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The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

Incentives are effective at enhancing productivity, but research also suggests that performance incentives can have “unintended negative consequences” including increases in hazard/injuries, increases in errors, and reduction in cooperation, prosocial behaviors, and creativity. Relatively overlooked is whether, when, and how incentives can be designed to prevent such negative consequences. The authors review literature in several disciplines (construction, healthcare delivery, economics, psychology, and [some] management) on this issue. This chapter, in toto, sheds a generally positive light and suggests that, beyond productivity, incentives can be used to improve other outcomes such as safety, quality, prosocial behaviors, and creativity, particularly when the incentives are thoughtfully designed. The review concludes with several potential fruitful areas for future research such as investigations of incentive-effect duration.

The question of how work motivation affects team members' tacit and explicit knowledge sharing has long puzzled organizational scholars. In this chapter, the quality of team–member exchange (TMX) is presented as one potential mechanism.

Key variables in the model are intrinsic and extrinsic work motivation, interactional and distributive organizational justice, tacit and explicit knowledge sharing, relationship-oriented and task-oriented TMX, organizational rules, organizational climate for trust. Separate models are developed for intrinsic versus tacit knowledge sharing.

While explicit knowledge sharing depends upon extrinsic factors such as extrinsic work motivation, task oriented TMX, distributive justice perceptions, and organizational rules, tacit knowledge sharing is dependent upon intrinsic factors such as intrinsic work motivation, relationship-oriented TMX, interactive justice perceptions, and perceptions of an organizational climate for trust.

This is the first model to provide a useful framework that should enable scholars to research the factors underlying the relationships between individual employee motivation and both explicit and tacit organizational knowledge sharing.

Despite the widespread attribution that stressful crucible experiences result in important individual developmental change within leaders, a deeper exploration of the mechanisms of that change is warranted. Likewise, literature linking the crucible and individual change to social and organizational considerations, including how organizations can plan for and sponsor institutionalized crucibles, is sparse. Thus, the intent of this chapter is to begin to synthesize the crucible, cognitive development, and stress literatures to show important linkages, risks, and outcomes, then provide a basic blueprint of planning considerations for organizations that desire to establish their own crucible events that target leader development.

Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of documents on public and social network websites. Therefore, this chapter integrates political blogs and news articles to develop a public mood dynamic prediction model for the stock market, while referencing the behavioral finance perspective and online political community characteristics. The goal of this chapter is to apply a big data and opinion mining approach to a sentiment analysis for the relationship between political status and economic development in Taiwan. The proposed model is verified using experimental datasets collected from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news, covering the period from January 1, 2016 to June 30, 2017. The empirical results indicate the accuracy rate with which the proposed model forecasts stock prices.

The aim of this research was to identify the importance of trust as a determinant of participants’ behaviour and the weight of different motivational factors that enhance the amount and the quality of contribution.

Quantitative research methods (online survey of 450 respondents and content analysis of 250 reviews) were applied on a Romanian crowdsourcing platform founded in 2008, with the mission to help potential tourists to take the most informed decision in their travel choices.

The data collected showed that the majority of the active members have a positive outlook over their experience within the community, admitting its trustworthy characteristics. The findings show that most of the top-rated members of the community were not motivated by material rewards such as money or prizes, but rather by socially related factors or by individual factors (positive feedback through comments or acquiring knowledge).

The findings cannot be generalised to other crowdsourcing models, which are subject to different task designs, outcomes, local contexts and even functionalities.

The results of this research can contribute to the design and implementation of customer-centred platforms, which might represent a way of development of organisational communication in the future.

The research posits that individuals’ experience within colloraborative crowdsourcing communities needs to be meaningful, as participants act based on a reciprocity norm, of giving something back to the community which is useful for fulfilling their own information-seeking purposes.