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This chapter explores the phenomenon of managerial overoptimism, focusing on the cognitive underpinnings of the mechanisms that generate this bias. It develops a formal model of probability estimation that is inspired by the biological (cognitive neuroscience) evidence on associative information processing in the brain. The model is able to make novel, testable predictions about managerial overoptimism. It is able to parse out three mechanisms that could lead to overoptimism, as well as predict boundary conditions on when these effects should be observed and when the opposite (a pessimistic bias) should be observed instead. Furthermore, it predicts that under certain conditions, attempts by managers to “debias” their estimates might exacerbate the overoptimistic bias.

Modern prejudice was examined as a potential predictor of overestimating proportions of minority employees in gender-typed occupations. Strength of conjunction error was considered as an indicator of distorted perceptions of these proportions. Furthermore, the purpose of this paper is to investigate whether the association between modern prejudice and strength of conjunction error was weaker for gender-untypical than for gender-typical targets.

Modern prejudice was considered as a predictor of overestimations of black female employees in Study 1 (n=183) and black female older employees in Study 2 (n=409). Data were collected using internet-mediated questionnaires.

In Study 1, modern racism, but not modern sexism, was associated with greater strength of conjunction error when respondents were presented with gender-typical targets. In Study 2, using a sample scoring higher on modern prejudice than in Study 1, modern racism, but not modern sexism and modern ageism, was associated with greater strength of conjunction error, irrespective of target occupation. Furthermore, there was an unexpected association between lower sexism and greater strength of conjunction error for gender-typical targets, but not for gender-untypical targets.

The findings lend support to the ethnic-prominence hypothesis in that modern racism, but not modern sexism or modern ageism, was associated with greater strength of conjunction error. Furthermore, empirical evidence suggests that target non-prototypicality can dilute the effect of modern prejudice on strength of conjunction error.

This is one of the rare studies examining attitudes and conjunction error in a work-relevant context, thereby bridging the gap between social cognition and applied psychology.

This study aims to determine how cognitive diversity at the workplace influences team creativity. In this regard, the authors examined knowledge sharing and team-focused inclusion through which team members’ cognitive diversity was expected to elevate their positive work outcomes.

A quantitative method is used to accumulate the data. The authors surveyed workers and their respective managers at a single China-based food company. The supervisors rated the outcome variables (creativity and team effectiveness) regarding their employees, whereas employees were asked to rate the cognitive diversity, inclusion and knowledge sharing within the workgroup. The final valid sample size (n = 391) consisted of 137 workgroups with an adequate response rate (62.3%).

Cognitive diversity is related to team effectiveness but not creativity. The research found that cognitive diversity can increase creativity only through enhanced inclusion and knowledge sharing. Inclusion, likewise, explained the impact of cognitive diversity on effectiveness.

The originality of the current research lies in its contemporary exploration of inclusion and cognitive diversity and their pathways to team creativity and effectiveness. The social capital theory was applied to explain the proposed relationships.

The purpose of this study is to offer implications and future research directions related to new organizational forms like crowds. Organizations are increasingly relying on online crowds to innovate through mechanisms such as crowdsourcing, open innovation, innovation challenges and tournaments. To leverage the "wisdom of crowds", crowdsourcing platforms that enable heterogeneous knowledge sharing in crowds lead to novel solution generation by individuals in the crowd. Based on the associative variety memory model of creativity, the authors hypothesize that when a crowd contributes a heterogeneous knowledge in form of a variety of knowledge associations, individual crowd members tend to generate solutions that are more novel. In contrast to the brainstorming view that focuses on ideas as knowledge, the authors propose, test, find and elaborate on implications of crowd sharing of heterogeneous knowledge for the generation of innovation, i.e. novel ideas. The authors coded and analyzed all the posts in 20 innovation challenges leveraging online temporary crowds that were structured to foster knowledge sharing as part of the idea generation process. The analysis shows a positive relationship between the variety of knowledge associations contributed by the crowd and the generation of novel solutions by individuals in the crowd. Further, the variety of knowledge associations contributed by the crowd has a stronger relationship with novel solution generation than the number of associations generated by the crowd, i.e. variety of knowledge has a greater impact than either the quantity of knowledge or the number of solution-ideas shared. The authors offer four implications and several future directions for research on the new organizational form of online crowds.

The authors coded and analyzed all the posts in 20 innovation challenges. They also designed and ran these challenges in collaboration with corporate sponsors. The ideas in the challenge were rated by senior executive at each company using a creative forecasting method.

The variety of knowledge associations contributed by the crowd has a stronger relationship with novel solution generation than the number of associations generated by the crowd, i.e. variety of knowledge has a greater impact than either the quantity of knowledge or the number of solution-ideas shared.

