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The objective of this paper is to analyze the relationship between innovation and the development of artificial intelligence (AI) and digital technologies in Europe. The use of digital technologies among European companies is studied through a composite index, while the relationship between innovation and AI is studied through a log-linear regression model. The results of the model have made possible to develop interesting indications for economic and industrial policy.

The use of digital technologies among European companies is studied through a composite index of AI and information technology (ICT) (using the Fair and Sustainable Welfare methodology) with the aim of measuring territorial gaps and to know which European countries are more or less inclined to its use, while the relationship between innovation and AI is studied through a log-linear regression model.

In the paper, two different methodologies were used to analyze the relationship between innovation and the development of digital technologies in Europe. The synthetic indicator made possible to develop a taxonomy between the different countries, the log-linear model made possible to identify and explain the determinants of innovation.

The description of the biunivocal relationship between innovation and AI is a topical and relevant issue that is treated in the paper in an original way using a synthetic indicator and a log-linear model.

本文旨在探討在歐洲、創新與人工智能和數字技術的發展之間的關係。研究人員透過一個綜合指數、去探討歐洲公司之間數字技術的使用狀況。至於創新與人工智能之間的關係, 則以對數線性回歸模型來進行研究。從模型所得的結果, 為我們提供了建議、去訂定適切的經濟和產業政策。

研究人員透過一個人工智能和資訊科技的綜合指數, 去探討歐洲企業之間數字技術的使用狀況 (研究人員使用了公平和可持續福利方法論), 其目標為測量領土差距, 以及確定哪些歐洲國家、大體上傾向於使用數字技術;至於創新與人工智能之間的關係, 則以對數性回歸模型來進行研究。

本文使用了兩個不同的方法、去探討在歐洲、創新與數字技術發展之間的關係。有關的合成指標, 使研究人員可製定一個不同國家間的分類法;而有關的對數線性模型, 則讓研究人員可確立並說明創新的決定因素。

本文使用了合成指標和對數線性模型、去探討創新與人工智能之間的一對一的關係, 這是時下受到關注和適宜的課題;就研究法而言, 本研究確是新穎獨創的。

Retailers are increasingly adding multiple platform apps. For instance, Hilton Hotel is listed on booking.com, Expedia and TripAdvisor. The purpose of this study is to examine whether and how the adoption of a second homogenous mobile platform app by new and existing consumers affects their purchasing behavior in both the original app and the overall platform apps.

With 604,864 unique data from a Chinese fast-food company, which sequentially add three food delivery platforms, this paper explores the influence of a second homogeneous mobile platform app adoption on consumer purchase frequency, order size and spending.

The results of the log-linear regression model show that multiplatform consumers are more profitable than single-platform consumers. For both existing and new consumers, multiplatform adoption would increase purchase frequency, decrease order size and increase total spending with the retailer. However, for existing consumers, multiplatform adopters are more likely to buy less frequently, spend less per order and have lower total spending in the original platform app.

This paper contributes to platform addition and multichannel literature by empirically finding that multiplatform adopters, both new and existing consumers, are more profitable than single-platform consumers. Managerially, the results suggest that companies should not hesitate to add multiple platforms and should encourage consumers to use multiple mobile apps.

First, this study examines the multiplatform addition effect on both new and existing consumers, which has not been discussed yet. Second, this study contributes to multichannel literature by finding that multiplatform consumers are more profitable than single-platform consumers. Third, unlike Rong et al. (2021), this study supports that channel capability theory is still valid in the homogenous mobile-to-mobile channel expansion context.

The paper aims to investigate the impact of political connections and board ethnicity on the value relevance of earnings and book value in Mauritius.

This study is based on a sample of 541 Mauritian-listed firm-year observations for 2001–2016. Financial and board diversity data have been collected using the listed firms’ annual reports and from reports published by the Stock Exchange of Mauritius. Political connection data was derived from the directory of Chief of State and Cabinet members. The research hypotheses were empirically tested using a modified Ohlson (1995) price model.

This study shows that political connections negatively impact the value relevance of earnings and book value. The authors find that firms with Franco-Mauritian directors will constrain political connections’ negative impact. The authors find contrasting results for Indo-Mauritian directors since they form an integral part of the government in Mauritius.

This study contributes to the scarce accounting literature in Mauritius. Firstly, no study has investigated the relationship between the value relevance of accounting information and political connections in Mauritius. Secondly, Mauritius’s capital market is dominated by a non-indigenous ethnic group, Franco-Mauritians, who remain the economic elite. Hence, Mauritius presents an opportunity to bring forth another important aspect in the capital market and corporate governance; diversity on the board of directors. Therefore, the study extends to the political connections and board diversity literature.

Prior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.

An empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.

The analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.

