Information about the paper titled "CITIZEN VS. PROFESSIONAL DEVELOPERS: DIFFERENCES AND SIMILARITIES OF SKILLS AND TRAINING REQUIREMENTS FOR LOW CODE DEVELOPMENT PLATFORMS" at IATED Digital Library

Worldwide spending on Internet of Things (IoT) applications is forecasted to surpass $1 trillion by 2022. To stay competitive in this growing technological industry segment, lowering costs while increasing productivity and shortening time-to-market will become increasingly important. Adopting Agile Software Development practices for IoT projects may provide this competitive advantage, as it enables organizations to respond to change, while being dynamic and innovative. Applying a mixed-methods approach, agile IoT practitioners around the world and from diverse industries were surveyed and interviewed. Our study recommends that Agile Software Development team makeup, practices, and methods should be tailored to the specific industry, culture, people, and IT application of an organization. People play an important role in the success of agile projects; therefore, our research focuses on identifying the critical attributes of agile teams to maximize success. Our study identified the five critical agile practices: Collective Code Ownership, Continuous Integration, Single Team, Dedicated Customer, and Sprint Planning and found that both technical and soft skills are essential for successful IoT development.

Purpose: This research delves into understanding the selection criteria of Generation Z, specifically those with an IT education background, in choosing potential employers. The study aims to bridge the gap in knowledge regarding employer attractiveness from the perspective of this emerging workforce cohort. Design/Method/Approach: A quantitative approach was employed, utilizing a survey method to gather data. The study predominantly targeted students from an Austrian business school, resulting in a sample size of 156 respondents. The survey included a Conjoint Analysis to evaluate various employer attributes. Findings: The findings highlight the significance of workplace flexibility, Work-Life Balance, and meaningful, varied job tasks for Generation Z. Salary emerged as a crucial factor influencing their choice of an attractive employer. While symbolic attributes were slightly more critical than instrumental ones, no significant impact of professional experience on the perceived relevance of symbolic attributes was found. Theoretical Implications: The study enriches the Instrumental-Symbolic Framework by validating the importance of symbolic attributes for Generation Z. Practical Implications: Practitioners can leverage these insights to tailor their Employer Branding strategies to attract Generation Z, focusing on flexible work arrangements, meaningful work, and competitive salaries. Originality/Value: This research provides new insights into the preferences of Generation Z in the IT sector, highlighting the nuanced differences in their employer selection criteria compared to previous generations. Research Limitations/Future Research: The study’s limitations include its focus on two specific educational institutions and the lack of rural-urban differentiation among respondents. Future research could explore these criteria across diverse geographic and institutional contexts. Paper Type: Empirical JEL Сlassіfіcatіon: J24, M12, M51

Market competition leads to shorter cycle times for new or updated products. Therefore, flexibility in reacting to market changes, product development, and all related processes must be accelerated. In this regard, accurate cost estimation in the early stages of product development is critical for assessing the economic viability of a product. However, cost estimation requires data and expertise from several departments. Machine learning approaches could improve the accuracy and reduce the time needed for cost estimation. To investigate the eligibility of machine learning based cost estimation, a case study was conducted on an industrial company that produces plastic molding parts as key components of its products. The study involved training various supervised machine learning algorithms on a dataset of plastic injection molding parts using three different cost calculation methods. The three methods differed in the extent to which they considered the different process steps involved in the production of the parts. Different tree-based machine learning regression models and neural network models were trained to identify the most suitable approach for cost estimation in the given context. The results showed that tree-based machine learning algorithms outperformed neural networks and that individually predicting manufacturing parameters for cost calculation of each manufacturing process step leads to the most accurate cost estimation. This paper demonstrates how machine learning can support cost estimation in the early stages of the product lifecycle, reducing development times and improving cost estimation accuracy.

“Someday, in the distant future, our grandchildren’s grandchildren will develop a new equivalent of our classrooms. They will spend many hours in front of boxes with fires glowing within. May they ...

Purpose: Data is collected from all aspects of our lives. Yet, data alone is useless unless converted into information and, ultimately, knowledge. Since data analysts, in most cases, are not the ones in charge of making decisions based on their findings, communicating the results to stakeholders is crucial to passing on information of data-driven insights. That is where the discipline of data storytelling comes into play. Often, data storytelling is considered an effective data visualization. Creating data stories is a structured approach to communicating data insights as an interplay of the three elements data, visuals, and narrative. Sharing data-driven insights to support better business decisions require data storytellers skilled in the “art of storytelling”. Design/Method/Approach: In this paper, the authors discuss the use of data storytelling in business to communicate data to stakeholders for improving decision-making. The findings are derived from (1) an extensive literature review and (2) a qualitative analysis of 13 expert interviews with people incorporating data storytelling into their daily work within their jobs in international companies. Findings: These interviews revealed the importance of providing a flexible tool to support knowledge sharing for people communicating complex data to internal stakeholders. Combining literature with qualitative research enabled the authors to create the "data storytelling cheat sheet", a guide for practical data storytelling. Theoretical Implications: Theories like the Psychological distance or the idea of the theory of dual processing dual are used to base our research idea on. There was no new theory built in this paper. Practical Implications: One of the results is an implementation systematic cheat sheet that helps practitioners to implement data storytelling in their daily business. Originality/Value: The theory of data storytelling is overwhelming the first time to use and based on an empirical study with experts in the field a guideline for hands on use was developed under a based on a cleanly defined empirical study. Research Limitations/Future Research: The paper focus on internal data storytelling – maybe with external stakeholders it might be slightly different. The results the data communication part in any data analytics project. Paper Type: Empirical JEL Classification: D7, D8