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This work is part of a research project that seeks to gain insight into the applicability of different actor analysis methods. This paper aims to describe the analysis of the predictive value of two different actor analysis methods applied to the redesign of the water management of a Dutch polder.

The paper's approach is a parallel application and comparison of the results of two separate actor analysis methods: conflict analysis and transactional analysis.

Transactional analysis was more accurate regarding the prediction of the real world outcomes than conflict analysis.

The choice of analysis method may be of significant influence on the quality of understanding of the risks involved with different stakeholders. There may not be a single silver bullet.

An asset manager needs to apply multiple actor analysis techniques to fully grasp the impact of the threats posed by the stakeholders on the asset and its risk register.

The parallel application of the two methods and comparison of their predictive value is new.

Empirical results remain unclear as to whether organizational unlearning can improve radical innovation performance. The purpose of this study is to investigate how, and under which conditions, organizational unlearning influences firms’ radical innovation performance.

Drawing on the knowledge-based view, this study develops a theoretical model that hypothesizes a positive relationship between organizational unlearning and radical innovation performance, which is mediated by knowledge generation strategies. It also proposes that the impact of unlearning on knowledge generation strategies will be moderated by dysfunctional competition. Using survey data from 191 Chinese manufacturing firms, the hierarchical regressions were used to test the hypotheses.

The empirical results show that organizational unlearning not only impacts radical innovation performance directly, but also indirectly affects radical innovation performance through two distinct types of knowledge generation strategies: (internal) knowledge creation and (external) information searching. Moreover, dysfunctional competition plays a dual role, strengthening the positive relationship between organizational unlearning and information search and weakening the positive relationship between organizational unlearning and knowledge creation.

The present research broadens the understanding of how to promote radical innovation performance, which has great potential to improve the performance of firms on the market. Specifically, it deepens the knowledge of how organizational unlearning facilitates radical innovation performance by focusing on two distinct types of knowledge generation strategies as the crucial links, and enriches existing literature on the effectiveness of organizational unlearning in a dysfunctional competitive environment.

Practicing organizational unlearning for firms’ long-term success requires firms to develop and implement appropriate knowledge generation strategies in accordance with the characteristics of market competition in their operating environment.

This study offers new insights into how and under what conditions organizational unlearning affects radical innovation performance, enhancing the understanding of how organizational unlearning can be implemented to drive firm radical innovation.

This article compares consumer decision‐making styles between Singaporeans and Australians. Utilising Hofstede’s framework, the paper argues that cultural dimensions influence consumer decision making styles. It is essential that managers understand cross‐cultural consumer decision‐making styles to make strategic decisions or effectively handle members of these nationalities. Marked differences were found between the two populations for: brand consciousness, innovativeness and overchoice confusion. The results suggest that some consumer decision‐making styles differ due to consumers’ cultural values. Managerial implications and future research directions are discussed.

The purpose of this study is to explore consumer innovativeness as a personality trait and addresses the hedonic, social, cognitive and functional motivational elements that lie behind consumer innovativeness. It explores the weak relationship between consumer innovativeness and really new product (novel innovation) adoption and challenges the classic relationship between consumer learning, attitude and intention.

This study adopts a quantitative approach, gathering survey data via an institutional online platform. A total of 300 participants were recruited. Participants were directed to a website presenting the information of the product with the inclusion of 2D and 3D images and an avatar. For data analysis, CFA and structural equation modelling (SEM) were used.

Results indicate a positive impact of attitude on comprehension and intention. In addition, hedonic innovativeness positively impacts customer's attitude, whereas there is a negative relationship between social innovativeness and attitude. Motivational elements of innovation, with the exception of hedonic motivation, positively influence purchase intention.

The main limitation of the study lies in the measurement of purchase intention, as actual purchases cannot be assessed as the products are not yet available. The findings encourage marketers to target innovators first, ideally innovators motivated by hedonic needs.

The findings encourage marketers to target innovators first, perhaps for a long-term, innovators motivated by hedonic needs, as they are the ones who change their attitude positively towards novel innovation when presented in an aesthetically pleasant manner.

This study challenges the classic theories identifying the link between comprehension, attitude and purchase intention within the field of innovation. The findings indicate that while interacting with really new products, comprehension does not necessarily lead to attitude and intention but attitude does positively influence both intention and comprehension.

This article aims to address the relationship between price strategies and price‐setting practices. The first derive from a normative tradition in the pricing literature and the latter from a descriptive tradition. Price strategies are visible in the market, whereas price‐setting practices are hidden behind the boundaries of an organization.

The study deals with the relationship between price strategies and price‐setting practices that refer to the use of customer value, competition, and cost information. Hypotheses are tested on survey data on 95 small and medium‐sized manufacturing and service firms in The Netherlands.

The results show that price strategies and price‐setting practices are related because strategies are implemented through price‐setting practices. However, some firms do not pursue any of the strategies indicated by pricing theory, some firms engage in practices for no clear strategic reasons, and some firms insufficiently engage in appropriate practices to implement their strategic choices.

