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As smart technologies become an integral part of real estate in smart cities, the purpose of this paper is to explore the impact of ubiquitous computing on space users in smart real estate.

The analysis builds on two fields of knowledge rarely referenced in real estate studies: computer sciences and social sciences. The paper starts by analysing the idiosyncrasies of a new type of space user in smart real estate, known as the Cyber-dasein in reference to Heidegger’s phenomenology. The Cyber-dasein serves as an archetypical space user in smart environments.

The paper introduces digital-time as a new realm of real estate, and discusses the use of “experienced utility” in hedonic pricing models of smart real estate. It concludes by advocating a multidisciplinary collaborative approach for future research on real estate in smart environments.

There is a need for the real estate sector to decide on a metric for the new digital dimension of real estate owing to the implementation of smart technologies in the built environment.

This is the first research paper on this important topic. It is totally original and new.

In previous research (Abou-Zeid et al., 2008), we postulated that people report different levels of travel happiness under routine and nonroutine conditions and supported this hypothesis through an experiment requiring habitual car drivers to switch temporarily to public transportation. This chapter develops a general modeling framework that extends random utility models by using happiness measures as indicators of utility in addition to the standard choice indicators, and applies the framework to modeling happiness and travel mode switching using the data collected in the experiment. The model consists of structural equations for pretreatment (remembered) and posttreatment (decision) utilities and explicitly represents their correlations, and measurement equations expressing the choice and the pretreatment and posttreatment happiness measures as a function of the corresponding utilities. The results of the empirical model are preliminary but support the premise that the extended modeling framework, which includes happiness, will potentially enhance behavioral models based on random utility theory by making them more efficient.

By adopting retrospective evaluation theories, this study aims to explain how innovations provided by separate suppliers in the tourism value chain influence tourist’s perceived value of the overall experience and further uncover which innovative product attributes are more effective in improving tourist perceptions of the overall value.

A survey yielded 584 valid responses from tourists who had experienced specific tourist product innovations during their travels. Structural equation modelling was used to test the proposed theoretical model.

The results reveal that tourists evaluate overall travelling experience value either by recalling an intense, impressive moment (i.e. a heuristic approach) or through an evaluation of the overall utility gained from the whole trip (i.e. a normative approach). Furthermore, innovations that are perceived as increasing convenience and enabling learning contribute to tourists’ overall value perception through both normative and heuristic approaches, while immersion resulting from innovation only contributes to overall perceived value through the heuristic approach.

Given the complex service ecosystem of tourism destinations, each tourism service provider should consider how innovations contribute to the experience of the whole trip and which attributes of innovations increase tourists’ overall perceived experience value.

This study complements existing knowledge by revealing the relationship between product innovation in tourism sectors and tourists’ perceived value of the whole trip. Moreover, it offers a theoretical framework for further investigation into service product innovation in hospitality and tourism industry.

Through a portfolio choice model, the study empirically examines the influence of the heuristic simplification through peak-end rule (PER) and the associated neglect of the duration of the experience. The portfolio strategy adopted involves optimizing portfolios to capture the impact of heuristic-driven investors' experience of good and bad states. The study attempts to validate PER in an empirical context and is expected to generate trading rules, which would exploit pricing errors emerging out of the use of heuristics by investors.

The empirical approach adopted in the study primarily examines returns to portfolios sorted according to various hedonic evaluation rules. Behavioral portfolios are constructed using hedonic experiences as conditioning variables.

The results imply that there is continued investor demand for such assets in the short run. An equal weight portfolio based on a three-month hedonic evaluation earns an average monthly return of 2.77% over the next 12 months.

The authors’ study may perhaps be the first attempt to use the peak-end heuristic in portfolio construction.

To many observers, the area of utility analysis appears disjointed, unfocused, and they wonder where it is going. Despite the fact that there is almost 20 years of experience with utility analysis since researchers brought it into the modern era, this approach to cost/benefit analysis has not caught on widely and it is not a standard procedure in the assessment of HR interventions. I argue that utility analysis remains an appropriate area of inquiry, but currently it suffers from three broad sets of problems: (1)from an applied research perspective there is often a failure to focus on critical, value‐adding activities that managers regard as relevant and important to their own success; (2) inability to communicate the results of utility analyses in a persuasive, credible manner to operating executives; and (3) technical problems, both theoretical and operational. In my view, none of these is insurmountable, but we must address each one in a systematic manner if the potential of utility analysis to help guide organizational decision making is to be realized in practice.

This article aims to develop a new theory that can better explain and predict how and when humans interact with commercial robots. To this end, utility maximization theory (UMT) along with four principles and propositions that may guide how human-to-commercial robot interactions are developed.

This article conceptualizes UMT by drawing from social exchange, conservation of resources, and technology-driven theories.

