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Although the literature provides insights into the role of experiential learning based on prototypes in identification of latent customer need, it offers different views on the role of product prototypes in improving the efficacy of learning customer need, and also neglects the role of vicarious learning in prototype-based experiential learning. In a data-rich environment, market big data create new opportunities to learn from vicarious, digitalized experiences that are not observable with prototype-based learning. Therefore, the purpose of this study is to compare the effects of product prototype strategies – basic prototype strategy and enhanced prototype strategy – on identification of latent customer needs, and determine how each prototype strategy interacts with vicarious learning based on market big data to identify latent customer needs.

We collected data from 299 Chinese manufacturing firms via on-site surveys to explore our research question. All of our hypotheses were supported by the regression results.

This study finds that both the enhanced and basic prototype strategies (experiential learning from direct market experience based on prototyping) have positive effects on latent need identification, but the effect of enhanced prototypes is stronger. Furthermore, the enhanced and basic prototype strategies have different interaction effects with market big data (vicarious learning from indirect market experiences) on latent need identification.

This research extends the literature on prototype-based learning for latent need identification. It also contributes to the experiential prototype-based learning literature by exploring the role of vicarious learning based on market big data.

Flow control has a great potential to contribute to a sustainable society through mitigation of environmental burden. However, the high dimensional and nonlinear nature of fluid flows poses challenges in designing efficient control laws using the control theory. This paper aims to propose a hybrid method (i.e. machine learning and control theory) for feedback control of fluid flows, by which the flow is mapped to the latent space in such a way that the linear control theory can be applied therein.

The authors propose a partially nonlinear linear system extraction autoencoder (pn-LEAE), which consists of convolutional neural networks-based autoencoder (CNN-AE) and a custom layer to extract low-dimensional latent dynamics from fluid velocity field data. This pn-LEAE is designed to extract a linear dynamical system so that the modern control theory can easily be applied, while a nonlinear compression is done with the autoencoder (AE) part so that the latent dynamics conform to that linear system. The key technique is to train this pn-LEAE with the ground truths at two consecutive time instants, whereby the AE part retains its capability as the AE, and the weights in the linear dynamical system are trained simultaneously.

The authors demonstrate the effectiveness of the linear system extracted by the pn-LEAE, as well as the designed control law’s effectiveness for a flow around a circular cylinder at the Reynolds number of Re_D = 100. When the control law derived in the latent space was applied to the direct numerical simulation, the lift fluctuations were suppressed over 50%.

To the best of the authors’ knowledge, this is the first attempt using CNN-AE for linearization of fluid flows involving transient development to design a feedback control law.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

In this chapter, the authors critically examine the application of unmeasured latent method factors (ULMFs) in human resource and organizational behavior (HROB) research, focusing on addressing common method variance (CMV). The authors explore the development and usage of ULMF to mitigate CMV and highlight key debates concerning measurement error in the HROB literature. The authors also discuss the implications of biased effect sizes and how such bias can lead HR professionals to oversell interventions. The authors provide evidence supporting the effectiveness of ULMF when a specific assumption is held: a single latent method factor contributes to the data. However, the authors dispute this assumption, noting that CMV is likely multidimensional; that is, it is complex and difficult to fix with statistical methods alone. Importantly, the authors highlight the significance of maintaining a multidimensional view of CMV, challenging the simplification of a CMV as a single source. The authors close by offering recommendations for using ULMFs in practice as well as more research into more complex forms of CMV.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

Family businesses are characterized by the simultaneous presence of the family and the business system. The literature analyses sporadically the family support during the creation of a new family business. For this reason, the aim of this article is to offer new reflections and theoretical approaches in the field of family business studies. In fact, the study focuses on the first generation and the relationship and support with the previous generation (latent generation).

This perspective paper is based on a concise review of the literature.

The results of this offer a state of the art, synthesized and integrated, on the first generation to proposal the reader new knowledge on the first generation and relationships with family members.

This perspective paper distinguishes between the first generation formally engaged in the family business and the latent generation. The authors identify latent generation as a generation coeval with the first that supports the entrepreneur without being formally engaged in the family business. This study summarizes existing research on the first generation, highlighting the crucial role of the latent generation. Considering the latent generation determines an implicit and tacit generational transition not yet considered in the literature on the topic This study provides new research directions for scholars and managers to understand the entrepreneurial behaviors of families, family members and family businesses.

The purpose of this study is to describe latent classes explaining the innovation logic in the Finnish construction companies. Innovativeness is a driver of competitive performance and vital to the long-term success of any organisation and company.

