s is essentially one‐dimensional in the closed economy (and two dimensional in the open economy). That is to say, in a closed economy we have been taught to operate on the level of demand ‐ with any or every macroeconomic instrument. When inflation is too rapid the aim has been to use (some or all of) our policy instruments to reduce demand; and when unemployment is too high we have learned to raise it.

Recognizing daily life activities and human behaviour from contextual information is a challenging task. The purpose of the research work in this paper is to develop a system that can detect indoor and outdoor daily life activities of low entropy mobile people such as elderly people and patients with regular routines using non-intrusive sensor and contextual information.

A framework is proposed that utilises a hierarchical approach in which high-level activities are divided into sub-activities and tasks and recognises the high-level outdoor and indoor activities of daily life. Tasks are recognised at lower level from sensor data and then used by the “activity recogniser” at higher level to recognise the high-level activities. For outdoor activities recognition, wireless proximity data are used, whereas for indoor activities, object usage data obtained through radio frequency identification sensors are used.

For outdoor tasks, results have shown 100 per cent recognition for experiment 1 and a decrease in recognition from 100 to 82.7 per cent, respectively, for experiment 2-9 due to increase in the entropy of individual tasks. Outdoor activity recognition ranges from 84.1 to 100 per cent. For indoor tasks, generating alternative tasks sequences approach effectively recognised the single tasks that were conducted with objects without any order. Average indoor activity recognition rate remains above 90 per cent. The reason why this approach is able to detect the activities without their distinct features is the planning capability of the Asbru that is used in the modelling of high-level activities.

The novelty of this research work is a framework that utilises different types of sensor data and recognises both indoor and outdoor daily life activities of individuals.

Through the lens of self-determination theory (SDT), the purpose of this paper is to examine the influence of autonomous and controlled motivational regulations in driving consumer participation in social brand engagement (SBE) practices. In addition, the moderating effects of cognitive effort and consumer demographic variables (age and gender) are examined.

The proposed model is tested by employing a quantitative survey design consisting of 832 consumers with prior experience in engaging with brands on social media. The respondents were conveniently interviewed using online questionnaire. The model estimation was done through structural equation modelling with AMOS 23.0.

The findings indicate that intrinsic, integrated, introjected and external motivational regulations significantly influence consumer participation in SBE activities, whereas identified regulation does not. Furthermore, while age and gender presented mixed interaction effects on the paths examined, cognitive effort does not moderate the influence of autonomous and controlled motivational regulations on SBE participation.

This study employed a cross-sectional survey to explore consumer motivation and cognitive effort in SBE practices. As an exploratory study, the findings may be limited and not conclusive, which could limit the generalisation of the results reported.

This study demonstrates a need for retailing managers to understand customers’ varying intentions or needs in participating in online SBE activities As a result, retail managers need to adopt social media strategies that could elicit interest and curiosity on the part of the customer to excite them to participate in the brand social interactions.

This research contributes to the conceptual understanding of SBE through the application of SDT, and contends that cognitive effort does not moderate consumer participation in SBE practices. Also, the mixed findings resulting from the moderation test of age and gender sheds light on specific types of regulated motivations that are either moderated or not in relation to these demographic variables.

A cell phone that behaves in a manner reflective of the current context has been a goal for the pervasive and ubiquitous research communities for a long time. This paper aims to demonstrate how two aspects of context – location and activity – can be inferred using GSM data present on standard cell phones.

A background knowledge of GSM networks is provided, followed by an assessment of the stability of signal strength levels in order to establish their usefulness in inferring aspects of context. A qualitative location system using GSM signals is presented and how to infer the current activity of the cell phone carrier is demonstrated.

The paper shows that by using the patterns of signal strength fluctuations and changes to the current serving cell and monitored neighbouring cells it is possible to distinguish between various states of movement such as walking, driving a car and remaining stationary.

The paper focuses on the practical aspects of deploying and managing location based services in dynamic outdoor environments.

The purpose of this paper is to compare the effect of different representations while teaching basic algorithmic concepts to novice programmers.

A learning activity was designed and mediated with two conceptually different learning environments, each one used by a different group. The first group used the learning environment “Visual Flowchart”, which enables the students to construct and examine an algorithm using visual representation based on actual flowchart objects. The second group used the software “Language Interpreter”, which allows the students to express an algorithms using pseudocode.

