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Practitioners and academics are starting to recognize the benefits of green IT/IS practices. Despite these benefits, this study aims to know more regarding the factors that would drive organizations to use green IT/IS practices within their IT function and across the enterprise. To further understanding in this area, this study applies a strategic cognition framework of firm responsiveness and institutional theory to determine the extent to which an organization uses green IT/IS practices in response to stakeholder concerns. This study investigates the extent to which two organizational logics – expressive and instrumental – and three institutional pressures – coercive, mimetic and normative – jointly affect an organization's use of both green IT practices and green IS practices.

This study tested the hypotheses with survey data collected from 306 organizations. Structural equation modeling was used for data analysis.

Findings support four joint effects: (1) individualistic identity orientation and coercive pressure positively affect green IT practices; (2) collectivistic identity orientation and normative pressure positively influence green IS practices; (3) cost reduction orientation and mimetic pressure positively affect green IT practices; and (4) revenue expansion orientation and normative pressure positively influence green IS practices.

This study contributes to the literature by providing evidence for joint drivers of green IT and green IS practices. Green IT and IS practices represent organizations' different levels of commitment to environmental sustainability and responsiveness to stakeholders (i.e. green IT/IS practices). Organizations of different expressive and instrumental orientations are attuned to institutional pressures to various degrees, which leads to different green IT/IS practices.

This study aims to analyze the effect of cryptocurrency capitalization market development on bank deposits variability in the United Arab Emirates (UAE) spanning the period 2005M1–2020M4 using the novel nonlinear autoregressive distributive lag (NARDL).

The study employs the NARDL recently developed by Shin et al. (2014) to estimate the long and short-run relationships between the variables rather than the widely known ARDL (Pesaran et al., 2001), which suffers from a complex structure in the estimation equation that usually includes lags and differences in both short and long terms. The implementation of NARDL required several proceedings after plotting the descriptive data, commencing with unit root tests, selection of lag length, estimating the long-and-short variables coefficients, heteroscedasticity test and Wald test for symmetries.

The long-run estimations of the positive and negative asymmetric coefficients indicate that cryptocurrencies capitalization has a negative impact on bank deposits in the UAE. Further, the short-run estimations coefficients exhibit that both significant positive and negative partial sum squares of cryptocurrencies decrease bank deposits.

The study has applied to the UAE spanning the period 2005M1–2020M4 using the NARDL.

The short-run estimations coefficients exhibit that both significant positive and negative partial sum squares of cryptocurrencies decreases bank deposits, which means that the increase in the magnitude of cryptocurrencies capitalization stimulates depositors and speculators to adjust their portfolios towards contracting their deposits in banks to invest partially in cryptocurrencies, on the other hand, the decline in cryptocurrencies capitalization process spur depositors and speculators to reduce their deposits for purchasing cryptocurrencies at lower prices.

The study infers that individuals and businesses are cautious when investing in cryptocurrencies, and they need more certainty and trust to include these types of assets in their portfolios. The fluctuation in cryptocurrencies capitalization prompts speculators to change their deposits according to the cryptocurrencies' prices.

This study explores the short-and long-run asymmetric impacts of cryptocurrencies capitalization development on bank deposits volatility in the UAE, based on a NARDL, for providing a manifest depiction of whether the cryptocurrencies industry might be a threat to conventional banking system performance in the potential future.

This study aims to investigate the effects of hotel customers’ perceived utilitarian and hedonic values on their intention to use service robots. In addition, the influences of innovativeness, ease of use and compatibility on hotel customers’ perceived utilitarian and hedonic values were examined.

The data of the current study was collected from 11 countries including the USA, UK, Turkey, Spain, Romania, Japan, Israel, India, Greece, Canada and Brazil. A structural equation modeling was used to test the study hypotheses.

The results indicated that hotel customers’ intention to use service robots was positively influenced by their utilitarian and hedonic value perceptions. In addition, customers’ perceptions of robots’ ease of use and compatibility had a positive impact on their perceived utilitarian and hedonic values.

The findings of the current study provide unique contributions in the context of hospitality robotics technology adoption literature. In addition, this study provides valuable insights and novel opportunities for hospitality decision-makers to capitalize on, as they strive to strategize the integration of robot-based services into their operations.

本研究调查了酒店顾客感知功能性价值和享乐性价值对服务机器人使用意向的影响。此外, 本研究考察了创新性、易用性和兼容性对酒店顾客感知功能性价值和享乐性价值的影响。

本研究的数据来自美国、英国、土耳其、西班牙、罗马尼亚、日本、以色列、印度、希腊、加拿大和巴西等十一个国家, 采用结构方程模型（SEM）对研究假设进行测试。

结果表明, 酒店顾客使用服务机器人的意向受到他们对功能性价值和享乐性价值的感知的积极影响。此外, 机器人易用性和兼容性对功能性价值和享乐性价值有积极影响。

本研究的发现对酒店行业机器人技术应用文献提供了独特的贡献。此外, 本研究为酒店业的决策者提供了宝贵的见解和新机遇, 使他们能够在将机器人服务的优势整合到酒店运营中。

This paper aims to investigate and formulate several business models (BM) for various energy communities (EC) members: prosumers, storage facilities, electric vehicle (EV) charging stations, aggregators and local markets.

