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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.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

The management of consumer privacy has become a critical concern for organizations in the age of artificial intelligence–powered marketing. The impact of data on the market environment has brought both benefits and challenges, with marketers gaining valuable insights but also raising privacy concerns. As artificial intelligence–powered marketing advances, consumer vulnerability increases due to the sensitivity of collected data. This vulnerability leads some consumers to resort to falsifying information, posing a significant threat to the digital economy. Privacy empowerment and customer control play a vital role in addressing these challenges. This chapter explores the influencing factors and ethical considerations surrounding data falsification. It also discusses strategies to mitigate perceived vulnerability through privacy controls and explores the consequences of data breaches and customer vulnerability. The chapter further emphasizes the need for organizations to balance benefits, risks, and customer trust while harnessing the value of customer data. An ethical framework for data privacy marketing audits is proposed to help organizations assess their data practices responsibly and competitively. By integrating personal data protection strategies within an ethical framework, organizations can protect consumer privacy, enhance customer trust, and maintain their competitive edge in the market.

The concept of “participation” has become a buzzword in contemporary public governance models. However, despite the broad and significant interest, defining participation remains a debated topic. The aim of the current study was to explore how participants perceived and interpreted the meaning and scope of participation.

This study is part of a four-year (2019–2022) longitudinal research project investigating stakeholder participation in the context of developing and establishing a strategic regional plan in Region Skåne in southern Sweden. The research project has a qualitative approach and uses interviews with different stakeholder groups such as municipal politicians and public officials and a survey as empirical material.

The authors developed a participation spectrum including eight types of participation: to be open, to be informed, to be listened to, to discuss, to be consulted, to give and take, to collaborate and to co-create. The authors also identified four different purposes of participation: creating a joint network, creating a joint understanding, creating a joint effort and creating a joint vision. The spectrum and the purposes were related through four characteristics of participation, i.e. involvement, interaction, influence and empowerment.

The study rests on a single case, and so the results have limited transferatibility.

Researching participation in terms of the participants' perceptions contributes a new perspective to the existing literature, which has commonly focussed on the organizers' perceptions of participation. Moreover, in order to clarify what participation meant to the participants, the study puts emphasis on untangling this from the why question of participation.

This paper investigates the effect of state-society relations on the industrially-related growth paths of developed countries.

It introduces a novel theoretical framework, the state-business-labor relations (SBLR) framework, where four main actors are identified: the state, big businesspersons or tycoons, owners and managers of small and medium enterprises (SMEs) or Entrepreneurs and labor. Different SBLR categories or modes are introduced depending on levels of coordination and power relations between the studied actors. The paper then investigates how these SBLR modes, through adopting various policies targeting the industrial sector, lead to different growth paths. Rather than focusing only on economic growth, this research regards a growth path as a matrix of the performance in long-run growth and equality of distribution.

Using regression analysis and statistical data, the results suggest that the Co-Balanced mode, having higher levels of coordination and lower favoritism, leads to the best growth path among the four introduced modes, especially with its emphasis on high levels of venture capital availability and easiness of starting business. while the Lib-Capture mode, characterized by lower coordination and higher favoritism, seems to have the worst growth path and the best implemented policy for this mode is suggested to be high profit taxes that seem to counter the negative impact of the existing high levels of favoritism.

Despite the important findings that this research has reached, this paper is mainly meant to open a further investigation into this topic and open this dimension that the research on VoC and political economy have under-researched. A deeper investigation of SBLR typologies that could only be possible by having richer datasets with more data on coordination for the whole world, rather than only the advanced economies, would further our understanding of the dynamics that shape the growth paths of different countries of the world.

To realize the best industrial growth path, fighting favoritism should be an important objective. The negative impact of favoritism on innovation could not be disregarded in the eve of the fourth industrial revolution, where innovation is increasingly pivotal to future industrial development. Actively engaging societal groups in the policymaking process is important in addressing their concerns and balancing them at the same time. This should lead to the double benefit of formulating better policies that should foster growth as well as provide better distribution of this growth. High levels of coordination should help in realizing this objective. Yet, this could only be possible if societal groups are free to associate and aggregate their power and when there are means of preventing one actor from gaining more favorite treatment and exclusive influence over policymakers. The presence of both powerful and broadly represented business associations and labor unions and the existence of a government interested in coordinating their efforts-rather than letting itself be controlled by one group at the expense of the others-should help in the realization of the best growth path. Thus, institutional reform that empowers societal groups and enables them to defend their interests as well as fights all forms of corruption should lead to the realization of a more prosperous and equitable industrial development, with the “re-industrialization” of the developed world being no exception. The technological and social challenges of intensive automation and digitalization accompanying the fourth industrial revolution make the envisaged institutional reform more urgent.