The authors offer four implications and several future directions for research on the new organizational form of online crowds.

The authors propose several ways in which companies running innovation challenges can moderate and encourage crowd to generate a variety of knowledge.

The authors believe that we are the first empirical paper to emphasize and show that associative variety of knowledge sharing in crowds has impact on novel idea generation by crowds. This view is counter to "electronic brainstorming" view where crowd is asked to just generate these ideas and often just submit their ideas to the sponsor. Their view also goes beyond knowledge refinement of ideas by crowds to more of knowledge integration by crowds.

An online system of associative information retrieval is described. The system uses a network representation of the information in a bibliographic database and a processing paradigm modeling a continuing flow of user interest through the network to implement associative retrieval and query modification. The system is capable of relevance feedback, thesaurus and statistical query expansion, Boolean and best‐match searching, and retention of associations based on previous search experience. The query modification capability stems from the ability to incorporate thesaurus or dictionary linkages between terms and preferred or alternative forms and to generate associations between sets of documents and the vocabulary they contain. The former capability allows the automatic incorporation in a query of pre‐defined equivalent or alternate terms. The use of terms associated with retrieved documents allows automatic discovery and inclusion of related vocabulary, index terms, and subject categories and may improve the effectiveness of free text searching in databases incorporating controlled vocabulary indexing and subject classification schemes. A pilot version of the system has been implemented on the DECsystem‐10 and tested on small scale files;illustrative sample searches are presented.

The purpose of this paper is to apply the (advanced) brand concept maps (BCM) approach to reinvestigate previous findings on feedback effects resulting from brand extension information (BEI) and to explore whether this information affects the structure of a brand’s associative network.

This research builds on the associative network memory model, as well as Keller’s conceptualization of customer-based brand equity, and uses a series of empirical studies with a total of 839 respondents in two different countries.

The findings reveal that BEI has no significant impact on the structure of the parent brand’s associative network at the individual level. Furthermore, key brand image dimensions (i.e. favorability, strength, and uniqueness of brand associations) are not affected.

By applying the (advanced) BCM approach, this paper is able to address shortcomings that are incorporated with the use of Likert scales for measuring a brand’s image and for investigating feedback effects in the field of brand extension. As the results indicate that the identification of feedback effects might be influenced by the approach used to measure a brand’s image, this paper calls for further investigations of feedback effects on a brand’s image.

Data from three empirical studies provide insights into the cognitive processing of BEI and their impact on a brand’s associative network.

Purpose — The chapter presents the practical applications of web search statistics analysis. The process description highlights the potential use of search queries and statistical data and how they could be used in various forecasting situations. The presented case is an example of applied computational intelligence and the main focus is oriented towards the decision support offered by the software mechanism and its capabilities to automatically gather, process and analyse data.

Methodology/approach — The statistics of the search queries as a source of prognostic information are analysed in a step-by-step process, starting from their content and scope, their processing and applications, and concluding with usage in a software-based intelligent framework.

Research implications — The analysis of search engine trends offers a great opportunity for many areas of research. Into the future, deploying this information in the prognosis will further develop intelligent data processing.

Practical implications — This functionality offers a unique possibility, impossible until now, to observe, estimate and predict various processes using wide, precise and accurate behaviour observations. The scope and quality of data allow practitioners to successfully use it in various prognostic problems (i.e. political, medical, or economic).

Originality/value of paper — The chapter presents practical implications of technology. The chapter then highlights potential areas that would benefit from the analysis of queries statistics. Moreover, it introduces ‘WebPerceiver’, an intelligent platform, built to make the analysis and usage of search trends easier and more generally available to a wide audience, including non-skilled users.

The purpose of this paper is to analyze how sequence learning can build on pattern‐recognition systems and how it can contribute to the behavioral options of goal‐oriented systems.

A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning, that can recognize patterns, register patterns occurring repeatedly and connect these to sequences. Based on that it is analyzed how goal‐oriented systems can use information about reoccurring sequences.

A subsystem for sequence learning basically requires pattern recognition and it needs a structure for the directed connection of single standards for pattern matching to standards for sequences, given that it can learn both new patterns and new sequences. Such a subsystem for sequence learning may recognize a certain pattern and with that the end of a certain sequence. So it may deliver more than one output signal at a point in time, and therefore needs additionally a subsystem for directing attention.

The paper analyses the principles of an “associative” way of connecting standards for pattern matching to standards for sequences. Also it shows the cybernetic necessity of an attention directing system that has to decide how to deal with the multiple outputs of a subsystem for sequence learning, i.e. to decide to act either towards a pattern or a whole sequence.

The paper investigates basic mechanisms of sequence learning and its contribution to goal‐oriented behavior. Also, it lays the base for an analysis of attention directing systems and anticipatory systems.