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The purpose of this paper is to demonstrate the rationale, technical framework, content creation workflow and evaluation for a multilingual massive open online course (MOOC) to facilitate information literacy (IL) considering cultural aspects.

A good practice analysis built the basis for the technical and content framework. The evaluation approach consisted of three phases: first, the students were asked to fill out a short self-assessment questionnaire and a shortened adapted version of a standardized IL test. Second, they completed the full version of the IL MOOC. Third, they were asked to fill out the full version of a standardized IL test and a user experience questionnaire.

The results show that first the designed workflow was suitable in practice and led to the implementation of a full-grown MOOC. Second, the implementation itself provides implications for future projects developing multilingual educational resources. Third, the evaluation results show that participants achieved significantly higher results in a standardized IL test after attending the MOOC as mandatory coursework. Variations between the different student groups in the participating countries were observed. Fourth, self-motivation to complete the MOOC showed to be a challenge for students asked to attend the MOOC as nonmandatory out-of-classroom task. It seems that multilingual facilitation alone is not sufficient to increase active MOOC participation.

This paper presents an innovative approach of developing multilingual IL teaching resources and is one of the first works to evaluate the impact of an IL MOOC on learners' experience and learning outcomes in an international evaluation study.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

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.

This paper aims to undertake a simultaneous assessment of interdependence in the behaviours of front-line and back-office employees and their joint effect on customer-related organisational performance. It also tests for a moderating influence of the emotional intelligence of front-line salespeople and back-office employees.

The sample comprises 105 front-line sales employees and 77 back-office employees. The customer-related organisational performance data come from a UK business-to-business (B2B) electronics company. With these triadic data, this study uses partial least squares to estimate the measurement and structural models.

Salespeople’s customer orientation directly affects customer-related organisational performance; the relationship is moderated by salespeople’s emotional intelligence. The emotional intelligence of salespeople also directly affects the customer-directed citizenship behaviour of back-office employees. Furthermore, the emotional intelligence of back-office staff moderates the link between the emotional intelligence of salespeople and back-office staff citizenship behaviour. Back-office staff citizenship behaviour, in turn, affects customer-related organisational performance.

The emotions deployed by employees in interactions with customers clearly shape customers’ perceptions of service quality, as well as employee-level performance outcomes. However, prior literature lacks insights into the simultaneous effects of front-line and back-office employee behaviour, especially in B2B settings. This paper addresses these research gaps by investigating triadic relationships – among back-office employees, front-line employees and customer outcomes – in a B2B setting, where they are of particular managerial interest.

The purpose of this paper is, first, to assess the potential of the visual to enact multiplicity and reflexivity in organizational research, and second, to develop a performative approach to the visual, which offers aesthetic strategies for creating future research accounts in organization and management studies.

The paper reviews existing visual research in organization and management studies and presents an in‐depth analysis of two early, almost classical, and yet very different endeavors to create visual accounts based on ethnography: the multi‐media enactments by Bruno Latour, Emilie Hermant, Susanna Shannon, and Patricia Reed, and the filmic and written work by Trinh T. Minh‐ha and her collaborators.

The authors’ analysis of how the visual is performed in both cases identifies a repertoire of three distinct and paradoxical aesthetic strategies: de/synchronizing, de/centralizing, and dis/covering.

The authors analyze two rarely acknowledged but ground‐breaking research presentations, identify aesthetic strategies to perform multiplicity and reflexivity in research accounts, and question the ways that research accounts are written and published in organization and management studies by acknowledging the consequences of a performative approach to the visual.

The purpose of this paper is to illustrate the advantages of using intelligent agents to facilitate the location and customization of appropriate e‐learning resources and to foster collaboration in e‐learning environments.

This paper proposes an e‐learning environment that can be used to provide customized learning. It utilizes a set of interacting agents that can personalize instruction based on an individual's prior knowledge as well as their cognitive and learning needs. The e‐learning agents monitor the e‐learning environment and improve learning and collaboration based on learners' prior knowledge, social characteristics and learning style.

E‐learning agents should allow the discovery of new learning objects more easily, allow learners to customize materials presented to improve learning outcomes, and improve collaboration in the e‐learning environment.

Little prior research has been done on the use of agents in e‐learning environments. This paper proposes a set of e‐learning agents that, if implemented in online education or training environments, should provide tangible benefits to organizations.