This study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

Italy has been the first Western Country to suffer a massive outbreak of COVID-19. Starting from the 11^th of March 2020, the Italian Government approved a series of emergency restrictive measures to limit people’s movement and social contacts. The aim of this short paper is to test if the number of norm-violations (related to people’s movement) might contribute to the peaks of new COVID-19 positives after few days. We show that peaks in the violations of the lockdown norms correspond to peaks in new positive cases about 6 days later.

This study aims to estimate the quality-adjusted changes in residential mobile consumer prices by controlling for the changes in the relevant service characteristics and quality, in a case study on Hungary between 2015 and 2021; compare the results with changes measured by the traditionally calculated official telecommunications price index of the Statistical Office; and discuss separating the hedonic price changes from the effect of a specific government intervention that occurred in Hungary, namely, the significant reduction in the value added tax rate (VAT) levied on internet services.

Since the price of commercial mobile offers does not directly reflect the continuous improvements in service characteristics and functionalities over time, the price changes need to be adjusted for changes in quality. The authors use hedonic regression analysis to address this issue.

The results show significant hedonic price changes over the observed seven-year period of over 30%, which turns out to be primarily driven by the significant developments in the comprising service characteristics and not the VAT policy change.

This paper contributes to the literature on hedonic price analyses on complex telecommunications service plans and enhances this methodology by using weights and analysing the content-related features of the mobile packages.

The world over is becoming urbanized, and people are migrating to cities in large numbers in search of opportunities. The increased urbanization has posed challenges such as congestion, rising crime, and growing urban poverty. The governments respond by providing amenities such as schools, hospitals, and housing to meet to increase in demand for these facilities. However, there is a need for the provision of facilities that meets the expectations of the people, particularly on the proximity of amenities and bundles of utility-bearing housing characteristics. In an attempt to address the challenge mentioned, the study estimated the hedonic characteristics influencing the willingness to accept and willingness to pay for housing facilities in the Eastern Cape Province, South Africa. Using a multiple linear regression model and artificial neural network, the study found out that properties with a bathroom, garage and large floor size have a higher value compared to properties without these facilities.When making decisions to acquire a property, buyers consider the availability of discounts and the prevailing property price. Overall, willingness to pay and accept decisions are mainly determined by location and the price at which homogeneous neighborhood properties were sold. Therefore, the study recommends that urban town planners and other housing authorities prioritize the construction of properties with larger floor areas, parking bays, and bathrooms using a cost-effective mechanism that makes the properties affordable to residents.

This study aims to examine the impact of oil consumption on carbon dioxide (CO₂) emissions and total factor productivity (TFP) in highly oil-consuming countries of the world from 1995 to 2019.

For this purpose, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied.

FMOLS and DOLS models reveal that oil consumption, human capital, population, trade openness and nonrenewable energy have a significant positive effect on CO₂ emissions. While information and communication technology (ICT), as proxied by mobile and natural resources, has a significant negative effect on CO₂ emissions. In the case of TFP, oil consumption, ICT and natural resources have a significant positive effect on the TFP. On the other hand, trade openness, population, human capital and nonrenewable energy have a significant negative effect on TFP. The results of this study can help to provide policy recommendations to reduce CO₂ emissions in studied highly oil-consuming countries of the world.

Due to the threat to sustainable development, climate change has become a major topic for debate around the world. The influence of oil consumption on CO₂ emission and TFP is less known in the available literature. Another significance of this study is that many researchers considered aggregate energy consumption to study this relationship, but the authors have studied the effect of energy consumption, particularly from oil in the top oil-consuming countries, which is a significant shortcoming of the present research.

Global warming exacerbates sea level rise and extreme weather events that cause severe flooding, resulting in lost productivity and property damage. To reduce the impact of flooding, residents are avoiding purchasing homes in high-risk areas. There are numerous studies on the relationship between flood hazards and housing prices in developed countries, but few in developing countries. Therefore, this study aims to investigate the relationship between flood hazards and housing prices in Hat Yai, Songkhla, Thailand.

This study uses spatial-lag, spatial error and spatial autoregressive lag and error (SARAR) models to analyze the effect of flood risk on property prices. The main analysis examines the degree of flood risk and housing rental prices from our survey of 380 residences. To test the robustness of the results, the authors examine a different data set of the same samples by using the official property valuation from the Ministry of Finance and the flood risk estimated by the Southern Natural Disaster Research Center.

The SARAR model was chosen for this study because of the occurrence of spatial dependence in both dependent variable and the error term. The authors find that flood risk has a negative impact on property prices in Hat Yai, which is consistent with both models.

This study is one of the first to use spatial econometrics to analyze the impact of flood risk on property prices in Thailand. The results of this study are valuable to policymakers for benefit assessment in cost–benefit analysis of flood risk avoidance or reduction strategies and to the insurance market for pricing flood risk insurance.

The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.

The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al. (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model.

The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy.

The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation.

Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.