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This paper aims to investigate three key factors (i.e. cognitive dimensions, the knowledge-driven approach and absorptive capacity) that are likely to determine the preference for informal inbound open innovation (OI) modes, through the lens of the OI model and knowledge-based view (KBV). The innovation literature has differentiated these collaborations into informal inbound OI entry modes and formal inbound OI modes, offering an advocative and conceptual view. However, empirical studies on these collaborations are still limited.

Building on the above-mentioned theoretical framework, the empirical research was performed in two stages. First, data were collected via a closed-ended questionnaire distributed to all the participants from the sample by e-mail. Second, to assess the hypotheses, structural equation modelling (SEM) via IBM® SPSS® Amos 20 was applied.

The empirical research was conducted on 175 small to medium enterprises in the United Kingdom, suggesting that the knowledge-driven approach is the strongest determinant, leading to a preference for informal inbound OI modes. The findings were obtained using SEM and are discussed in line with the theoretical framework.

Owing to the chosen context and sector of the empirical analysis, the research results may lack generalisability. Hence, new studies are proposed.

The paper includes implications for the development of informal inbound OI led by knowledge-driven approach.

This paper offers an empirical research to investigate knowledge-driven preferences in informal inbound OI modes.

This study aims to explore the role of equity investment in knowledge-driven business model innovation (BMI) in context of open modes according to the evidence from China’s primary market.

Based on the database of China’s private market and data set of news clouds, the statistic approach is applied to explore and explain whether equity investment promotes knowledge-driven BMI. Machine learning method is also used to prove and predict the performance of such open innovation.

The results of logistic regression show that explanatory variables are significant, providing evidence that knowledge management (KM) promotes BMI through equity investment. By further using back propagation neural network, the classification learning algorithm estimates the possibility of BMI, which can be regarded as a score to quantify the performance of knowledge-driven BMI

The quality of secondhand big data is not very ideal, and future empirical studies should use first-hand survey data.

This study provides new insights into the link between KM and BMI by highlighting the important roles of external investments in open modes.

From the perspective of investment, the findings of this study suggest the importance for stakeholders to share knowledge and strategies for entrepreneurs to manage innovation.

The concepts and indicators related to business models are difficult to quantify currently, while this study provides feasible and practical methods to estimate knowledge-driven BMI with secondhand data from the primary market. The mechanism of knowledge and innovation bridged by the experience from investors is introduced and analyzed.

This paper aims to introduce a knowledge‐based managemental prototype entitled E+ for environmental‐conscious construction relied on an integration of current environmental management tools in construction area. The overall objective of developing the E+ prototype is to facilitate selectively reusing the retrievable knowledge in construction engineering and management areas assembled from previous projects for the best practice in environmental‐conscious construction. The methodologies adopted in previous and ongoing research related to the development of the E+ belong to the operations research area and the information technology area, including literature review, questionnaire survey and interview, statistical analysis, system analysis and development, experimental research and simulation, and so on. The content presented in this paper includes an advanced E+ prototype, a comprehensive review of environmental management tools integrated to the E+ prototype, and an experimental case study of the implementation of the E+ prototype. It is expected that the adoption and implementation of the E+ prototype can effectively facilitate contractors to improve their environmental performance in the lifecycle of projectbased construction and to reduce adverse environmental impacts due to the deployment of various engineering and management processes at each construction stage.

Boredom is believed to be the common cause of workers' absenteeism, accidents, job dissatisfaction, and performance variations in manufacturing environments with repetitive jobs. Effectively measuring and possibly predicting job boredom is the key to the design and implementation of appropriate strategies to deal with such undesirable emotional state. The purpose of this paper is to present new methodologies to measure and predict human boredom at work.

Two series of mathematical formulations, linear and nonlinear, to describe the variation of human boredom at work are first presented. Given the complexity of human emotions, the authors also present a probabilistic framework based on state‐of‐the‐art Bayesian networks to model employees' boredom at work.

The proposed methods centre on the prediction and measurement of human boredom at work. They enable managers to take proactive actions to deal with human boredom at work. Examples of such actions are task rotation and job redesign.

The proposed methods are verified using a number of cases describing a set of phenomena that may occur in the real world. However, further research is required to demonstrate the validity of the models using real world data.

