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Digital twins provide enormous opportunities for smart buildings. However, an up-to-date intellectual landscape to understand and identify the major opportunities of digital twins for smart buildings is still not enough. This study, therefore, performs an up-to-date comprehensive literature review to identify the major opportunities of digital twins for smart buildings.

Scientometric and content analysis are utilised to comprehensively evaluate the intellectual landscape of the general knowledge of digital twins for smart buildings.

The study uncovered 24 opportunities that were further categorised into four major opportunities: efficient building performance (smart “building” environment), efficient building process (smart construction site environment), information efficiency and effective user interactions. The study further identified the limitations of the existing studies and made recommendations for future research in the methodology adopted and the research domain. Five research domains were considered for future research, namely “real-time data acquisition, processing and storage”, “security and privacy issues”, “standardised and domain modelling”, “collaboration between the building industry and the digital twin developers” and “skilled workforce to enable a seamless transition from theory to practice”.

All stakeholders, including practitioners, policymakers and researchers in the field of “architecture, engineering, construction and operations” (AECO), may benefit from the findings of this study by gaining an in-depth understanding of the opportunities of digital twins and their implementation in smart buildings in the AECO industry. The limitations and the possible research directions may serve as guidelines for streamlining the practical adoption and implementation of digital twins for smart buildings.

This study adopted scientometric and content analysis to comprehensively assess the intellectual landscape of relevant literature and identify four major opportunities of digital twins for smart building, to which scholars have given limited attention. Finally, a research direction framework is presented to address the identified limitations of existing studies and help envision the ideal state of digital twins for smart buildings.

This paper aims to investigate the cultural meanings of excessive drinking in three different countries with different levels of alcohol use chosen as case studies of wider geographies representing Northern (Denmark), Southern (Italy) and Eastern (Estonia) Europe.

Data were collected according to the Reception Analytical Group Interview method, using video clips as stimuli to enhance comparability. Eight online focus groups were organized in each country for a total number of 128 participants. Symbolic boundaries defining what drinking patterns are socially acceptable were then analysed to look at cross-national variations.

Results show how different conceptualizations of excessive drinking persist, although a convergence process among drinking patterns is also observed, which suggests that differences mainly depend on meanings and values attributed to intoxication. These are both rooted in the traditional drinking cultures and affected by ongoing social and economic change processes.

Because of the chosen research approach, the research results may lack generalizability, even at country level, as there are differences also within the same drinking culture; however, addressing these differences was beyond the scope of the present study, which aimed to contribute to understanding persisting differences in European drinking culture despite different drivers seem to act for globalization of drinking habits.

The paper includes implications for the development of tailored and effective prevention messages, considering rooted attitudes and cultural values attached to drinking and drunkenness in different European geographies, which are also related to conceptualizations of risks and pleasure.

This paper fulfils an identified need to understand persisting differences in alcohol-related behaviours and outcome in different European countries emerging from quantitative data.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The purpose of this study is to examine the buyers’ preferences influencing the purchase of privately developed affordable housing in Kolkata and to determine whether unsold houses result from misalignment with these preferences.

The literature review and user-opinion survey identified 119 independent variables that indicate buyers’ preferences. A questionnaire survey of 383 households in affordable housing units from 32 housing complexes in Kolkata recorded buyers’ preferences and satisfaction against the independent variables grouped under five levels of characteristics. The product weights of variables derived from the rank sum method and percentage satisfaction give the Utility Score. Multivariate regression and univariate linear regressions were conducted to determine the significance of each Level of characteristics and each variable, identifying the significant variables that would affect the sale of affordable houses.

The multivariate regression analysis has indicated that 68.56% of the variation in the percentage of unsold houses was explained by the five utility scores, which affirms that misalignment with buyers’ preferences significantly affects the sale of privately developed affordable houses. Furthermore, building and neighbourhood-level utility show the highest significance as predictors, while city-level and miscellaneous utility have moderate significance, but housing complex-level utility lacks statistical significance.

This study addresses a research gap in privately developed affordable housing in Kolkata, enhancing understanding of buyer preferences in this segment.

The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables.

A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.

The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots.

The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education.

T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.

This paper aims to empirically investigate the extent to which interdependence in markets may be driven by COVID-19 effects.

The current global COVID-19 pandemic is adversely affecting the oil market (West Texas Intermediate) and crypto-assets markets.

The authors find that the dependence structure changes significantly after the global pandemic, providing valuable information on how the COVID-19 crisis affects interdependencies. The results also prove that the performance of digital gold seems to be better compared to stablecoin.

