of their work. These organic teams usually consist of 3-15 employees, their responsibilities are rotated among them, and they change quickly to respond to the needs of any given situation (MacDonald, n.d.; Yeatts et al., 2004; George & Hinkes, 2016; Banner, Kulisch, & Perry, 1992; Wageman, 2001; Rapp et al., 2015; Smets 2014). Self-managedteams can provide organizations with a competitive advantage (Carson, Tesluk & Marrone, 2007) by offering many benefits as for instance increasing productivity and performance if implemented effectively (MacDonald, n.d.; Smets, 2014; Hauschildt & Konradt, 2012). Empowered workers are given the chance to take on responsibilities and the ability to contribute to changes, decisions and production within the company which is not possible under closely managed supervision (Bishop, 2013). Additionally, the participation in decision-making enhances the flow and use of important information within the organization (Yeatts, et al., 2004). Furthermore, they save costs (MacDonald, n.d.; Smets, 2014) which are for example caused by the reduction of midlevel supervisors. If workers become sufficiently productive self- managedteams are substituted for managerial control (Boundless, 2016; Bishop, 2013). Self-management increases motivation, pride, and trust and respect among the team members (Boundless, 2016; MacDonald, n.d.), which “lead[s] to increased morale, satisfaction [and] commitments” (Yeatts et al., 2004, p. 257) which in turn reduces staff turnover and absenteeism (Yeatts et al., 2004). They are also more effective due to the fact that decisions are made by the most suitably skilled employees concerning the specific job (MacDonald, n.d.).
As presented in the framework there were three main points chosen with which the healthcare sector has to deal with. In the case of TakeCare these were also present. TakeCare has to deal with rising costs and a political pressure of decreasing the care expenditures. In 2016 there is less budget available for the residential care due to a discount rate. They also need to reduce the number of beds by 70 which again results in shrinkage of personnel. This makes it even more important for TakeCare to focus on expenses and reducing non-attendance. In 2014 there was a big reorganization where TakeCare again reduced staff but also managers. At this point there were only self-managedteams left next to the small management and support groups. Bringing the hierarchical structure down to a flat organizational structure led to a big decrease in labor costs. Their labor costs made up approximately 76 percent of their operating costs in 2014. The transition to self-managedteams was also done with an aim on the client. This transition was used to put the client as central focus and provide personal care, which is of importance in a service organization. According to organizational document TakeCare had the highest score on client satisfaction, employee satisfaction and conduct of business in 2012 and 2015 in a benchmark for healthcare organizations. (TakeCare Documents). The fact that employee satisfaction had such a good score is an interesting point since the literature claims that employees in the healthcare are not satisfied with their jobs in general (Cooke & Betram, 2015). From several interviews also came forward that the employees perceive to be under the pressures of work intensification.
We capture four different aspects of innovative work practices (i.e. high-performance workplace systems, HPWS). These measures correspond to the central pieces of a high-performance workplace from the point of view of employees, as outlined in Appelbaum et al. (2000). Self-managedteams are defined as teams that select their own foremen and decide on the internal division of responsibilities. Information sharing equals one if employees are informed about the changes at work at the planning stage rather than shortly before the change or at its implementation. Training equals one if the employee has participated in employer-provided training during the past 12 months. 8 Incentive pay equals one if the person has performance-related pay and bonuses are based on the employee’s own effort. To examine the joint effects of innovative work practices, we identify “bundles”. Because there is no single definition for summary measures (e.g. Blasi and Kruse, 2006; Kalmi and Kauhanen, 2008), we follow a simple strategy. “Bundles” are captured by our variable HPWS, which equals one if more than one of the aspects of workplace innovations (self-managedteams, information sharing, employer-provided training or incentive pay) is present. 9 We include a vector of control varia bles to all models that can be regarded as ‘the usual suspects’, based on the absenteeism literature (e.g. Brown and Sessions, 1996; Holmlund, 2004; Dionne and Dostie , 2007). The exact definitions including the means and standard deviations of the variable s are documented in the Appendix (Table AI).