According to accessible literature, human boredom is being measured by self reporting scales thus far. This study describes and demonstrates analytical approaches to model human boredom at work.

Adopting a knowledge-based view of the firm, this chapter explores how different contents of firm-level entrepreneurship may influence performance of SMEs in moderately dynamic industries, which represent the bulk of economic activity in several countries. More specifically, this study aims, first, at identifying what types of entrepreneurial behavior – new-market entry, new-product development, diversification – are more suitable in order to survive and prosper in industries characterized by moderate growth and dynamism. Second, the analysis aims at assessing whether knowledge sharing is to be promoted in order to successfully compete in these industries. Third, the study aims at identifying which type of knowledge – market knowledge or technology knowledge – is most needed to develop entrepreneurial behavior and performance in low-growth industrial contexts. Following a knowledge-driven approach, we propose a view on corporate renewal that may complement current streams of research focused on large firms in high-velocity settings. Emerging results contribute to advancing the literature on entrepreneurial renewal by providing both an investigation of such behaviors within an industrial setting different from the high-growth, high-technology industries in which investigations have been conducted so far, and by suggesting that rich insights may be gained by investigating entrepreneurial recombinations within smaller firms that operate in less-dynamic contexts.

The curation of ontologies and knowledge graphs (KGs) is an essential task for industrial knowledge-based applications, as they rely on the contained knowledge to be correct and error-free. Often, a significant amount of a KG is curated by humans. Established validation methods, such as Shapes Constraint Language, Shape Expressions or Web Ontology Language, can detect wrong statements only after their materialization, which can be too late. Instead, an approach that avoids errors and adequately supports users is required.

For solving that problem, Property Assertion Constraints (PACs) have been developed. PACs extend the range definition of a property with additional logic expressed with SPARQL. For the context of a given instance and property, a tailored PAC query is dynamically built and triggered on the KG. It can determine all values that will result in valid property value assertions.

PACs can avoid the expansion of KGs with invalid property value assertions effectively, as their contained expertise narrows down the valid options a user can choose from. This simplifies the knowledge curation and, most notably, relieves users or machines from knowing and applying this expertise, but instead enables a computer to take care of it.

PACs are fundamentally different from existing approaches. Instead of detecting erroneous materialized facts, they can determine all semantically correct assertions before materializing them. This avoids invalid property value assertions and provides users an informed, purposeful assistance. To the author's knowledge, PACs are the only such approach.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

Many changes that call for concerted social action were observed in society and business performance during the coronavirus 2019 (COVID-19) outbreak. The impact of digitization and customer participation was evident in providing medical guidelines, updates on government initiatives, education or the supply of essential services during lockdown in many countries. However, there were aberrations. The purpose of this study is to explore some consumers and firms' being better equipped for service co-creation than others, specifically during a pandemic; the different degrees of service co-creation and the possible outcomes of customer participation in the service context.

Qualitative study with 35 in-depth interviews of supply- and demand-side actors, with coding and analysis of interview transcripts was conducted.

The authors identify two levels of service co-creation: (1) service co-development and (2) service co-evaluation that are affected by customer capabilities and firm/institutional barriers. The outcome of service co-creation lies in the social, economic and experiential values thus created. A pandemic strengthens the effect of antecedents (customer capabilities and firm capabilities) on the co-creation process.

Managers can refer to the findings to manage customer engagements and co-creations effectively, especially during a pandemic.

The impact of the pandemic on the service co-creation process in an emerging market, and the antecedents (firm- and customer-side) and consequences (mutual value outcomes) of service co-creation and actor participation are explored.

This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge.

This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients.

A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches.

The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features.

This paper aims to give an oversight of what is being done by researchers in GIS and remote sensing (field) to explore minerals. The main objective of this review is to explore how GIS and remote sensing have been beneficial in identifying mineral deposits for easier and cost-effective mining.

The approach of this research used Web of Science to generate a database of published articles on the application of GIS and remote sensing techniques for mineral exploration. The literature was further digested, noting the main findings, adopted method, illustration and research scales.

When applied alone, each technique seems effective, but it is important to know that combining different methods is more effective in identifying ore deposits.

This paper also examined and provided possible solutions to both current and future perspective issues relating to the application of GIS and remote sensing to mineral exploration. The authors believe that the conclusions and recommendations drawn from case studies and literature review will be of great importance to geoscientists and policymakers.