The authors fit copulas to pairs of before and after returns, analyze the observed changes in the dependence structure and discuss asymmetries on propagation of crisis. The authors also use the findings to construct portfolios possessing desirable expected behavior.

Crypto-asset can be traded on many different exchanges worldwide with servers located in countries with different financial characteristics and institutional surroundings. Trading volume on these servers varies considerably regarding the server’s location, even though the prices do not differ greatly. Crypto-asset markets are poorly regulated and, as such, may leave a place for potential fraudulent activities and be linked to corruption. This paper aims to examine the role of country’s institutions in attracting Bitcoin traders.

Assuming heterogeneity between countries where crypto-asset exchange servers are located, the Pool Mean Group Estimator is used.

Results indicate that, from institutional variables, corruption in the country attracts while internal and external conflicts repel investors. Additionally, the growth of global uncertainty and the decline in the local stock markets motivate investors to trade Bitcoin.

Previous research has empirically proved the importance of institutions’ quality for financial market development. This paper goes one step further and tries to empirically confirm the theoretical assumptions and investigate in detail the role of institutions in choosing servers in a particular country for Bitcoin trading.

This study considers transitive service triads, which consist of three dyads formed by three actors: supplier, logistics service provider and customer, who remain directly linked by one or more of the upstream and downstream flows of products, information and finances. This paper aims to explore the link between information governance, decentralized information technologies and supply chain self-organization, and their resulting impact on network performance in the transitive service triads.

Drawing upon the tenets of the theory of complex adaptive systems and supply chain practice view, this paper involves an empirical investigation that uses survey data gathered from transitive service triads in the European countries. The study uses partial least squares structural equation modeling to estimate the formative-reflective hierarchical component model and test the research hypotheses.

Information governance defines how supply chain information flows are controlled, accessed and used by a focal organization and its business partners. As empirically evidenced in this study, it can be depicted as a latent construct consisting of three distinct dimensions of information custody, information ownership and right to data access. Likewise, the study also indicates that supply chain self-organization, as a second-order construct, consists of three interactive self-organization actions undertaken by specific firms participating in the triadic arrangement. Supply chain self-organization is thus produced by firms that are reciprocally interrelated and interacting, having effects on one another. Furthermore, the study also highlights that information governance creates an environment for applying decentralized information technologies, which then positively affects supply chain self-organization. Finally, the research also empirically operationalizes the construct of network performance within the transitive service triads.

Although the results provide several major contributions to theory and implications for practitioners, the study still demonstrates some methodological constraints. Specifically, although the study uses a relatively large research sample of 350 transitive service triads, it still focuses only on a selected group of industries and is limited to investigating solely a particular type of service triads.

Given the increasing interest in investigating triads, this study examines how information governance and decentralized information technologies support supply chain self-organization to yield network performance in transitive service triads.

This paper verifies whether popular Internet information from Internet forum and search engine exhibit useful content for forecasting the volatility in Chinese stock market.

First, the authors’ study commences with several HAR-RV-type models, then the study amplifies them respectively with the posting volume and search frequency to construct HAR-IF-type and HAR-BD-type models. Second, from in-sample and out-of-sample analysis, the authors empirically investigate the interpretive ability, forecasting performance (statistic and economic). Third, various robustness checks are utilized to reconfirm the authors’ findings, including alternative forecast window, alternative evaluation method and alternative stock market. Finally, the authors further discuss the forecasting performance in different forecast horizons (h = 5, 10 and 20) and asymmetric effect of information from Internet forum.

From in-sample perspective, the authors discover that posting volume exhibits better analytical ability for Chinese stock volatility than search frequency. Out-of-sample results indicate that forecasting models with posting volume could achieve a superior forecasting performance and increased economic value than competing models.

These findings can help investors and decision-makers obtain higher forecasting accuracy and economic gains.

This study enriches the existing research findings about the volatility forecasting of stock market from two dimensions. First, the authors thoroughly investigate whether the Internet information could enhance the efficiency and accuracy of the volatility forecasting concerning with the Chinese stock market. Second, the authors find a novel evidence that the information from Internet forum is more superior to search frequency in volatility forecasting of stock market. Third, they find that this study not only compares the predictability of the posting volume and search frequency simply, but it also divides the posting volume into “good” and “bad” segments to clarify its asymmetric effect respectively.

This study aims to verify whether posting volume and search frequency contain predictive content for estimating the volatility in Chinese stock market.

The forecasting model with posting volume can achieve a superior forecasting performance and increases economic value than competing models.

The results are robust in alternative forecast window, alternative evaluation method and alternative market index.

The posting volume still can help to forecast future volatility for mid- and long-term forecast horizons. Additionally, the role of posting volume in forecasting Chinese stock volatility is asymmetric.