An integrative framework for business model innovation in the tourism industry旅游业商业模式创新的综合框架

Kraus, Sascha; Kallmuenzer, Andreas; Kanbach, Dominik K.; Krysta, Peter M.; Steinhoff, Maurice M.

doi:10.1080/02642069.2022.2127690

Cited by 9 publications

(14 citation statements)

References 132 publications

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“…Social does not apply only to large companies. It can also help start-ups and SMEs grow since it is a new way of knowing what you think the market and society think of the products of a brand or company or on the brand and the company in general (Kraus et al 2023 ; Sharma et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Entrepreneurship innovation using social robots in tourism: a social listening study

et al. 2023

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The tourism sector has been one of the most impacted by the COVID-19 pandemic, due to restrictions on mobility and fear of social contact. In this context, business innovation through digital transformation is presented as a great opportunity for the tourism industry and the inclusion of social robots in service tasks is an example. This transformation requires new methodologies, skills and talent that must be promoted to improve the innovative tourism ecosystem. With this research, we try to determine how the inclusion of social or service robots in hotels can improve the image and perception held by clients or guests. For that, we first analyse the degree of knowledge and sentiment generated by social robots through a social listening study in social networks. In addition, we determine whether these perceptions on the subject are in tune with other more formal fields, such as scientific research, or with the strategies followed at a national or international level by companies, agencies and organisations related to the technology and innovation of social robotics. For both objectives, we use the Simbiu social listening tool, a software-based program on Talkwalker, and we obtain interesting results. Basically, people on Twitter have a neutral or positive feeling about the use of social robots, and people who write in English have a more positive attitude towards social robots than Spanish speakers. After COVID-19, are necessary changes in strategic decisions of the hospitality and it is essential to continue investigating the role of social robots in this new context.

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Section: Introductionmentioning

confidence: 99%

Entrepreneurship innovation using social robots in tourism: a social listening study

et al. 2023

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“…This adaptation, modification, change and redesign of an existing BM are referred to as BMI (Lindgardt et al, 2009). When internal and external opportunities or threats arise (Bucherer et al, 2012;Kraus et al, 2023), BMIs are an appropriate managerial approach to remain competitive (Geissdoerfer et al, 2018). BMIs increase the resilience of an organization (Casadesus-Masanell and Ricart, 2011) by identifying new ways to create and generate value (Amit and Zott, 2001;B€ ottcher et al, 2022;Teece, 2010).…”

Section: Theoretical Background 21 Business Model Business Model Inno...mentioning

confidence: 99%

“…We therefore decided to apply the 4I framework of Frankenberger et al (2013), who divide the BMI process into four steps: Initiation, Ideation, Integration and Implementation. At the beginning, an internal or external change triggers the BMI process (Kraus et al, 2023) and prompts the innovating company to understand and reevaluate the environment and stakeholders. Based on emergent internal or external opportunities and threats, the responsible organizational members must collect and generate ideas for the new or adapted BM in the second process phase (Frankenberger et al, 2013).…”

Section: Theoretical Background 21 Business Model Business Model Inno...mentioning

confidence: 99%

Business model innovation decisions: the role of group biases and risk willingness

Mueller-Saegebrecht

2024

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PurposeManagers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.Design/methodology/approachEmpirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.FindingsFirst, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.Practical implicationsThis paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.Originality/valueThis qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

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“…To the present day, the transformation and adaptation occurring in enterprises has been fuelled by the spread of Covid-19 (Fuming et al ., 2022; Kusa et al. , 2022; Kraus et al. , 2023), also for travel and restaurant online reservations segment (Gretzel et al.…”

Section: Introductionmentioning

confidence: 99%

“…From this approach, e-commerce expanded their reach accordingly those years, so that researchers have gained an interest in the digital landscape promoting this at a time when the tourism sector gained from publications targeting the area (Buhalis and Law, 2008;Kimes, 2011;Amaro and Duarte, 2013;Law et al, 2014;Singh and Bashar, 2023;Arun et al, 2021). To the present day, the transformation and adaptation occurring in enterprises has been fuelled by the spread of Covid-19 (Fuming et al, 2022;Kusa et al, 2022;Kraus et al, 2023), also for travel and restaurant online reservations segment (Gretzel et al, 2020;Zanetta et al, 2021;Ali et al, 2022;Sari et al, 2022). Some of the areas with a high point of interest focussing attention for research studies are TRPs, in relation to behavioural factors, such as: consumer trust (Agag and El-Masry, 2017;Najdawi et al, 2023); for consumers to acquire products (Talwar et al, 2020;Khan et al, 2023), or to recommend a tool to other users (Choi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Understanding the use of travel and restaurant platforms through the UTAUT2 model

García de Blanes Sebastián,

Azuara Grande,

Sarmiento Guede

2023

BFJ

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PurposeReservation of travel and leisure services through the digital environment has gained a growing role in society, influencing the revolution of the sector. It needs to be noted that there is a considerable lack of formal approaches to the identification of factors for the positive reception and use of these digital tools. For this reason, it is necessary to establish the main factors influencing the adoption and use of digital travel and restaurant platforms (TRPs), adding to the theoretical model two new factors: trust and word-of-mouth (WOM). This paper aims to discuss the aforementioned ideas.Design/methodology/approachA theoretical model has been proposed, based on the extended unified theory of acceptance and use of technology (UTAUT2). Data collection was conducted through an online survey, in which 331 responses were compiled. Data obtained were analysed using structural equation modelling (SEM) with AMOS v27 software.FindingsThe results show that performance expectancy and WOM have a significant impact on the adoption of TRPs. However, it was found that effort expectancy, social influence, facilitating conditions, trust and price/value are not significant variables.Social implicationsThis research confirms that WOM communication positively influences the use of TRPs, facilitating various aspects for restaurant customers, including reducing wait times, streamlining the ordering process for allergic and intolerant customers, enhancing loyalty programs, order management and the opportunity to provide a personalised experience.Originality/valueThis study is the first to incorporate WOM variable into the extended UTAUT2 model applied to TRPs. Through a literature review, it has paved the way for significant future research directions that have not been adequately addressed by the scientific community, including the adoption and usage of food delivery platforms and online review platforms, as well as the behaviour of disabled customers towards these platforms.

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An integrative framework for business model innovation in the tourism industry旅游业商业模式创新的综合框架

Cited by 9 publications

References 132 publications

Entrepreneurship innovation using social robots in tourism: a social listening study

Entrepreneurship innovation using social robots in tourism: a social listening study

Business model innovation decisions: the role of group biases and risk willingness

Understanding the use of travel and restaurant platforms through the UTAUT2 model

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