Using finite mixture structural equation modelling (FMSEM), the authors have classified innovation logic into latent classes. The method analyses and recognises classes for companies that have similar logic in innovation activities based on the collected data.

Through FMSEM analysis, the authors have identified three latent classes that explain the innovation logic in the Finnish construction companies – LC1: the internal innovators; LC2: the non-innovation-oriented introverts; and LC3: the innovation-oriented extroverts. These three latent classes clearly capture the perceptions within the industry as well as the different characteristics and variables.

The presented latent classes explain innovation logic but is limited to analysing Finnish companies. Also, the research is quantitative by nature and does not increase the understanding in the same manner as qualitative research might capture on more specific aspects.

This paper presents starting points for construction industry companies to intensify innovation activities. It may also indicate more fundamental changes for the structure of construction industry organisations, especially by enabling innovation friendly culture.

This study describes innovation logic in Finnish construction companies through three models (LC1–LC3) by using quantitative data analysed with the FMSEM method. The fundamental innovation challenges in the Finnish construction companies are clarified via the identified latent classes.

Cost and time are two of the primary benchmarks in which construction projects are measured. A variety of factors, however, affect cost and time on construction projects, as identified in previous research. This has led to a need for better understanding how factors affecting cost and time overruns on public construction projects can be managed more efficiently. The purpose of this paper is to address these issues.

In this study 26 factors affecting cost and time overruns on construction projects were identified, through qualitative interviews with project managers from Danish governmental agencies and through a literature review. Through principal component analyses the 26 factors were subsequently narrowed down to four primary latent factors.

The identified four latent factors affecting cost and time overruns on public construction projects were lack of quality management, lack of project pre-planning, lack of user management and lack of project management.

Previous research has focussed on increasing knowledge by identifying and ranking factors affecting time and cost performance. This has led to the identification of an overwhelming number of factors to use for managing construction projects. The present research reduced the number of factors by clustering them into key latent factors responsible for most of the deviation in performance, narrowing the scope of construction cost and time management into a few tangible key focus areas. This supports and improves fast decision-making that is necessary in a changeable environment such as construction.

This paper focuses on patterns of film consumption within cultural consumption more broadly to assess trends in consumerism such as eclectic consumption, individualised consumption and omnivorous/univorous consumption and whether economic background and status feature in shaping cultural consumption. We focus on film because it is widely consumed, online and offline, and has many genres that vary in terms of perceived artistic and entertainment value. In broad terms, film is differentiated between mainstream commercially driven film such as Hollywood blockbusters, middlebrow “feel good” movies and independent arthouse and foreign language film. Our empirical statistical analysis shows that film consumers watch a wide range of genres. However, films deemed to hold artistic value such as arthouse and foreign language feature as part of broad and wide-ranging pattern of consumption of film that attracts its own dedicated consumers. Though we found that social and economic factors remain predictors of cultural consumption the overall picture is more complex than a simple direct correspondence and perceptions of other cultural forms also play a role. Those likely to consume arthouse and foreign language film consume other film genres and other cultural forms genres and those who “prefer” arthouse and foreign language film have slightly more constrained socio-economic characteristics. Overall, we find that economic and cultural factors such income, education, and wider consumption of culture are significant in patterns of film consumption.

There is evidence that quality improvement projects developed with the participation of patients and family members are more likely to result in a sustainable change. To identify the intervening factors is an important step in promoting and supporting patient and family members’ engagement.

A survey was carried out with 90 hospitals. A total of 35 intervening factors were evaluated by the healthcare professionals from the quality area using a Likert scale. Factor analysis was applied to identify the relationship among the factors and cluster analysis and the standardized scores for each new latent variable were obtained to observe the association between them and hospitals profile. Cluster analysis allowed to group the hospitals with similar responses and to analyze whether there was any association with the profile of the institutions.

A total of ten intervening factors are identified: two in the financial dimension, five in the structural and three in the personal and cultural. The standardized scores of latent variables suggest that the financial factors could be affected by the hospital capacity. The structural factors could be impacted by the accreditation status, location (region) and administrative control (ownership). And the personal and cultural factors could be by the location and dominant organizational culture. All of factors are influenced by the performed quality management activities. The cluster analysis allowed the identification of three groups in the financial dimension, and four in the other two dimensions. Except for the accreditation status in the personal and cultural dimension, no evidence of association between the groups and the variables raised to characterize the profile of the hospitals was found.

The study contributed to identify the relationship among the intervening factors turning possible to simplify and reduce them more comprehensively than those originally identified in the literature and at the same time maintaining the representativeness of the original variables.