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The purpose of this paper is to examine the effect of managerial change in the English football industry. The authors’ theoretical discussion covers three contrasting concepts that attempt to explain the association between manager change and organizational performance (scapegoating theory, vicious circle theory and tenure and life-cycle theory).

Data were collected for the four main English Football Leagues (EFLs) between 2000/2001 and 2015/2016. A total of 2,816 football matches were included in the study and during this time 525 instances of managerial change were observed. Analysis was conducted using relevant statistical techniques to examine the impact of managerial change on performance.

The results show significant differences in all four EFLs when considering teams who make a managerial change and those who do not. Further analysis revealed that a managerial change is more beneficial for clubs in the bottom half of the league, particularly for the English Premier League.

The implications for clubs competing in English football are clear when considering the strategic direction of the club in respect of managerial change and its impact on team performance. Yet, our findings come with a warning. The findings do not infer direct cause and effect here, and any board decision should consider additional factors other than sporting performance before deciding to sack their manager.

Purpose – We make a case for bridging two types of logics – analytic and dialectic – for explaining processes of social-historical change, and maintain that a successful bridge between these two logics depends on a variety of conditions and most especially the type of analytic logic or model one employs for capturing dynamic processes.

Methodology/approach – Conventional models of social change processes typically presuppose ergodic social worlds and are problematic as analytic approaches generally and most certainly are not fertile grounds for feeding dialectic theorization. Instead, we propose modeling dynamic processes that begin by assuming a nonergodic social world – one in flux, one that is nonrepeating, one within which model process and parameter structures are historically contingent and change with time, one that is autocatalytic, creating and changing its own possibilities.

Findings – We develop the line of thinking adumbrated above and illustrate these modeling strategies with empirical examples from US labor movement history. Results from these examples lend much weight to our proposals. Thus, this chapter demonstrates that concerns about the use of ergodic assumptions and about greater use of dialectical reasoning when studying social processes are not idle speculations within theoretical commentaries but have practical consequences in the conduct of research and the building of better theory.

Research limitations/implications – To approximate such an approach, social scientists should avoid cross-sectionalist and longitudinal modeling strategies that presuppose stability and homogeneity in parameter and process structures. Homogeneity and stability in parameter and process structures should be demonstrated, not assumed.

Originality/value – Rather than accepting the alienated spheres of social science analytics and dialectic theory, our proposal presupposes nonergodic social worlds and takes pragmatic steps for estimating analytic models that are more amenable to dialectic reasoning. Models that take nonergodicity seriously not only have the potential to produce better, historically grounded analytics but are also best suited to bridge with dialectic logic, thus taking advantage of the strengths of both forms of logic.

University campuses behave as complex systems, and sustainability in higher education is best seen as an emergent quality that arises from interactions both within an institution and between the institution and the environmental and social contexts in which it operates. A framework for strategically prioritizing campus sustainability work is needed. This paper seeks to address these issues.

First, a conceptual model is developed for understanding institutions of higher education as systems. Second, a leverage points framework is applied to experiences at the University of Vermont in order to evaluate campus sustainability efforts. Finally, real‐world examples are used to analyze and prioritize campus sustainability leverage points for advancing organizational change.

This systems thinking approach identifies key leverage points for actions to improve sustainability on campus. The leverage points framework is found to be valuable for: evaluating the potential of individual programs or actions to produce system‐wide change; coordinating individual programs into a strategic effort to improve the system; and making connections between campus and the surrounding social and environmental contexts. Advancing campus sustainability is found to be strengthened by particular ways of thinking and an organizational culture committed to continuous improvements and learning improved ways of doing business based on environmental and social, as well as institutional, benefits.

Campus sustainability workers must develop a prioritization process for evaluating which ideas to move forward on first. Systems thinking can cultivate our ability to consciously redesign and work with the systems that are in place, to intentionally pursue organizational improvements, and to plan and coordinate sustainability programs with potential for big changes.

This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is deployed for recognizing new unseen activities without access to the ground truth. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the recognition model without explicit detection of changes in the model performance.

The approach determines the variation between reference activity data belonging to different classes and newly classified unseen data. If there is coherency between the data, it means the model is correctly classifying the instances; otherwise, a significant variation indicates wrong instances are being classified to different classes. Thus, the approach is formulated as a two-level architectural framework comprising of the off-line phase and the online phase. The off-line phase extracts of Shewart Chart change parameters from the training data set. The online phase performs classification of new samples and the detection of the changes in each class of activity present in the data set by using the change parameters computed earlier.

The approach is evaluated using a real activity-recognition data set. The results show that there are consistent detections that correlate with the error rate of the model.

