A study on mushroom management perception of employees in Provincial Directorates of Youth and Sports (Niğde province example)

It seems that the mushroom management metaphor has become a new research area in management sciences. This management style takes its name from mushroom cultivation in terms of method similarity. In mushroom cultivation; mushrooms are left in the dark and only allowed to grow by giving them the resources they need. In the mushroom management metaphor, managers leave employees in a kind of darkness. They provide only the resources they need without conducting mutual communication with them and does not need to share information with the employees. At the end of this process, managers expect an increase in performance from their employees. Within the scope of this study, it was aimed to measure the mushroom management perceptions of 4-a and 4-b employees in Niğde Provincial Directorate of Youth and Sports. The sample of the study consists of 92 civil servants, 42 women and 50 men, working in the Niğde Provincial Directorate of Youth and Sports. Personal information form and the “Mushroom Management Scale” developed by Birioğlu and Tekin (2018) were used as data collection tools. Descriptive statistics were used in the analysis of the data, T-test was used for pairwise comparisons, and ANOVA test was used for more than two comparisons. As a result, according to the findings, it was determined that the employees had a medium level of mushroom management perception. No significant difference was found between the sub-dimensions of administrative duty, gender, age, and tenure. ​Extended English summary is in the end of Full Text PDF (TURKISH) file. Özet Mantar yönetim metaforunun, yönetim bilimlerinde yeni bir araştırma alanı haline geldiği görülmektedir. Bu yönetim tarzı ismini metot benzerliği açısından mantar yetiştiriciliğinden almaktadır. Mantar yetiştiriciliğinde; mantarlar karanlıkta bırakılır ve sadece ihtiyacı olan kaynaklar verilerek yetişmesi sağlanır. Mantar yönetim metaforunda da yöneticiler, çalışanları bir çeşit karanlıkta bırakır; onlarla karşılıklı iletişim yürütmeden sadece onların ihtiyacı olan kaynakları sağlar ve çalışanlarla bilgi paylaşımına gerek duymaz. Bu sürecin sonunda yöneticiler, çalışanlardan performans artışı beklerler. Bu çalışma kapsamında, Niğde Gençlik ve Spor il Müdürlüğünde 4-a ve 4-b çalışan personellerin mantar yönetim algılarının ölçülmesi amaçlamıştır. Çalışmanın örneklemini, Niğde Gençlik ve Spor İl Müdürlüğünde çalışan, 42 kadın, 50 erkek olmak üzere toplam 92 memur oluşturmaktadır. Veri toplama aracı olarak kişisel bilgi formu ve Birincioğlu ve Tekin (2018) tarafından geliştirilen “Mantar Yönetim Ölçeği” kullanılmıştır. Verilerin analizinde tanımlayıcı istatistikler, ikili karşılaştırmalarda T-testi, ikiden fazla karşılaştırmalarda Anova testi uygulanmıştır. Sonuç olarak; elde edilen bulgulara göre çalışanların orta düzeyde mantar yönetim algısına sahip oldukları belirlenmiştir. İdari görev, cinsiyet, yaş ve görev süresi ile alt boyutlar arasında hiçbir anlamlı farklılık tespit edilememiştir.

Vector space models for trace clustering: a comparative study

Process mining explores event logs to offer valuable insights to business process managers. Some types of business processes are hard to mine, including unstructured and knowledge-intensive processes. Then, trace clustering is usually applied to event logs aiming to break it into sublogs, making it more amenable to the typical process mining task. However, applying clustering algorithms involves decisions, such as how traces are represented, that can lead to better results. In this paper, we compare four vector space models for trace clustering, using them with an agglomerative clustering algorithm in synthetic and real-world event logs. Our analyses suggest the embeddings-based vector space model can properly handle trace clustering in unstructured processes.

Difficulties in maintaining productivity and engaging teams in home office

Technological advances have allowed for new work concepts, modifying the way communication takes place, through tools, platforms and devices, enabling models such as home office. The difficulties of maintaining productivity and engaging teams in a home office regime is a relevant reality for many organizations today. In this scenario, this research aims to identify the challenges of maintaining productivity and engaging teams in a home office regime. To achieve the proposed objective, an exploratory, descriptive, and quantitative research was carried out, with 37 process managers. With this research, it was concluded that the evolution of home office management is intense, but aspects such as managing the journey, adapting the environment, training the team through the online mode, methods of maintaining commitment, generating networking alternatives and exchange of experiences, can be improved by organizations over time.

Human versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design

Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an Artificial Intelligent (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams' actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers' intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from the both the AI and human manager as equally relevant and helpful, and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work is able to match the capabilities of humans, showing potential in automating the management of a complex design process.