One of the flexibility drivers is triggered by avoiding the cost and maximizing value that consists of delivering a service such as increasing generation or reducing consumption when it is valued most. The transition to greener economies led to the emergence of aggregators that aggregate bits of flexibility and handle the interest of their providers, e.g. small entities such as consumers, prosumers and other small service providers. On one hand, the research method consists of formulating six BM and implementing a BM that includes several consumers and an aggregator, namely, scheduling the household electricity consumption (downstream) and using flexibility to obtain revenue or avoid the cost. This is usually performed by reducing or shifting the consumption from peak to off-peak hours when the energy is cheaper. Thus, the role of aggregators in EC is significant as they intermediate small-scale energy threads and large entities' requirements, such as grid operators or retailers. On the other hand, in the proposed BM, the aggregators' strategy (upstream) will be to minimize the cost of electricity procurement using consumers’ flexibility. They set up markets to buy flexibility that is valued as long as their costs are reduced.

Interesting insights are revealed, such as when the flexibility price doubles, the deficit coverage increases from 62% to 91% and both parties, consumers and retailers obtain financial benefits from the local market.

One of the limitations of using the potential of flexibility is related to the high costs that are necessary to implement direct load control. Another issue is related to the data privacy aspects related to the breakdown of electricity consumption. Furthermore, data availability for scientific research is limited. However, this study expects that new BM for various EC members will emerge in the future largely depending on Information Communications and Technology developments.

An implementation of a local flexibility market (LFM) using 114 apartments with flexible loads is proposed, demonstrating the gains obtained from trading flexibility. For LFM simulation, this study considers exemplifying a BM using 114 apartments located in a multi-apartment building representing a small urban EC situated in the New England region in North America. Open data recorded in 2016 is provided by UMassTraceRepository.

As a novelty, six BM are proposed considering a bottom-up approach and including various EC members.

The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.

We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.

Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.

Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.

In this study, the authors propose a confirmatory factor analysis (CFA) to create a tenable measurement model and identify the factors that have the potential to enhance awareness of pro-environmental measures. The successful implementation of demand response (DR) programs and their required infrastructure is significant for moving towards green energy communities and a better environment for living. Not only can renewable energy capacities contribute to this desideratum, but also electricity consumers who, until the last decade, have played a passive role.

To answer these questions, a complex data set of 243 post-trial questions created by the Irish CER are analyzed using first-order and hierarchical CFA models with several SAS procedures (PROC CALIS, MIANALYZE). The questionnaire was launched to over 3,000 electricity consumers from Ireland that were participants to a trial program after the installation of smart metering systems and implementation of DR programs.

The effect of five latent factors – positive attitude, negative attitude, perceived impact of own actions, price- and incentive-DR programs – is measured. With a bi-factor CFA measurement model, the authors assess that they significantly influence the electricity consumers' awareness.

However, these findings have to be backed up by relevant information and simulations showing consumers benefits in exchange to their efforts. They have research implications on the design of the business models and DR programs pointing out the importance of benefits and fairness of value sharing mechanisms within energy communities.

Thus, the electricity consumers may change their consumption behavior as they positively perceive the implementation of DR programs.

This paper fulfills an identified need to study post-trial questionnaire and reveal latent factors that make electricity consumer change their behavior.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Audit consortium of joint and dual audits is one of the most controversial mechanisms aimed at improving audit quality and resolving several related debatable issues. This study aims to empirically investigate the impact of audit consortium on audit quality assessment in Egypt. It specifically examines whether audit opinion modification level is triggered by joint and dual audits existence and whether it is influenced by the relative importance of the auditor pair combination types.

A sample of companies listed on the Egyptian Stock Exchange constituting the EGX 30 index is examined over a period of five years, from 2016 to 2020. A quantitative research methodology is used, using content analysis of companies’ audit reports and carrying out longitudinal panel ordinary least squares multiple regression tests.

Results show that audit quality is significantly enhanced by conducting joint and dual audits of Egyptian companies’ financial statements. Findings indicate that both joint and dual audits significantly increase auditors’ propensity to modify audit opinions as compared to companies that engage in single audits. However, this increase in audit quality is not supported by the presence of Big 4 joint auditors or affiliated joint auditors, while the impact of Big 4 dual auditors cannot be confirmed. Nevertheless, such a potential increase in audit opinion modification is boosted by the presence of affiliated dual auditors, which appears to translate into higher quality.

The study has important implications for researchers, corporates, those charged with governance, financial statement users, auditors, regulators and standard setters, who might be interested in whether an audit consortium and a particular auditor pair combination are associated with superior audit quality. It provides empirical evidence that might contribute to the continuous challenge of promoting the quality and effectiveness of the external audit.