This study is based on a qualitative exploratory research approach and makes use of both primary data with semi- structured interviews and secondary data. For the collection of primary data, 6 interviews were planned with members of various self-managedteams in close collaboration with Livio Thuiszorg in Enschede in the Netherlands. Interviews were scheduled from May 2017 till mid-June 2017 with each interview taking around 1 hour. Interviewing the team-members in a semi- structured format creates a more open atmosphere and lets the interviewees expand on their answers and give further and new insights into performance management related topics. Furthermore, data were gathered by observing the locations and employees before and after the interviews. In addition, discussions were held with peer junior researchers who conducted interviews in similar topics. In appendix A, the interview protocol used during the interviews can be found. The interviews, are with permission of the interviewee, audio-recorded.
Qualitative studies often try to draw conclusions by focussing on a solid and distinct set of circumstances (Yin, 2011). Within the scope of this research, the agile coaches were asked to exemplify their answer by using their experiences from practise. Illustrating and providing examples of situations in which they decided to make use of a certain tool, performance measure or took a role. These examples can be used to formulate the circumstances in which certain variables do or do not apply. Assuming the agile coaches are expert within their field, the ideal interview would be to let the agile coach speak and explain for most of the time. The interviewer should take an inductive stance, so without introducing preconceptions. This way an internal validity is secured. For the interview, an interview protocol was used, however, the order in which the questions were asked, and how the questions were asked differed per interview. During the creation of the interview protocol, two more questions were added to increase the context of understanding. How does the performance of agile teams differ with and without the presence of an agile coach during the meetings? And. How does an agile coach know when to interfere during meetings when the effectiveness goes down? The interview protocol can be found in Appendix C. The interview consisted of open questions only. Probing was part of the interviewing process. This, however, only took place after the coach had given an answer to the main question. This way probes would not lead to advancement into categories, propositions, and meaning based on these misconceptions (Yin, 2011). The probes were sometimes open or closed questions, meant for getting more detailed information on a topic. For the qualitative analysis of the data, a thematic analysis procedure was followed. Because of the theoretical freedom and thus flexibility this procedure gives it is a practical research tool, which can give comprehensive and accurate account of data (Braun & Clarke, 2006).
up for difficult situations, so that she could take final decisions when the team could not come to an agreement. Teams that were further in the development process indicated the same, with the difference that the last time that they used this opportunity was a long time ago. Another aspect from the coach manager as back-up which was also recognized by the teams was positive coaching, which means that the coach manager provides some advice in case of problems and gives informal rewards in case of success as a SMT (Morgeson, 2005; Wageman, 2001; Kirkman and Rosen, 1999). The second thing that was mentioned about the authority was that, according to the team members, the tasks were divided in the wrong way, meaning that some tasks they were responsible for should be under the authority of the coach manager and vice versa. This came to light during an interview with a team that was not performing so well with regards to being a SMT. These aspects that were found mean that the coach manager should get a clear picture of the progress of the team in the process of becoming a SMT. She should also get insight in the extent to which the team is ready to pick up certain team tasks. On the basis of this she should figure how the autonomy should be divided between her and the team. In this decision, she should on the one hand consider the input of the team, as that will increase the chance that the final decision that she will make will be supported (Cawley et al., 1998) and so that chance of undermining the performance is reduced. On the other hand, the coach manager should make a decision that challenges the team and stimulates the team members in their progress of becoming a SMT. When the coach manager provides the teams with authority on the basis of this, she deals with the different perception of autonomy that teams have due to their circumstances and their interpersonal differences (Langfred and Moye, 2004; Endler, Kantor and Parker, 1994).
Self reconfiguring robot systems can be applied to many problems, and the purpose of this paper is to investigate a specific one. We explore how a group of self organized robots can explore an unknown and potentially diffi- cult environment, and deploy a limited number of carried surveillance sensors within it to maximize the area surveyed. A possible scenario could have an SRR dropped into an unknown environment, where its goal is to place a series of acoustic sensors to detect audible movement of humans or vehicles. This is a complex task that consists of multiple steps and sub-goals. Because the environment is initially unknown, a significant part must be explored first if it wants to place sensors in anything resembling a ”good” position. This problem was chosen because it should take advantage of the strengths of SRRs. A SRR can choose to split into multiple robots to explore parts of the environment, but then merge back together to deploy a sensor in an location which is diffi- cult to get to. We look at how it can succeed at its main goal, to survey the most terrain, relative to the time taken and energy used to do so.