Doing the right thing or doing things right: what is better for a successful manufacturing reshoring?

The article concerns the revision of earlier decisions to offshore production activities (so called “relocation of second degree”); more specifically it is focused on the “reshoring” (also referred as “relocation to the home country”, “back-reshoring” or “back-shoring”). The research aims are to investigate what types of mistakes occur along the decision-making and implementation process and how they affect the outcome, in terms of success or failure, of a relocation strategy. A multiple case study involving four companies in the fashion industry from Portugal and Italy was conducted. The cross-case analysis allowed to differentiate decision-making mistakes from implementation ones and to assess differences and similarities among the cases in terms of content of the relocation, drivers and outcomes. The research contributes to previous literature on reshoring by bringing evidence of different types of mistakes to be considered, thus requiring further conceptualization of the reshoring process. Managers and entrepreneurs should consider the importance of doing the things right also during the implementation, too often underestimated. The present article is the first one in the reshoring literature bringing evidence of cases of failure in the relocation decisions and discriminating among different kinds of mistakes.

City Cleanliness Drive Web Portal Using Region Based Convolutional Neural Networks

Identification or detection of object played an important role in Computer Vision, in implementations like city construction process Managers had often wasted lot of their energy, time and resources in cleaning up the garbage, which was unexpectedly showed up. When deep network systems increased its complexity, the systems are constrained by the training data availability. Due to this, Open CV, Google AI released the Open images dataset publicly, so that the research and development would happen in study and analysis of images. As a result, virtual street cleanliness been at most important in this project, however the existing system has disadvantages like collection of garbage is not automated. It doesn’t use the best real time algorithm for identifying the objects. This project will embed the above said things in the system, making the work of managers to keep the city/construction site clean very simple and effective.

Adoption, use and management of process mining in practice

PurposeThis study explores how process managers perceive the adoption, use and management of process mining in practice. While research in process mining predominantly focuses on the technical aspects, our work highlights organizational and managerial implications.Design/methodology/approachWe report on a focus group study conducted with process managers from various industries in Central Europe. This setting allowed us to gain diverse and in-depth insights about the needs and expectations of practitioners in relation to the adoption, use and management of process mining.FindingsWe find that process managers face four central challenges. These challenges are largely related to four stages; (1) planning and business case calculation, (2) process selection, (3) implementation, and (4) process mining use.Research limitations/implicationsWe point to research opportunities in relation to the adoption, use and management of process mining. We suggest that future research should apply interdisciplinary study designs to better understand the managerial and organizational implications of process mining.Practical implicationsThe reported challenges have various practical implications at the organizational and managerial level. We explore how existing BPM frameworks can be extended to meet these challenges.Originality/valueThis study is among the first attempts to explore process mining from the perspective of process managers. It clarifies important challenges and points to avenues for future research.

Seeking Research Questions from Implementers: Considerations for Leveraging Ground Actors Research Needs in the Fight against Malaria in West Africa.

Malaria, a parasitic infection, still a major public health threat and hence a heavy economic burden that hampers the development of countries especially in West Africa. Malaria and Neglected Tropical Diseases (NTD) altogether are now the third leading cause of death in ECOWAS region with 77% of deaths in children under 5 years due to malaria. Countries and partners set ambitious goals for reducing the burden and eliminating malaria by 2030. To achieve these goals, West African countries program managers and researchers are engaged in a series of activities to address practical bottlenecks. This article aims to evaluate the effectiveness of a collaboration between malaria program implementation and research needs in West African countries, related to malaria. It describes the crucial role and process managers and researchers can play in these countries, in assuring the identification of research related program implementation issues as well as solutions to those barriers in order to alleviate the burden. Identifying research questions pertaining to each program through an implementation science approach, integrating health delivery and innovative approach to bottlenecks, and decision support to promote joint optimization of activities for malaria elimination. Managers and researcher discussed together programs difficulties being encountered and highlighted key research questions to be addressed.

Operators’ Load Monitoring and Management

Due to the introduction of highly automated vehicles and systems, the tasks of operators (drivers, pilots, air traffic controllers, production process managers) are in transition from “active control” to “passive monitoring” and “supervising”. As a result of this transition, the roles of task load and workload are decreasing while the role of the mental load is increasing, thereby the new type of loads might be defined as information load and communication load. This paper deals with operators’ load monitoring and management in highly automated systems. This research (i) introduces the changes in the role of operators and requirements in load management, (ii) defines the operators’ models, (iii) describes the possible application of sensors and their integration into the working environment of operators, and (iv) develops the load observation and management concept. There are some examples of analyses of measurements and the concept of validation is discussed. This paper mainly deals with operators, particularly pilots and air traffic controllers (ATCOs).