This article proposes UMT, which consists of four guiding principles and propositions. First, it is proposed that the human must invest sufficient resources to initiate a human-to-commercial robot interaction. Second, the human forms an expectation of utility gain maximization once a human-to-commercial robot interaction is initiated. Third, the human severs a human-to-commercial robot interaction if the human is unable to witness maximum utility gain upon the interaction. Finally, once the human severs a human-to-commercial robot interaction, the human seeks to reinvest sufficient resources in another human-to-commercial robot interaction with the same expectation of utility maximization.

This article is one of the few studies that offers a theoretical foundation for understanding the interactions between humans and commercial robots. Additionally, this article provides several managerial implications for managing effective human-to-commercial robot interactions.

Disaster mutual assistance (DMA) or mutual aid is a reciprocal arrangement between organizations that permits and prearranges one company to access resources from another company to recover from disaster impacts faster. As a practical tool to access response resources quickly, DMA can be an important element of an effective emergency management process, but the decision to provide (or not to provide) DMA is challenging and involves a number of factors. The purpose of this paper is to present the results of a study conducted to identify DMA decision criteria and their weights based on electricity companies operating in North America.

The authors employed a combination of Delphi and analytical hierarchy process (AHP) methods. Delphi method identified the decision criteria that should be considered before electricity utilities enact their DMA agreements. A standard AHP calculated the weights of identified DMA criteria.

In total, 11 criteria were identified and classified into three main groups: responding criteria, requesting criteria and disaster criteria. Of the 11, “Emergency Conditions” within the responding criteria group, “Extent of Damage” of the requesting criteria group, and “Size of Disaster”, associated with the disaster criteria group, had the highest weight. Three other factors (“Work Safety Practice”, “Natural Hazards” and “Availability of Resources”) carried a noticeable weight difference, while the remaining factors were weighted relatively lower.

At present, a decision to provide mutual assistance is highly subjective, based on “gut feel”, and dependent on interpersonal relationships between the requestor and the provider. However, mobilizing and dispatching electricity industry crews is a risky and costly operation for both requesting and responding companies and requires careful assessment for which a cost-benefit threshold has not been developed. This cost-benefit perspective is often frowned upon owing to the intended altruistic nature of DMA agreements and its influence on decision makers. The developed criteria in this study are intended to assist electricity companies in making a more informed and quantifiable decision when deliberating a request for mutual assistance. These criteria may also be used by assistance-requesting companies to better identify electricity companies that are more likely to provide assistance to them.

This study contributes to the literature by examining the current state of DMA in electricity utilities, identifying decision criteria and weighing such criteria to enable electricity companies in making more objective decisions, thereby, increasing the overall effectiveness of their disaster management process.

This paper aims to examine whether the instructional message used to introduce game-based learning (GBL), prior knowledge, flow and prior gaming experience have an impact on secondary students’ learning outcomes, perceived game utility and motivation to use the game.

Fifty-four 9th grades students enrolled in this quasi-experimental study. Both groups played the same game. The experimental group was told that they would be playing a game, whereas the control group was told that they would be doing an exercise. The game was designed to promote knowledge recall about familiar mathematical concepts. Prior knowledge was assessed. An immediate and a two-days delayed post-evaluated learning outcomes. Flow, prior gaming experience, perceived game utility and motivation to use the game were assessed with questionnaires after playing.

Introducing the GBL activity as a game increased immediate learning outcomes for low knowledgeable students, but these benefits did not extend to longer term. Indeed, when the GBL activity was presented as an exercise, low higher knowledgeable students obtained poorer longer-term learning outcomes than more knowledgeable ones, whereas no difference appeared when the GBL was introduced as an exercise. Prior gaming experience and the type of instructional message used to introduce a GBL activity positively influenced perceived game utility and motivation to use the game.

This study is the first to highlight that the way a GBL activity is framed affects differently low and higher knowledgeable students’ learning outcomes and influences perceived game utility and motivation to use the game.

The most commonly described components of customer experience include cognitive and affective aspects. However, the subjective self-reported methods traditionally applied in tourism research cannot fully represent the instant, dynamic, and affective nature of customer experience. Therefore, there is a need for moment-based approaches and longitudinal methods in tourism research. The chapter provides a selective review of measures that can be used to assess the affective aspects of customer experience. Taking into account the advantages and limitations of each method, the integration of self-reported scales, moment-based psychophysiological techniques, and longitudinal methods should be considered as the best approach to measuring affective components of customer experience in tourism. This holistic interdisciplinary approach will help researchers and tourism practitioners understand the relationship between affective and cognitive components of tourists' pre-visit, onsite, and post-visit experience, as well as evaluate the effectiveness of marketing campaigns, identify weak points of tourists' customer journey, and maximize total travel experience.

As demand mounts for a review of the UK’s privatized utilities regulatory system, focuses on an increasing public phenomenon ‐ that of conflict between regulators and regulatees. Identifies the factors contributing to the difficulties which the regulators face in carrying out their roles. Utilizes the benefit of hindsight to reach conclusions as to why the current attacks on the roles of the regulators are natural outcomes, flowing from the ways in which regulatory systems have been designed.