The results are limited to small companies. Researchers should examine why firms may not pursue any price strategy that is offered by pricing theory. They may also focus on organizational learning and pricing capabilities.

Managers need greater awareness about the price strategies they can use, should be cautious about a potential mismatch between price strategies and price‐setting practices, and should reassess whether their firms are capable of engaging in the appropriate practices.

Linking price strategies to price‐setting practices reduces conceptual confusion in the pricing literature and may help to specify the gap between pricing theory and practice.

The purpose of this paper is to identify which innovation implementation strategies have the highest success potential, and which factors affect the adoption of fruit‐related innovations by consumers. The authors focus on consumer‐driven and responsive fruit supply chains.

The authors propose a new conceptual framework that links fruit consumption to innovation implementation strategies. A total of 36 experts in four panels organised, respectively, in Spain, Poland, Greece, and The Netherlands evaluated the elements of the proposed framework.

“Market orientation” and “continuous learning and knowledge acquisition” surfaced as the innovation implementation strategies with the highest success potential. However, the optimal mix of strategies depends on the particular innovation as well as the geographic and cultural characteristics of the targeted consumer population. Furthermore, improving technological competence is the single most important factor affecting fruit innovation adoption.

The reported results are derived from four small groups of purposefully chosen experts and supply chain stakeholders. Nevertheless, they provide new insights useful to policy makers, consumers, and entrepreneurs.

In designing innovations, fruit chain actors should first and foremost consider the price premium consumers will have to pay. Furthermore, for some innovations geographic or cultural characteristics become very important. Therefore, fruit supply chains should design their innovation implementation strategies accordingly.

By adopting efficiently designed innovation implementation strategies, consumer‐driven fruit supply chains will convince consumers to eat more fruit. Thus, given the critical contribution of fruit consumption to human health, an important social goal is achieved.

While the literature on innovation is enormous, very little has been published on innovation implementation strategies adopted by consumer‐driven and responsive fruit chains. The significance of addressing the previously mentioned issues stems from the need to increase fruit consumption in Europe, which lags behind the levels suggested by physicians and nutrition scientists. Fruit‐related innovations are a useful means to achieving this goal.

The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.

A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.

The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.

Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.

While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.

The purpose of this paper is to analyze the innovation process of organizations representing the main sectors of Brazilian economic activity.

The literature review focuses on analyzing the innovation process characteristics regarding the innovation types. The authors carried out interviews with executives and managers in charge of innovation at the leading large companies in the respective sectors analyzed. The data analysis of this qualitative research was structured in three steps. The first step is the analysis of data collected for encoding, the second step, the summarization of the common points presented by the companies in each sector and, finally, the interpretation of these data, aided by triangulation from secondary data that support the analysis of the collected primary data.

The main contribution of this study is to characterize the innovation process of organizations representing the main sectors of the Brazilian economy, with a classification regarding the sectoral innovation standard.

The authors’ intent is that the paper can contribute with a comparative analysis among companies of the same sector and, subsequently, among companies of the different surveyed sectors. Thus, the characterization aims to present the companies’ innovation process and the comparative analysis aims to verify the innovation sectoral patterns. In addition, as implications for management practice, some strategies for better knowledge management in the organization are suggested for each type of innovation.

The main theoretical contribution focuses on the development of a conceptual model that structures the analyzed variables of the constructs “innovation process” and “innovation sectoral patterns”, allowing not only the characterization but also the comparative analysis of the representative organizations present in the sample.

Additive manufacturing (AM) or solid freeform fabrication (SFF) technique is extensively used to produce intrinsic 3D structures with high accuracy. Its significant contributions in the field of tissue engineering (TE) have significantly increased in the recent years. TE is used to regenerate or repair impaired tissues which are caused by trauma, disease and injury in human body. There are a number of novel materials such as polymers, ceramics and composites, which possess immense potential for production of scaffolds. However, the major challenge is in developing those bioactive and patient-specific scaffolds, which have a required controlled design like pore architecture with good interconnectivity, optimized porosity and microstructure. Such design not only supports cell proliferation but also promotes good adhesion and differentiation. However, the traditional techniques fail to fulfill all the required specific properties in tissue scaffold. The purpose of this study is to report the review on AM techniques for the fabrication of TE scaffolds.

The present review paper provides a detailed analysis of the widely used AM techniques to construct tissue scaffolds using stereolithography (SLA), selective laser sintering (SLS), fused deposition modeling (FDM), binder jetting (BJ) and advanced or hybrid additive manufacturing methods.

Subsequently, this study also focuses on understanding the concepts of TE scaffolds and their characteristics, working principle of scaffolds fabrication process. Besides this, mechanical properties, characteristics of microstructure, in vitro and in vivo analysis of the fabricated scaffolds have also been discussed in detail.

The review paper highlights the way forward in the area of additive manufacturing applications in TE field by following a systematic review methodology.