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The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts.

Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced.

Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches.

Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions.

The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.

A decade after Powell et al.’s (2011) seminal article on behavioral strategy, which called for models to solve real-world problems, the authors revisit the field to ask whether behavioral strategy is coming of age. The purpose of this paper is to explain how behavioral strategy can and has been used in real-world settings.

This study presents a conceptual review with case study examples of the impact of behavioral strategy on real-world problems.

This study illustrates several examples where behavioral strategy debiasing has been effective. Although no causal claims can be made, with the stark contrast between the negative impact of biased strategies and the positive results emerging from debiasing techniques, this study argues that there is evidence of the benefits of a behavioral strategy mindset, and that this should be the mindset of a responsible strategic leader.

This study presents a demonstration of analytical, debate and organizational debiasing techniques and how they are being used in real-world settings, specifically military intelligence, Mergers and acquisitions deal-making, resource allocation and capital projects.

Behavioral strategy has broad application in private and public sectors. It has proven practical value in various settings, for example, the application of reference class forecasting in large infrastructure projects.

A conceptual review of behavioral strategy in the wild.

The purpose of this paper is to analyze how far technology and information enable, facilitate or support the planning and implementation decisions in humanitarian vaccine cold chains for vaccination campaigns. The authors specifically focus on three emerging technologies that have the potential to create more flexible conditions in the field, and identify the need to further explore the link between uncertainty, information and irreversibility.

The authors present a basic structure for the analysis of cold chain disruptions in terms of three distinct yet connected layers of deficient infrastructure and capacity, information gaps and failures in decision making. The authors then review three humanitarian technologies and their impact on vaccine campaigns along these layers. From there, a research agenda is developed to address research gaps this review brought forward.

Three critical research gaps in the areas of technology innovation for humanitarian vaccine cold chain management are presented. The authors argue that technology to improve capacity, information and decisions need to be aligned, and that the areas of uncertainty, information and irreversibility require further investigation to achieve this alignment. In this way, the paper contributes to setting the research agenda on vaccine cold chains and connects humanitarian logistics to technology, information management and decision making.

This paper presents the humanitarian vaccine cold chain problem from an original angle by illuminating the implications of technology and information on the decisions made during the planning and implementation phases of a vaccine campaign. The authors develop an agenda to provide researchers and humanitarians with a perspective to improve cold chain planning and implementation at the intersection of technology, information and decisions.

Evidence from previous literature indicates that adopting a new innovative technology has a positive impact on a company’s business performance. Much less work has been carried out into examining whether a technology adoption has impact on corporate reputation. This paper aims to examine the latter topic in a context where social media is the channel used to share news about the introduction of a new technology. The empirical setting of the study consists of five retail companies located in the USA that decided to include Bitcoin as a payment platform.

Twitter data were used to measure how sharing news about the adoption of new technology could affect the reputation of the companies selected, keeping a clear distinction between the volume of data relating to social media responses and the sentiment expressed in the tweets. A panel vector autoregression model was used to incorporate series of data relating to news items, volume and sentiment.

The results show that the news about the adoption of a new technology has a positive impact on both the volume of tech-related tweets and the sentiment expressed in the tweets themselves, although the patterns of these two effects are different. The resulting impact decreases after a few days, both in volume and in sentiment.

The analysis has limitations that future research could address by extending and diversifying the examined companies and the social media used as data sources. The research suggests that managers in medium-sized companies can leverage on the introduction of new technologies that have a direct impact on their customers and gain reputational benefits in terms of immediate visibility.

The research introduces an additional dimension of analysis to the current stream of corporate reputation. Although the literature has already covered the dynamics of response to events on Twitter, by focusing on the adoption of the new Bitcoin technology, the paper provides novel insights.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

The purpose of the paper is to theorize how to develop student entrepreneurs' ability to reflect by means of a learning activity called the entrepreneurial diary, which seeks to develop self-regulated learners capable of intelligent entrepreneurial action. The importance of self-regulation in entrepreneurship is linked to the individual's ability to make judgments under conditions of uncertainty, which requires reflective thinking.

The paper builds on a synthesized conceptualization of three main literature strands, reflective thinking, cognitive-load theory and experiential entrepreneurship education. In addition to the synthesized conceptualization, it builds on some empirical insights derived from a venture creation master programme in which the learning activity has been developed and refined for the last seven years.

The main finding from the paper is the theoretical justification for why reflective thinking deserves an important place in the educational process and how the entrepreneurial diary as a learning activity can create a bridge between theory and practice in venture creation programmes that take an experience-based pedagogical approach. Furthermore, the study also provides some empirical insights of how students create self-awareness of their learning through the method and the metareflection reports. Self-awareness is foundational for developing conditional knowledge on why and when to make entrepreneurial decisions to balance the often action-oriented processes seen in venture creation programmes.