The students were randomly assigned to groups of three to six people, in each of the courses, in order to participate in extensive group work throughout the term. Student work groups were appropriate subjects for this study because they closely resemble self-managed work teams. They completed an entire piece of work involving interdependent tasks over which they have substantial autonomy. In addition, the leaders had no authority over the group members. There were a total of 44 groups and leaders were randomly assigned the leader role. All students in a group were essentially equal in terms of the contribution they could make to the groups’ work and their ability to enforce cooperation and compliance. Leaders were given the additional tasks of being the prime contact between the groups and the instructor, and organizing and facilitating task completion. Throughout the term, the groups participated in class to solve problems directly related to the material being presented in their course of study. These problems were time limited and graded, and the marks used as part of the students’ final course grade making task performance important to them.
Figure 7 illustrates this scenario for four network nodes: one RNC, one RBS (also known as Node-B) and two devices acting as network routers (Routers A and B). The standby link between the RNC and the RBS is originally deployed through Router-A. This standby link therefore has reserved resources in the RNC, the RBS and also Router-A. In this example, Router-A can drop the standby link between the RNC and the RBS if it is experiencing high-traffic levels and requires the reserved resources to meet user demand. The network device which governs the standby link configuration, in this instance the RNC, will then try to reconfigure a new standby link using other entities in the network. This very simple scenario is intended to illustrate how some of the basic control procedures work in this system and how conflicting decisions and stability are managed. This scenario assumes that in the future, the transport network between the Radio Access Network (RAN) nodes will be IP, based on MultiProtocol Label Switching (MPLS) tunnels as suggested by 3GPP R5  and a standby link will be composed of two directed MPLS tunnels, one forwards and another backwards.
An alternative component approach that has investigated the coordinated reconfiguration of decentralized, self-managed systems is k-Components . Here, a k-Component is a component with local architecture and a reflective meta protocol to inspect and adapt this architecture. Each k-Component is then related to a management agent; this is responsible for monitoring the environment and making decisions about when to adapt the component structure. In the co-ordination dimension, distributed agents can communicate with one another, although decisions to adapt are made locally. Hence, the approach is suited to only decentralized reconfigurations, with no guarantee that behaviour is changed across a system. Our approach, is in general more flexible allowing the mechanism for co-ordinated adaptation to be tailored to the requirements e.g. centralized or decentralized.
The SPADI is a self-report measure which includes 13 items divided into 2 sub-scales; pain (5 items), disability (8 items). The responses are indicated on a visual analogue scale where 0 = no pain/no difficulty and 10 = worst imaginable pain/so difficult it requires help. The items are summed and converted to a total score out of 100. The SPADI has been validated for use in this patient population and a minimally clinically important change of 10 points has been identified (8 10).
The case studies show that transparency of payment is still an issue which is influenced by negative associations in society and organizations. Nevertheless, it is one of the possible steps when self- management is introduced, and hierarchies are changed. Bernstein found that models such as Holacracy do not provide solutions for issues such as “career progression, compensation, hiring, firing”, which are traditional components of bureaucracy (Helmore, 2015, para.15). Introducing a new payment process is less complex in a team than in a whole organization if the team is already operating with flat hierarchies, and a climate of trust and support is dominant. At the organizational level there are more reservations as salary decisions were formerly made by managers in a pyramidal hierarchy and people fear the transparency and potential conflicts that can arise from the new process. This is also reflected in field studies. Researchers such as Keltner et al. (2003), Marmot (2004) and Weber (1947) found that income disparity is a form of hierarchy (as cited in Anderson & Brown, 2010, p.63). Furthermore, Desai et al. (2010) found that individuals see their relative salary as a sign of how respected and valued they are compared to their colleagues (as cited in Anderson & Brown, 2010, p.63).