Analysis of results among the two groups showed no statistically significant differences in the students’ performance with respect to the tool they used to solve the activity, the school stream they followed in high school and their gender.

The lack of difference among the two groups could be attributed to the non‐complicated nature of the given activity. In addition, longitudinal studies of the effect of the different representation in the frame of an introductory first semester academic course in computer science could further validate the results.

Two alternative learning environments aimed to support learning of basic programming skills.

Two alternative learning environments were presented and discussed in detail, aimed to support learning of basic programming skills. The conclusions of the present study are in contrast to the research that has taken place in the past which compared usage of flowcharts and pseudocode to educate novice programmers, and wider adoption of “flowcharts” was depicted.

Proposes a heuristic iterative scheduling algorithm for the resource constrained project scheduling problem. Considers a general model where activities are represented by multiple operating modes and each operating mode is constituted of different activity durations and resource requirements. The performance measures considered are the minimization of project duration and the maximization of net present value (NPV). In the cash flow model assumed, activity expenditures take place at their start times and the project payment is made on its completion. The iterative scheduling algorithm consists of forward/ backward scheduling passes, where consecutive scheduling passes are linked by updated activity time windows. The iterative algorithm is supported by a conflict‐based activity selection technique called local constraint based analysis (LCBA). A considerable amount of improvement in both performance criteria is observed when the results of the iterative algorithm are compared with the results given by the initial forward schedule.

Physical inactivity has a considerable negative impact on health. Physical activity has reduced partly due to workplace and lifestyle changes, causing people to spend more time in buildings and increasing sedentary behaviour. The purpose of this paper is to address a largely untapped opportunity for designers and managers to improve building users’ health by designing buildings that raise users’ Non-Exercise Activity Thermogenesis (NEAT) levels. In this research a conceptual model was developed to assess buildings’ performance in providing NEAT-promoting opportunities through building design features and management, in relation to building users’ propensity for NEAT behaviours.

The conceptual model was developed by a multi-disciplinary team of researchers and data to populate the model was obtained through a survey of 75 buildings in Jakarta (Indonesia).

The presented proof-of-concept shows that the model’s “meso-scale” approach to study physical activity and building design can lead to potential improvements of NEAT levels and physical activity in buildings.

The review of precedent models shows that this subject has been researched at micro-scale (i.e. detailed monitoring of individuals’ movement) and macro-scale (i.e. epidemiological studies of populations’ health). The presented model is original, as it explores a “meso-scale”(i.e. building scale) that is unique.

Discussion regarding systems that promote innovation, aptly named innovation ecosystems, has been intensifying both in academia and business. The purpose of this paper is to introduce the activity theory as a theoretical framework for conceptualising and studying innovation ecosystems. Using the activity theory, it investigates elements that affect the success and viability of innovation ecosystems formed between startups and incumbent firms, collaborating with an established firm within the context of an open innovation programme.

This study uses an exploratory case research approach and proposes the activity theory as a theoretical background to be used in innovation ecosystem research. Based on this approach, this study draws from interviews and research observations in an innovation ecosystem formed between an established firm and various startups that aim to co-develop innovative offerings.

By applying the activity theory tools, this study identifies several contradictions between interacting actors of this innovation ecosystem that can adversely affect the innovation process. Furthermore, it proposes the use of the activity theory as a fitting theoretical lens to study innovation ecosystems.

The novelty of this study is related to the focus on the incumbent–startup context for extending the innovation ecosystem literature. Using the activity theory as a viable methodological tool allows us to conceptualise firms as social constructs and hence pinpoint inner characteristics that can affect and shape their interactions and the broader ecosystem. This process is further enhanced by the use of primary data that give unique insights into the inner workings of innovation ecosystems by identifying underlying contradictions.

One often thinks of competition as a mechanism whereby undertakings are forced to answer more efficiently the needs of clients. One may wonder whether that system could be used to organize the institutional environment. Competition already exists among states or regions, where it affects their capacity to attract investment or skilled workers. Could we go further and organise institutional competition among authorities within the same territory? Electronic communications provide a good case study, with the same competencies being attributed to regulators, competition authorities and judicial power.