The developed approach does not use ground truth to detect classifier performance degradation. Rather, it uses a data discrimination method and a base classifier to detect the changes by using the parameters computed from the reference data of each class to discriminate outliers in the new data being classified to the same class. The approach is the first, to the best of the authors’ knowledge, that addresses the problem of detecting within-user and cross-user variations that lead to concept drift in activity recognition. The approach is also the first to use statistical process control method for change detection in activity recognition, with a robust integrated framework that seamlessly detects variations in the underlying model performance.

The extent to which advertisers choose to make more or less use of “informative copy”, or “copy points”, as a communicative and persuasive technique, represents a model of how an advertisement will work. With changing media technology, changing popular culture, and changing advertising fashions, it might be expected that models of effective advertising would change over time. Such changes would be reflected in the use of “copy points” in advertisements. Using a variant on the methodology developed by Resnik and Stern, extends research on the information content of advertising copy through a study of magazine advertisements over a period of 40 years. The number of copy points contained in an advertisement rose steadily from 1953 until the late 1970s. In the 1980s this trend was reversed, with the number of copy points falling sharply to below the 1953 level. While the observed fall in copy point level in the 1980s was expected, the long prior period of increase was not and may go some way to explain why the 1980s fall occurred.

This paper aims to present a new kinematics mapping method based on dynamic equivalent points. In teleoperation, this method enables a robotic (follower) arm to mimic human (leader) arm postures and avoid obstacles in a human-like manner.

The information of the human arm is extracted based on the characteristics of human arm motion, and the concept of equivalent points is introduced. Then, an equivalent point is determined to transform the robotic arm with a nonhuman-like kinematic structure into an anthropomorphic robotic arm. Based on this equivalent point, a mapping method is developed to ensure that the two arms are similar. Finally, the similarity between the human elbow angle and robot elbow angle is further improved by using this method and an augmented Jacobian matrix with a compensation coefficient.

Numerical simulations and physical prototype experiments are conducted to verify the effectiveness and feasibility of the proposed method. In environments with obstacles, this method can adjust the position of the equivalent point in real time to avoid obstacles. In environments without obstacles, the similarity between the human elbow angle and robot elbow angle is further improved at the expense of the end-effector accuracy.

This study presents a new kinematics mapping method, which can realize the complete mapping between the human arm and heterogeneous robotic arm in teleoperation. This method is versatile and can be applied to various mechanical arms with different structures.

The purpose of this paper is to develop a new model for depicting organizational processes: the episodic spiral model (ESM).

On the basis of a strong process view as the orienting paradigm, the authors demonstrate the need for the ESM by discussing the shortcomings of two specific spiral types in the organizational literature – the knowledge creation spiral and the efficacy spiral.

A review of each spiral type through the lens of nonlinear assumptions reveals the treatment to date of organizational spirals as uni-directional and insufficient for understanding organizations. The authors propose that managers must undertake a paradigm shift in order to gain a greater awareness of both the environment in which they operate, as well as their process actions. To facilitate this shift, the ESM depicts choice points, chosen and rejected trajectories, and upward and downward environmental drafts, as well as a multi-dimensional environment, as a way of re-conceptualizing approaches to space, time, and change in organization studies.

The authors propose that the model provides a way for scholars to enhance the study of organizations by understanding that organizations exist in a more dynamic environment than previously studied; recognizing that the organization has a wider range of choices available, and acknowledging the long-lasting ramifications of both choices made and choices discarded; and obtaining a more comprehensive look at the way the organization moves through space and time at any given moment. Taken together, the authors hope that these contributions allow organizational scholars a new approach to theorizing, exploring, and writing about the organizations they study.

This chapter addresses issues of what should be done and when. Structuring of the project task into a number of activities is key to planning a course of action for a project. It involves combining two dimensions: when (the time) and what (relevant subject areas or work paths). Several examples will illustrate the issue.

The five-by-five model will be used to identify five parallel processes of a project: (1) task-oriented processes concerned with development of a solution, (2) organisational change processes focusing on understanding and accepting the change, (3) application and operational processes concerned with application and use of the project’s results, (4) environmental processes focusing on the interplay between the project and its environment including stakeholders, and (5) management processes dealing with activities in the middle box of the five-by-five model.

A number of models of the project’s course of action will be presented and discussed, e.g., a waterfall model, a parallel stream model based on the four perspectives introduced in Chapter 1, the course of actions as decisions and as a change process. Finally, we shall discuss Agile and Lean project management.

The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM_2.5 through wavelet and regression analysis.

In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM_2.5 as a dependent variable.

It is found that few pollutants such as NO₂, NO_x, SO₂, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM_2.5. The regression model developed has high R² value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model.

The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM_2.5.

Control measures such as control charts can be implemented for significant factors.

Rules and regulations can be made more stringent on the factors.

The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.