This paper is introducing a novel theoretical framework for studying the effect of state-society relations, particularly SBLR, on the industrial growth paths of developed countries. It integrates three important bodies of literature in order to build a more comprehensive understanding of the dynamics of state-society relations and their economic consequences. These are the Varieties of Capitalism (VoC), State-Business Relations (SBR) and Industrial Relations. The SBLR framework differentiates between tycoons and entrepreneurs, an important distinction that often goes unnoticed. Different SBLR categories or modes are introduced, depending on levels of coordination and power relations between the actors. It is proposed in this research that the effect on growth paths goes beyond the simple dichotomy between CMEs and LMEs usually present in the literature of VoC and that power relations provide an essential complementary dimension in explaining this causality.

This study aims to explore the effects of expertise diversity on project efficiency and creativity in health-care project teams.

This study analyzes hierarchical linear models using multi-source data from 50 project teams in a large health-care organization in the USA. This data set includes self-reported survey responses from 274 team members and human resource information for all 515 members across the 50 teams. Expertise diversity is operationalized by professional diversity and positional diversity reflecting two dimensions, domain and level, of the concept of expertise.

This study reveals that professional diversity is negatively related to project efficiency and project creativity, whereas positional diversity is positively related to project efficiency.

Successfully managing a project team of experts within a limited time frame is a challenge for organizations. This study advances the understanding of the double-edged sword effect of expertise diversity on project teams, focusing on professional and positional diversity. It provides important insights for human resource development in terms of the composition of project teams regarding members’ expertise.

In the mid-2000s, the operator of New York City’s mass transit network committed more than a half-billion dollars to military contractor Lockheed Martin for a security technology capable, in part, of inferring threats based on analysis of data streams, of developing response strategies, and taking automated action toward alerts and calamities in light of evolving circumstances. The project was a failure. This chapter explores the conceptualization and development of this technology – rooted in cybernetics – and compares its conceptual underpinnings with some situated problems of awareness, communication, coordination, and action in emergencies as they unfold in one of the busiest transport systems in the world, the New York subway. The author shows how the technology, with all the theatrical trappings of a “legitimate” security solution, was apparently conceived without a grounded understanding of actual use-cases, and the degree to which the complex interactions which give rise to subway emergency can be anticipated in – and therefore managed through – a technological system. As a case-study, the chapter illustrates the pitfalls of deploying technology against problems which are not well-defined in the first place, to the neglect of investments against much more fundamental problems – such as inadequate communication systems, and unstable relationships with emergency response agencies – which might offer guaranteed benefits, and indeed lay a firm groundwork for future deployment of more ambitious technology.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

In this paper, the authors examine the strategies used to reduce labour costs in three public hospitals in South Africa, which were effective and why. In the democratic era, after the revelations of large-scale corruption, the authors ask whether their case studies provide lessons for how public service institutions might re-make themselves, under circumstances of austerity.

A comparative qualitative case study approach, collecting data using a combination of interviews with managers, focus group discussions and interviews with shop stewards and staff was used.

Management in two hospitals relied on their financial power, divisions between unions and employees' loyalty. They lacked the insight to manage different actors, and their efforts to outsource services and draw on the Extended Public Works Program failed. They failed to support staff when working beyond their scope of practice, reducing employees' willingness to take on extra responsibilities. In the remaining hospital, while previous management had been removed due to protests by the unions, the new CEO provided stability and union–management relations were collaborative. Her legitimate power enabled unions and management to agree on appropriate cost cutting strategies.

Finding an appropriate balance between the new reality of reduced financial resources and the needs of staff and patients, requires competent unions and management, transparency and trust to develop legitimate power; managing in an authoritarian manner, without legitimate power, reduces organisational capacity. Ensuring a fair and orderly process to replace ineffective management is key, while South Africa grows cohorts of competent managers and builds managerial experience.