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For at least three decades, inter-organizational collaboration (IOC) has attracted scholarly attention and many studies have unveiled its inner dynamics. More recently, new phenomena have appeared in the changing landscape of IOC, affecting the way in which organizations are open to interact with, and rely upon, other actors that may be standalone entities as well as representatives of other organizations. These actors operate “betwixt and between” the organizational core and its external environment(s), populating a liminal space located at the organization’s boundary in which activities take place according to non-proprietary and non-employment logics. The authors focus on the forms of collaboration, which blur the lines between organizations, calling into question the fundamental label of crowd-focused IOCs. The authors consider two forms: crowd-open and crowd-based organizations. The authors show the organizational design impact of openness spans from the mere scalability associated with organizational growth to the phenomena of reshaping formalization and standardization of roles and processes, and self-organizing over time.

Decision-making has long been recognized as being at the core of organizational life. Yet, the cognitive mechanisms by which managers make decisions represent a critical field of exploration. In this context, business models (BMs) are cognitive representations of organizational architectures that managers use to orient their firms in the business environment. While BMs – as managerial schemas – have been extensively studied for their beneficial applications at the strategic level, scholarly attention has rarely focused on their dark side. In this chapter, we point out that BM thinking – that focuses excessively on established schemas – might narrow managerial cognition in the process of fine-tuning the current BM; in the process, opportunities for more radical BM innovation can be overlooked. We systematize March and Simon’s contribution on managerial cognition into a more comprehensive conceptual framework by integrating the perspectives of Kahneman, Baron, and Gollwitzer. The result is an epistemologically coherent framework for managerial cognition and decision-making that focuses on how managers can overcome cognitive biases that derive from a reliance on established BMs as schemas. We close this chapter with directions for further research.

This paper aims at proposing knowledge translation as an element of business model design that can support entrepreneurs in achieving alignment and collaboration between entrepreneurial teams and external stakeholders.

The conceptual model presented in the paper is developed from the literature review and draws mainly on two streams of work as follows: first, the contributions related to the lean start-up methodology initially developed by Blank and Ries and second, the work of Osterwalder and Pigneur on business models and its subsequent developments. In addition, we draw on key insights from the entrepreneurship and organizational learning literature, such as discovery-driven planning and disciplined entrepreneurship.

The continuous validation framework (CVF) is introduced, posing the attention on underlining knowledge-translation mechanisms to decode complex concepts related to new venture creation.

The authors propose a new framework (the CVF) as an effective translational tool because it is a visual diagram that allows entrepreneurs to translate complex and technical ideas into a format that is more understandable for external audiences. Additionally, for each step of the CVF, specific translational mechanisms are defined and discussed, as each stage of the CVF presents specific translational challenges that result in outcomes that differ from stage to stage.

This study focuses on forecasting demand for markdown pricing in situations where historical data are limited and proposes using a hybrid knowledge-based residual (KRL) network to enhance the accuracy of demand predictions in fast fashion retailing.

We use a hybrid KRL network structure to increase forecasting accuracy in fast fashion data by combining artificial neural network (ANN) and theoretical demand models (TDMs) such as linear, exponential and multinomial logit. We used a linear demand model (LDM) as a theoretical demand model, which is one of the popular TDMs in the literature, and combined it with neural networks utilizing the residual network structure. We tested it on real fast fashion data to estimate the next week’s demand at the clearance seasons of 5 years.

The results underscore KRL’s capability to derive mutual benefits from both neural networks and LDM, especially in the specific context of limited fast fashion data. Furthermore, KRL outperforms LDM and ANN models when used individually in forecasting accuracy.

The research paper proposes a scientific, quantitative method for forecasting the sales for markdown settings, combining data driven and TDMs using residual learning thereby resolving the issue of insufficient sales data in the fashion industry.

The purpose of this paper is to propose a novel integrated approach using analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods for evaluation and prioritization of appropriate manufacturing flexibility type required in the face of multiple environmental uncertainties.

Using a case study of an Indian fashion apparel firm, the study demonstrates the application of the proposed integrated framework for evaluation and prioritization of manufacturing flexibility. The study uses AHP method to determine importance weight of environmental uncertainty criteria and subcriteria and then employs TOPSIS method to determine the final ranking of manufacturing flexibility types required to cope up with these uncertainties.

The findings of the case suggest that the proposed integrated approach is feasible and practically implementable for manufacturing flexibility assessment.

AHP has been extensively studied and used, but the major limitation of this proposed approach is the involvement of large number of pairwise comparisons leading to difficulty in maintaining consistency in pairwise comparisons.

