Search results

This study aims to evaluate the usability of the augmented reality-based Evoke Education System (EES) to improve service operations in educational settings. The EES uses an animated character (Moe) to interact with children in a classroom by reproducing their teacher's movements and speech.

This study uses a quantitative approach for the system usability evaluation. The ESS was evaluated by 71 children aged 6–8 years old, from two primary schools. After interacting with the EES, they completed a system usability questionnaire and participated in a knowledge acquisition test.

The knowledge acquisition test undertaken on the initial day showed statistically significant improvements for children taught with the EES, compared to children taught through traditional teaching approaches. However, the retest nine days later was not statistically significant (as only one school participated) due to low power. This study used confirmatory factor analysis (CFA), resulting in the identification of five essential factors (likeability, interactiveness, retention, effectiveness/attractiveness and satisfaction) that contribute to the EES's usability. The comparison with existing literature shows that these factors are consistent with the definition of system usability provided by the International Organization for Standardization and current academic literature in this field.

The findings presented in this study are based on the data from only two schools. The research can be extended by involving children from a greater number of schools. Mixed methods and qualitative research approaches can be used for future research in this area to generalise the results.

This study proposes an innovative augmented reality-based education system to help teachers deliver their key messages to the children in a fun way that can potentially increase their knowledge retention.

The purpose of this article is to aid conceptualization of social well-being at work by identifying its components in a contemporary office context, so adequate measures can be developed to monitor social well-being and to assess the impact of interventions in the workplace.

This study used existing interview data from recent post-occupancy evaluations of two large activity-based flexible offices in the Dutch public sector. Data-driven concept mapping of 182 different employees' statements on social aspects of well-being was used to find communalities in their perceptions.

From the data 14 key concepts emerged referring to employees' social needs, reactions to (anti-)social behaviour of others and perceived social affordances of the work environment. Contrary to established theory, social well-being appeared to be a context-bound phenomenon, including components of both short-term hedonic and long-term eudaimonic well-being.

The findings serve as an inductive source for the further development of adequate measures of social well-being at work. Limitations concern the specific (cultural) setting of the cases and the use of existing data.

Preliminary suggestions for fostering social well-being include change management, participatory design, being alert of the identified risks of activity-based offices and supporting privacy regulation, identity marking and a sense of community, as well as a diversity of informal face-to-face interactions balanced with quiet spaces.

This article contributes to the conceptualization of social well-being in contemporary offices by discussing established social well-being theory and analysing real-world data, using a method novel to management research.

This paper is premised upon an analysis of 26 cities within the UK regarding their smart city projects. Each city was analyzed through news articles, reports and policy documents to ascertain the level of each city's development as a smart city. Each was coded by separating the projects into five types, which were ranked on a scale from 0 (no plans for use) to 5 (project type in use). The most common types are the provision of open data and the creation of business ecosystems as the primary driver of the smart city. However, many councils and enterprises proclaim smartness before the technology is actually in use, making it difficult to separate what is utilised and what is under development. Therefore, this paper further carried out an analysis of 20 cities and their intended plans to usher in the smart city, to observe the expected emergence of smart city technology. This was achieved by interrogating various roadmaps and policy documents produced by the respective cities. It was found that the most prevalent form of emergent smart city technology is the rollout of 5G and increased educational programmes alongside a proliferation of internet of things and electric vehicle usage.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

Drawing from legitimacy and institutional entrepreneurship theory, this study assesses the naming patterns of entrepreneurial firms in the US biotechnology industry.

The authors use a mixed-methods design of content analysis and regression to analyze a sample of 441 entrepreneurial biotechnology firms, for which data were obtained from Net Advantage. The authors track changes to the proportion of firms with naming attributes, such as name length and type of name. The authors also examine variability in those characteristics during the industry's evolution, comparing freestanding to acquired start-ups.

Start-ups select names that are longer, more descriptive, begin with rare sounds or hard plosives and have stronger discipline- or technology-specific links during nascent years of the industry. As the industry evolves, entrepreneurs are more likely to select names that are shorter, more abstract, begin with hard plosives and have stronger industry-specific links. The naming patterns of freestanding and acquired companies differ, and companies that conform to industry pressures tend to remain independent.

Unlike extant studies that assess established industries, the current study identifies shifting trends in the naming patterns of entrepreneurial firms in an emerging industry. By focusing on start-ups, the authors expand research on organizational naming practices, which focuses traditionally on name choices and name change patterns of incumbents. By using marketing and linguistics methods when analyzing organizational name attributes, naming patterns in these attributes are identified, including name length, name type, starting letter of the name and link to the industry.

This study aims to examine the role of hunger, environmental, economic, landfill and water shortage concerns as significant dimensions of consumer social awareness marketing in socially responsible plate food consumption.

To carry out their purpose, the authors validate the hypothesized model empirically through data from 1,536 households using structural equation modeling (SEM). In particular, the construct measures of the structural model have been tested by confirmatory factor analysis (CFA).

The outcome the authors came up with is coherent with the hypothesized model, and it proves a positive relationship of the five dimensions identified on consumer awareness. Moreover, the study results show the crucial role of landfill and water shortage concerns in measuring consumer awareness.

These findings may be of interest to practitioners, academics and policymakers for socially responsible food consumption guidance and training for planning consumer awareness programs. More in detail, this study offers the indication that the dimensions of the social consumer awareness construct are differing from commercial consumer awareness.

Even though several previous studies have addressed the concept of consumer awareness concerning product and service purchase decisions, this is one of the first research studies on consumer awareness as a multidimensional construct in social marketing studies domain.

Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.

The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.

It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.

This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.