In some European countries (e.g., Germany and Switzerland) there is an ongoing political debate on the question whether the introduction of SMWT perhaps harms employee well-being by promoting substantial self-organized work intensification (e.g., Lehndorff, 2007; Singe and Croucher, 2003). 28 Some practitioners, such as employer and employee representatives, seem to be split on this question. While employer representatives typically emphasise the positive effect that SMWT is expected to have on employees’ job autonomy and work-life balance, unions often tend to oppose SMWT. One of their main arguments is that recording working hours protects workers from being exploited by the employer. Consequently, the omission of working hours registration in SMWT arrangements would pressurise workers to intensify effort in order to meet the employer’s expectations. The claim is that work intensification might reach a level that could even harm the workers’ physical and mental health. 29 SMWT is also controversially discussed
The first of these practical reasons is the sheer numbers of SMSFs. At the time of the Wallis report there were approximately 187,000 SMSFs; currently they exceed 468,000. These numbers mean the supervisory regulator cannot develop the depth of knowledge or understanding of the sector necessary to prudentially regulate it, encompassing as it does such a wide range of experience, level of assets and engagement by trustees. The major problem facing the ATO is lack of transparency of the SMSF sector. APRA has a close relationship, depth of knowledge and good understanding of the 386 large funds and 3,519 Small APRA funds for which it provides supervisory regulation, and can easily ‗tweak‘ regulations in response to perceived problems. SMSFs, on the other hand, cover a huge range from high-value, sophisticated vehicles to poorly resourced funds managed by naïve trustees who devote little time and care to running them. Anecdotally, some trustees are not even aware they hold that office.
To realize this vision, the future of resource provisioning is here argued to lie in software-defined infrastructures (SDIs). SDIs are realized as cyber-physical systems that dynamically and seamlessly distribute software components among a mix of resources. These include large, energy-efficient and scal- able data centers (usually placed at remote locations and interconnected with high capacity networks), and low-latency edge data centers (smaller, placed closer to end users and interconnected at the edge of the access networks). To address these goals, we formulate an approach based on advances in three primary research areas: resource management; data science and data analytics; and intelligent automation. Resource Management—Resource management in SDIs re- volves around the control of cyber-physical systems where physical components are fully abstracted and controlled via software. Resource management addresses the questions of how much capacity and what type of resources to allocate to applications, and when and where to deploy resources in and between data centers. The managed entities of SDI environ- ments are provided by low-level virtualization technologies, e.g., Virtual Machines (VMs), virtual networks, and contain- ers operating in data centers. Such techniques deliver high levels of flexibility in the management of resource capacity. However, while virtualization is today a well-established tech- nology, efficient resource management remains a significant
Self-managed superannuation funds (SMSFs) are a uniquely Australian retirement savings vehicle which are constituted as trusts with one to four members, each of which must be a trustee of the fund unless under a legal disability. SMSFs have become so popular in recent years that as a sector they are now the largest in terms of number of funds and assets under management, yet very little academic study has been directed at this important sector to date.
Cloud systems use different virtual machine (VM) placement algorithms to schedule instances by selecting physical machines (PMs) according to real time system information (i.e usage of CPU, memory, hard disk, network and other). In this work we stress the problem of instantaneously collected system data that in many cases does not reflect the big picture (i.e. average resource utilization levels). The current VM placement does not consider real time VM resource utilization levels. In this work we propose a new VM placement algorithm based on past VM usage experiences. We monitor the VM usage in real time and we train different machine learning models to calculate the prediction of the VM resource usage per server, thus to place VMs accordingly. We present an algorithm that allows selfmanaged VM placement according to PM and VM utilization levels. Usually, traditional systems (i.e. OpenStack) use a filtering (which PMs can host the VM thus having resources) and weighing method (which PM has the higher RAM) to select PMs based on the specific time instance, without considering the actual VMs’ resource usage of the selected PMs. We introduce the concept of analyzing past VM resource usage according to historical records based on computational learning to optimize the PM selection phase.