The proposed approach can work as a benchmarking tool to practitioners in evaluating and prioritizing manufacturing flexibility alternatives and to suggest strategic allocation of resource by prioritizing different manufacturing flexibilities types.

Unlike conventional approaches, the study provides meaningful knowledge to decision makers by demonstrating a simple, flexible, and efficient method to evaluate and rank the appropriate manufacturing flexibility types.

Networks and learning matter to small- and medium-sized enterprises (SMEs). Networks and learning are also further elaborations on the exploration–exploitation (EE) dilemma. Ambidexterity, that is, managing this apparent dilemma, can be difficult as a result of many constraints. One of these constraints is that of mutually exclusive network structures. Consequently, ambidexterity is the ability to change networks, depending on need using mixed data on four small companies formed as part of an undergraduate management class, I hypothesize how specific network properties of the advice-seeking relationship, including density, cohesion, centralization, and embeddedness, affect two outcomes. Specifically, early exploratory learning is proposed to be positively affected by less-dense networks that maintain cohesion without centralization and do not have relations embedded in other relations. In contrast, later exploitative learning should be associated with denser networks that also have higher cohesion, higher centralization, and greater embeddedness. The results provide some support for these hypotheses and suggest further research in two areas that will benefit SMEs. One, how do early networks affect learning mode? Two, how does the ability to rewire networks provide the relational infrastructure to shift from exploration to exploitation – that is, to be ambidextrous in the face of the exploration–exploitation tradeoff?

The Metaverse is rapidly reshaping the understanding of tourism, yet the public perception of this new domain remains largely uncharted empirically. This paper aims to build on the technology acceptance model (TAM) and diffusion of innovations theory (DIT) to fill this gap, offering crucial insights that could inform scholars and practitioners in both the tourism and technology sectors.

Using a big-data approach, the authors applied machine learning to scrape comments made by social media users on recent popular posts or videos related to tourism in the Metaverse from three prominent social media platforms. The cleaning process narrowed down 15,461 comments to 2,650, which were then analysed using thematic, emotion and sentiment analysis techniques.

The thematic analysis revealed that virtual tourism evokes a complex range of public beliefs. While many express awe and excitement toward its immersive capabilities, others remain sceptical about authenticity compared to physical travel. Additional themes show people draw comparisons to real-world tourism, discuss technology’s role and note educational value and novelty. However, some comments raise concerns about potential societal harms, exploitation and mental health impacts. Sentiment analysis found over half of the comments positive, though some were negative. Emotion analysis showed contentment, happiness and excitement as most frequent, though sadness, worry and loneliness also featured. Overall, perceptions of Metaverse tourism encompass enthusiasm yet substantial ambivalence.

To the best of the authors’ knowledge, this study is one of the first to comprehensively analyse public discussions on Metaverse tourism. It takes TAM and Rogers’s DIT a step further and provides fresh insights into how these theories can be employed in the emerging field of Metaverse tourism. The themes revealed new conceptual insights into multidimensional factors shaping public beliefs about Metaverse tourism and thus informing scholarly research on virtual interaction and technology acceptance regarding Metaverse tourism. In addition, the results can help tourism providers, platforms and marketers address salient public beliefs and sentiments/attitudes in developing marketing offerings, experiences and communications. Over time, this analysis methodology can be used to track the evolving public perceptions of Metaverse tourism.

The purpose of the present study is to investigate travel agencies' social media usage and its perceived effectiveness by small- and micro-Italian travel agencies; the pre-pandemic period is compared to the forecasts for the post-Covid-19 period and different characteristics of firms and entrepreneurs are considered. Furthermore, the study analyses the expected benefits in terms of marketing objectives, such as improving brand image and/or personalizing the offer.

The research was developed through a questionnaire administered electronically to travel agents (282 respondents). The resulting data was analyzed by applying the McNemar test, a pairwise t-test and the multivariate analysis of variance.

The results show that social media are strategically significant for travel agents, even though their adoption is influenced by different agency aims; the perceived effectiveness results are diversified according to varying agency typologies.

The two main limitations of the study are its focus on the Italian context only and the missing consideration of the consumer's point of view. The latter prevents an exhaustive assessment of future trends regarding the use of social media in the client–agency relationship.

The study, which focuses on a little debated topic concerning the relationship between social media and SMEs, organically explores various dimensions related to the adoption of social media by small agencies, also considering the impact of the Covid-19 on the perception of travel agents. As a further element of originality, the research takes into consideration the main social platforms separately rather than the set of tools as a whole.