Search results

As one of the most energy-intensive sectors, the manufacturing industry is strongly affected by current economic, ecological and political issues and is increasingly looking to reduce its energy consumption through effective management. One important component of energy management is monitoring, which can be improved by using the Internet of Things (IoT). This study aims to identify the factors behind the adoption of IoT energy monitoring systems in manufacturing industries and IoT’s impact on consumption in this context. Moreover, this study explores what constitutes successful implementation and develops recommendations on how companies can best use the generated energy-saving potential.

For this purpose, an exploratory, inductive research approach is taken in which five semi-structured interviews with IoT energy-monitoring platform providers located in Germany are conducted and additional documents about these platforms are analysed.

Based on these interviews and documents, this study defines the relevant factors involved in the increased adoption of IoT energy monitoring systems as desired cost reduction, regulatory requirements and customer expectations. Once implemented, a company’s energy efficiency potential strongly depends on its individual situation; however, the transparency characteristic of IoT supports managers in deriving effective energy-saving measures. Additionally, the used efficiency potential is influenced by the level of data collection and organisational capabilities.

To the best of the authors’ knowledge, as the first study, this research combines findings from the energy management and IoT research streams and places them into an organisational context. The application of the fit-viability model as an overarching framework enhances the theoretical contributions of this study. Moreover, focusing on Germany and its substantial industrial sector enables the gathering of important insights and the analysis of the specific use case of industrial IoT, which yields new findings for practitioners as well.

The current gap between the required and available artificial intelligence (AI) professionals poses significant challenges for organisations and academia. Organisations are challenged to identify and secure the appropriate AI competencies. Simultaneously, academia is challenged to design, offer and quickly scale academic programmes in line with industry needs and train new generations of AI professionals. Therefore, identifying and structuring AI competencies is necessary to effectively overcome the AI competence shortage.

A probabilistic topic model was applied to explore the AI competence categories empirically. The authors analysed 1159 AI-related online job ads published on LinkedIn.

The authors identified five predominant competence categories: (1) Data Science, (2) AI Software Development, (3) AI Product Development and Management, (4) AI Client Servicing, and (5) AI Research. These five competence categories were summarised under the developed AI competence framework.

The AI competence framework contributes to clarifying and structuring the diverse AI landscape. These findings have the potential to aid various stakeholders involved in the process of training, recruiting and selecting AI professionals. They may guide organisations in constructing a complementary portfolio of AI competencies by helping users match the right competence requirements with an organisation's needs and business objectives. Similarly, they can support academia in designing academic programmes aligned with industry needs. Furthermore, while focusing on AI, this study contributes to the research stream of information technology (IT) competencies.

Considering working in the digital age, questions on the consequences for the individual workers are, so far, often neglected. The purpose of this paper is to deal with the question of whether the digital competences of the workforce is a research topic. The authors argue for the thesis that it is indeed a research topic.

In addition to a literature analysis of the top IS, HR, and learning publications, non-scientific sources, as well as the opinions of the authors, are included. The authors’ thesis is challenged through a debate of corresponding pros and cons.

The definition of digital competences lacks scientific depth. Focussing on the workforce is valid, as a “lifelong” perspective is not mandatory for research. Digital competence research is a multidisciplinary task to which the IS field can make a valuable contribution.

Although relevant references are included, some aspects are mainly driven by the opinions of the authors. The theoretical implications encompass a call for a scientific definition of digital competences. Furthermore, scholars should focus on the competences of the workforce, including occupations, roles, or industries. The authors conclude by providing a first proposal of a research agenda.

The practical implications include the alignment of multiple stakeholders for the design of “digital” curricula and the integration by HR departments of the construct of digital competences, e.g. for compensation matters and job requirements.

This paper is one of very few contributions in the area of the digital competences of the workforce, and it presents a starting point for future research activities.

The purpose of this paper is to emphasize the need to engender a positive attitude toward business analytics in order for firms to more effectively transform into data-driven businesses, and for business schools to better prepare future managers.

This paper develops and validates a measurement instrument that captures the attitude toward business statistics, the foundation of business analytics. A multi-stage approach is implemented and the validation is conducted with a sample of 311 students from a business school.

The instrument has strong psychometric properties. It is designed so that it can be easily extrapolated to professional contexts and extended to the entire domain of business analytics.

As the advent of a data-driven business world will impact the way organizations function and the way individuals think, work, communicate and interact, it is crucial to engage a transdisciplinary dialogue among domains that have the expertise to help train and transform current and future professionals.

The contribution provides educators and organizations with a means to measure and monitor attitudes toward statistics, the most anxiogenic component of business analytics. This is a first step in monitoring and developing an analytics mindset in both managers and students.

By demonstrating how the advent of the data-driven business era is transforming the DNA and functioning of organizations, this paper highlights the key importance of changing managers’ and all employees’ (to a lesser extent) mindset and way of thinking.