An integrative framework for collaborative forecasting in tourism supply chains

Abstract

Tourism practitioners must often rely on each other in a tourism supply chain (TSC). Demand forecasting plays a key role in shaping the activities of TSC practitioners. In the past 4 decades, researchers have developed many techniques for advanced tourism demand forecasting, but practitioners have had little interest in them. To bridge this gap, we examine the nature of the forecasting tasks of TSC practitioners in Hong Kong and propose a collaborative TSC forecasting framework that not only integrates tourism demand forecasting methods with practitioners' knowledge, but also facilitates information sharing between TSC practitioners to increase industry collaboration and improve forecasting performance.

Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology

“…Another interesting topic is how to build the IFN-based fuzzy productivity learning model if the collected productivity data are incomplete [67]. The proposed methodology can also be extended to fulfill a multiple-expert collaborative forecasting task [68][69][70][71][72].…”

Effect of inter-organizational systems appropriation in agility and resilience development: an empirical investigation

“…For complex service SCs like tourism, collaboration and coordination are key success factors for attaining optimal performance (Gursoy et al, 2015;Zach, 2016). Firms can collaborative effectively, provided relevant information sharing is done in a timely manner (Kasemap, 2017;Wu and Chiu, 2018;Zhang and Song, 2018). Furthermore, tourism SCs can function optimally in a synchronized fashion provided the diverse actions and processes of SC firms are well aligned (Huang, 2018).…”

“…While studies have been increasingly paying conceptual and empirical attention to tourism supply chains (Zhang and Song, 2018;Huang, 2018;Ghaderi et al, 2018;Lin and Wei, 2018;Babu et al, 2018;Jena and Jog, 2017;Pongsathornwiwat et al, 2017;Azmi et al, 2017;Chen et al, 2017;Akhter, 2017;Mandal et al, 2016;Roy et al, 2016;Guo and He, 2012;Zhang et al, 2009) for determining capabilities to improve overall performance (Mandal et al, 2016;Roy et al, 2016), little attention has been given to explore the development of SC capabilities in tourism with few exceptions (Mandal et al, 2016;Roy et al, 2016). More recently, while studies have explored sustainability issues also in the tourism supply chain (Mandal and Dubey, 2020;Tseng et al, 2018), there is a greater need to ensure sustainability in tourism supply chains through suitable dynamic capabilities of tourism SC agility (TAGL) and tourism SC resilience (TREL).…”

“Coopetition” for Cultural Tourism

“…The role of public institutions and decision-makers as neutral and impartial facilitators and coopetition enablers is fundamental (Seow et al, 2020). This well-guided and coordinated network thus overcomes the above-presented problems (size for and attitude toward innovation), since it better absorbs external influences; allows an increasing resilience of the stakeholders involved (Luthe et al, 2012); enables the development of an innovative offer in line with the needs of tourists (van der Zee and Vanneste, 2015;McCabe et al, 2012); supports the maintenance of the competitiveness of tourism organizations (Liu et al, 2017;Wang and Fesenmaier, 2007;Lavie, 2006); and becomes a generator of competitive advantage for the destination or attraction (Anderson and Hardwick, 2017;Zhang and Song, 2017), also giving rise to local resource-based barriers to entry compared to competitors (Favre-bonté and Thevenard-puthod, 2013; Barney & Arikan, 2001). In this way, coopetition finds the most effective way to maintain the competitiveness of cultural tourism destinations and attractions because of their smaller or niche markets compared to other attractions.…”