Although expressing positive emotions is typically socially rewarded, in the present work, we predicted that people suppress positive emotions and thereby experience social benefits when outperformed others are present. We tested our predictions in three experimental studies with high school students. In Studies 1 and 2, we manipulated the type of social situation (outperformance vs. non-outperformance) and assessed suppression of positive emotions. In both studies, individuals reported suppressing positive emotions more in outperformance situations than in non-outperformance situations. In Study 3, we manipulated the social situation (outperformance vs. non-outperformance) as well as the videotaped person’s expression of positive emotions (suppression vs. expression). The findings showed that when outperforming others, individuals were indeed evaluated more positively when they suppressed rather than expressed their positive emotions, and demonstrate the importance of the specific social situation with respect to the effects of suppression.
While the be-perfect (BEP) is found in a number of (particularly European)
languages, in English it has almost exclusively been considered from a diachronic
perspective, and is commonly seen as a dying structure in present-day
varieties. In contrast, previous acceptability and corpus studies have indicated
that the BEP persists as a formal variant. Against the backdrop of this apparently
conflicting evidence, in this paper I take a closer look at this “zombie” structure.
With the help of data from the International Corpus of English (ICE) and the Corpus
of Global Web-based English (GloWbE), I aim at updating and sharpening the synchronic
perspective on the BEP. In particular, I (re-)address whether the BEP has
really “died” or rather still represents a productive pattern in present-day varieties
of English. In this connection, lexical restrictions are considered as important
indicators, and the role of different factors favoring the BEP as well as its potential
status as a vernacular universal are discussed.
Among the time-reference forms of English, the acquisition of the
present perfect is regarded as the single most challenging task for non-native
speakers, mainly due to the semantic peculiarities of this form in contrast to
many other languages. In this chapter, we focus on variation between the present
perfect and the simple past in German-speaking learners to assess the influence
of learner proficiency level and mode on the use of the present perfect. Our results
suggest that (i) the present perfect is more frequent in writing, and (ii) emerges
very late in learner language, such that only the most advanced learners, and
specifically those who started learning English very early in primary school, use
it as frequently as native speakers. This finding tentatively supports proponents
of early English language teaching. To explain the results, we explore potential
reasons for the late emergence of the present perfect, and conclude with specific
recommendations for English language teaching.
This paper presents a supervised classification model, where the indicators of correlation between dependent and independent variables within each class are utilized for a transformation of the large-scale input data to a lower dimension without loss of recognition relevant information. In the case study, we use the consumption data recorded by smart electricity meters of 4200 Irish dwellings along with half-hourly outdoor temperature to derive 12 household properties (such as type of heating, floor area, age of house, number of inhabitants, etc.). Survey data containing characteristics of 3500 households enables algorithm training. The results show that the presented model outperforms ordinary classifiers with regard to the accuracy and temporal characteristics. The model allows incorporating any kind of data affecting energy consumption time series, or in a more general case, the data affecting class-dependent variable, while minimizing the risk of the curse of dimensionality. The gained information on household characteristics renders targeted energy-efficiency measures of utility companies and public bodies possible.
Energy utility companies hold a great potential to improve sustainable practices, as they have access to large amounts of energy consumption data that can be translated into actionable insights toward individual consumers. We investigate the role of energy informatics based artifacts for utility companies to (i) profile households and to individually address customers that have a large potential to elevate their energy efficiency and (ii) at the same time create revenue by purchasing energy efficient goods and related services. Furthermore, we demonstrate how the predicted attributes of millions of households can, in turn, contribute to the economic, environmental, and societal value creation for energy suppliers, utility customers, and the community as a whole. For our empirical study on the detection of households with old heating systems, we cooperated with a large energy supplier in Belgium. The results are used for targeted positioning of new heating systems in a cross-selling campaign and may serve as a blueprint for related data analytics artifacts in this field.
Utility companies generally have an extensive customer base, yet their knowledge about individual households is small. This adversely affects both the development of innovative, household specific services and the utilities’ key performance indicators such as customer loyalty and profitability. With the goal to overcome this knowledge deficit, persuasive systems in the form of customer self-service applications and efficiency coaching portals are becoming the getaway of data exchange between utility and user. While improved customer interaction and the collection of customer data within respective information systems is an important step towards a service-oriented company, the immediate value generated from the collected data is still limited, mostly due to the small fraction of customers actually using such systems. We show how to utilize the knowledge gained from the sparse number of active web users in order to provide low-cost and large-scale insights to potentially all residential utility customers. We do so using machine-learning-based Green IT artifacts that allow for improving decision-making, effectiveness of energy audits, and conservation campaigns, thus ultimately increasing the customer value and adoption of related services. Moreover, we show that data from the publically available geographic information systems can considerably improve the decision quality.