This study adds to the relatively limited and challenging literature on the potential contribution of audit consortium, using audit opinion modification level as a direct assessment of audit quality. It extends the scope of prior research by examining the existence of joint and dual audits and the relative importance of joint and dual auditor pair combination types. The study provides key insights from a distinctive and complex emerging audit market.

Credit Default Swap (CDS) trading alters equilibrium interactive monitoring of external corporate monitors due to a possible change in private lenders' incentive to monitor client firms. This study explores how audit fees change in response to CDS trade initiation on client firms and how this effect is moderated by investor protection.

With 6,052 cross-country firm observations, the author conducts estimations in the systems dynamic general methods of moments framework.

The author documents that audit fees rise on average after CDS trade initiations with and/or without investor protection. Meanwhile, change in auditors' risk perception result in increased audit costs when CDS trade initiation and investor protection interact. The effect of CDS trading on audit fees remain after controlling for firm, audit, and auditor features are robust to different proxies of audit cost.

The need for firms in high investor protection jurisdictions to initiate CDS trade to implement policies in order to maximize their gains from investor protection activities to lessen the overall impact of any increased audit cost that may arise. Furthermore, CDS regulation may be strategically targeted to lessen the effect of increased audit costs on firms after initiation. This would ensure that the resulting increase in audit cost may not materially impact the cash or profitability position of such firms.

This study is distinct from previous ones by focusing on variation in private lenders incentive to monitor after CDS trade initiation after controlling for possible monitoring by short-term creditors. Given that monitoring is not costless for private lenders and CDS trading on their borrowers causes a change in this cost structure, the author documents how auditors react to such changes in incentive to monitor.

信用違約互換交易會改變外部監督機制的均衡互動監測，這是因為私人貸款者去監控客戶公司的激勵可能有所改變。本研究擬探究審計費用如何改變，以應對向客戶公司進行的信用違約互換交易啟動;研究亦探討投資者保障、如何緩和上述的影響。

我們透過6,052個穿越全國的企業觀察，進行了對系統動力廣義矩估計體系的估測。

無論投資者保障存在與否，信用違約互換交易啟動必帶來審計費用一般的平均升高，我們已把這關聯記錄下來。同時，當信用違約互換交易啟動和投資者保障兩者互相影響時，審計員的風險認知的改變，是會導致審計費用增加的。若拔除公司和審計的影響，信用違約互換交易對審計費用的影響會保持不變;而且，就各個不同的審計費用代理權而言，審計員特點是牢固的。

本研究的結果，確定了若公司屬高投資者保護管轄權的類別，則有需要去啟動信用違約互換交易來實施政策，其目的為能從投資者保障的行動中取得最大的收益，從而減弱審計費用的增加所帶來的全面影響。再者，信用違約互換的管理或許可戰略性地訂立目標，俾能減弱於啟動後，審計費用的上昇對公司帶來的影響;這或會確保審計費用的增加、不會對有關公司的貨幣頭寸或盈利狀況產生重大的影響。

本研究有別於從前的研究，因它的焦點在於短期債權人可能的監督的影響給拔除的情況下，在信用違約互換交易啟動後，以監督為目的私人貸款者激勵的變化。鑒於對私人貸款者來說，監督不是不需要成本的;而且，為他們的借貸者的信用違約互換交易會為這個成本結構帶來變化，我們記錄了審計員如何對以監督為目的的激勵的有關改變作出回應。

This paper analyzes the value creation process in terms of the relationships between institutions, technology, integration of resources and contextual value. The study was conducted within an online learning setting in higher education, and utilized service-dominant logic as a basis for analysis.

A total of 349 responses were collected through an online survey. After removing data from respondents who did not meet the criteria and outliers, 280 responses were analyzed. Furthermore, six hypotheses were tested using structural equation modeling (SEM) techniques.

The results confirm that institutions are proven to influence technology and resource integration. The technology significantly affects resource integration and value-in-context. Likewise, resource integration determines value-in-context remarkably. On the other hand, this study found no evidence of the impact of institutions on value-in-context.

The study has been conducted in the Jabodetabek area, with a sample size of only 280. An extensive survey, including a larger sample size, may reveal a broader glimpse of the value co-creation process of students in higher education institutions. Only three antecedents of contextual value have been explored, namely institutions, technology and resource integration. More strengthening and detailed findings could be derived if the antecedents of the contextual value addressed could be added. In the sampling, the researchers have used non-probability sampling for collecting data due to various constraints. The use of the probabilistic sampling method might have given some new insights to the study and made the sample more representative. The convenient sampling method employed in this study may limit the generalization of this study's findings. Therefore, the findings of the hypothesis test only apply to the selected sample data. Another limitation of the study is that the survey respondents represented an urban Indonesian perspective. So, replication of this study in different areas (e.g. west, east and central Indonesia) would help to generalize the findings. In this study, there is no evidence that institutions have a direct impact on contextual value. The authors suggest reexamining the relationship between institutions and contextual value in future studies.

In particular, the authors have succeeded in designing a new empirical model in higher education based on the perspective of service-dominant logic (S-D logic). This finding further strengthens the existence of the perspective of S-D logic as a new general theory of the market.