The paper provides both a practical learning activity to be used in the entrepreneurial classroom and a theoretical contribution on how entrepreneurial experience is transformed into entrepreneurial knowledge to enhance students' judgmental abilities to make entrepreneurial decisions in future entrepreneurial endeavours.

This study aims to investigate how two collaborative methods – selection and synthesis – influence knowledge convergence when people articulate a new strategic direction driving transformation within the organization.

The study is based on a longitudinal field experiment developed in four organizations involving 82 employees over a three-month process. Inspired by dynamics governing flocks as complex adaptive systems, selection and synthesis have been separately used in two sets of companies. Primary and secondary data have been largely collected and analyzed throughout the whole process.

This study describes how the two alternative methods differently influenced two kinds of knowledge convergence. While selection triggers a general and static knowledge convergence and the propagation of individual knowledge over time, synthesis fosters a local and dynamic knowledge convergence where individuals tend to propagate knowledge generated collectively.

This research offers insights into understanding the influence of alternative collaborative methods on the creation and propagation of knowledge when people are converging toward a new strategic direction. From a theoretical perspective, it contributes to complex adaptive system theory, highlighting the role of knowledge convergence and emergence through collaboration.

This research offers insights to managers who deal with the complexity of the engagement of different stakeholders during collaborative processes, offering some actionable takeaways to foster knowledge convergence by alternatively employing selection and synthesis.

This paper contributes to the management and social information processing literature emphasizing the role of knowledge convergence emerging from the complex interactions among multiple stakeholders.

The purpose of this paper is to identify and prioritize the positive and negative impacts of tourism on the process of tourism growth at a national scale in Iran, by taking into account the reviews of previous studies, views of experts and structural analysis.

In this investigation, structural analysis technique has been used to identify the correlation between variables by using mix method data analysis. By using cross-impact analysis (N × N integer matrix) in the form of the Micmac method, the economic, sociocultural and environmental factors have been evaluated.

The results of the distribution of factors in the coordinate axes and the graphs between them indicate their features, and for reaching a sustainable system of tourism development, at first, priority should be given to the negative influential factors, especially the environmental fields, and then the focus should be on the decrease of the dual and risk variables as they cannot be anticipated.

For the rapid growth of tourism in many countries, governments ensure that policies have been heeded in designing and preparing general plans of the country to understand how the development trend is moving on. In this respect, arisen impacts of tourism system are one of the important issues during the development path and in the field of tourism future. Because of the complexity and broadness of tourism activities, these impacts have also many interconnected dimensions that should also be considered while studying tourism impacts.

The purpose of this paper is to determine best practices of aid agencies for outsourcing logistics to commercial logistics service providers (LSPs) in disaster relief. Moreover, it evaluates the application of the Delphi method for research in humanitarian logistics.

The paper is based on a two-round Delphi study with 31 experts from aid agencies and a complementary full-day focus group with 12 experts from aid agencies and LSPs.

The study revealed 12 best practices for outsourcing logistics in disaster relief and a compilation of more than 100 activities for putting these practices into action. Experts consider a proper balance between efficiency and compliance, a detailed contract and a detailed service request most important. Additionally, the Delphi method was found to be a promising technique for research on humanitarian logistics.

By critically examining the Delphi method, this study establishes the basis for a wider application of the technique in the field of humanitarian logistics. Furthermore, it can help to prioritize future research as the ranking of practices reflects the priorities of practitioners.

The paper provides guidance to practitioners at aid agencies in charge of outsourcing logistics.

This research is one of the first in the field of humanitarian logistics to apply the Delphi method. Moreover, it addresses the lack of literature dealing with approaches for building successful cross-sectoral partnerships.

The study aims to examine the level and extent of forward-looking information (FLI) disclosure and identify the determinants driving the FLI disclosure (FLID) in the context of an emerging and developing economy.

The sample includes annual reports of the top 30 listed companies in Bangladesh for the years 2013–2017. The content analysis approach is used to examine the practice of FLID and to determine the extent of FLID based on the index. Multiple linear regression analysis is performed to identify the determinants of FLID.

This research finds that board size, auditor's global affiliation, leverage and profitability have a substantial positive impact on FLID. By contrast, firm size and listing age have a significant negative association with FLID. Moreover, contrary to our expectation, female representation in the boardroom has an inverse effect on FLID. This study, however, does not suggest any significant impact of board independence.

Small sample size may limit the generalizability of the findings. Besides, the FLID index score may be affected by the subjective judgment while analyzing the content of the annual report.

The findings of this paper may assist the regulators and policymakers in incorporating this new reporting paradigm in regulations. Alternatively, the current research can serve as a basis to further understand the importance of FLID for the stakeholders.

This empirical study contributes to the current FLI literature in Bangladesh. A handful of studies have been done to examine the nature and level of FLID and find out the determinants of FLID in the developing countries. To the best of the authors' knowledge, no study yet has been explored on FLID and its determinants by classifying them as qualitative and quantitative in Bangladesh.