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This study investigates the extent to which a firm’s centrality and autonomy in its supply network are associated with the intensity and complexity of its competitive actions.

Utilizing social network analysis and dynamic panel data models, this study analyzes a comprehensive panel dataset with 10,802 firm-year observations across various industries between 2011 and 2018 to test the hypotheses.

Our findings show that a firm’s level of centrality in its supply network has an inverted U-shaped relationship with both competitive intensity and competitive complexity. In addition, the turning points of these two inverted U-shaped relationships differ in that firms with a lower level of centrality tend to compete aggressively by launching more actions within fewer categories, while firms with a higher level of centrality tend to compete aggressively by launching fewer actions that cover a larger range of categories. Finally, we find that a firm’s structural autonomy has a positive relationship with competitive complexity.

This study bridges the gap between the supply chain management literature and strategic management literature and investigates how supply networks shape competitive aggressiveness. In particular, this research investigates how a firm’s structural position in its supply network affects its competitive actions, an important intermediate mechanism for competitive advantage that has been overlooked in the supply chain management literature.

The purpose of this paper is twofold: the authors initially survey a sample of literature published after the Great Recession that address macroeconomic and commercial real estate forecasting methods related to the Great Recession and compare significant lessons learned, or lack thereof. The authors then seek to identify new models to improve the predictability of commercial real estate early warning signals regarding cyclical turning points which result in negative appreciation rates.

The authors develop a probit model to estimate quarterly probabilities of negative office appreciation returns using an alternative methodology to Tsolaco et al. (2014). The authors’ alternative method incorporates generally publicly available macroeconomic and real estate variables such as gross domestic product, office-related employment sectors, cap rate spreads, and commercial mortgage flow of funds into a probit model in order to estimate the probability of future quarterly negative office appreciation rates.

The authors’ models demonstrate the predictive power of macroeconomic variables typically associated with office demand. The probit model specification shows probabilities of negative office appreciations rates greater than 50 percent either as the quarterly office returns become negative, or in some cases several quarters before office returns become negative, for both the Great Recession and the recession occurring in the early 1990s. The models fail to show probabilities greater than 50 percent of negative office returns until after they occur for the recession in 2001. While this indicates need for further improvement in early warning models, the models do predict the more severe periods of negative office returns in advance, indicating the findings useful to real estate investors to monitor the changes in economic and real estate data identified as statistically significant in the results.

The Great Recession is a unique laboratory of significant contractions, recessions, and recoveries that challenge pre-recessionary real estate cycle models. The models provide guidance on which historical economic indicators are important to track, and gives a framework with which to calculate the probability that office prices are likely to decline. Because the models use macroeconomic indicators that are publicly available from at least one quarter in the past, the models or variations of them may provide real estate professionals with some indication of an impending decrease in office prices, even if that indication comes only one quarter in advance. Armed with this information, property owners, investors, and brokers can make more informed decisions on whether to buy or sell, and how sensitive their real estate transactions may be to timing.

The authors introduce several new models that examine the ability of historical macroeconomic indicators to provide early warning signals and identify turning points in real estate valuations, specifically negative office appreciation rates caused by the Great Recession. Using data from at least one quarter in the past, all the data in the models are publicly available (excluding National Council of Real Estate Investment Fiduciaries data) at the observed return quarter being predicted, which gives practitioners rational insights that can provide at least one source of guidance about the likelihood of an impending decrease in office prices.

The chapter discusses the question of social justice in social science research by problematizing the researcher-research content relationship and its guiding principle framework Science-Society-Me. With a focus on early career researchers, the author draws on her own PhD research experience to highlight the social justice tension inherent in the normative approaches and methods for selecting research topic, collecting data and relating with research participants, and analyzing and interpreting data especially in empirical research with fellow human beings. Drawing on the theory of affect, the chapter centralizes the position, biography and experience of the researcher, and the relationship between the researcher and the research participants to balance out the privileged (power) position of ‘science’ and ‘society’ in the current framework. To this end, I make two ontological and methodological adjustment recommendations: (1) Relocating the space of research from a fixed and exclusive ‘location’ of science theories, methods, concepts etc., to the space of daily life experience and interaction, requiring a compassionate approach to research, and thus the second recommendation; (2) Developing long-term, inclusive and equal relationships with research participants augmented by constant reflexivity – as including the biographies and experiences of the researcher and the research participant – as well as political reflexivity.

This paper aims to detect the most profitable, i.e. optimal turning points (TPs), from the history of time series using a binary integer programming (BIP) model. TPs prediction problem is one of the most popular yet challenging topics in financial planning. Predicting profitable TPs results in earning profit by offering the opportunity to buy at low and selling at high. TPs detected from the history of time series will be used as the prediction model’s input. According to the literature, the predicted TPs’ profitability depends on the detected TPs’ profitability. Therefore, research for improving the profitability of detection methods has been never given up. Nevertheless, to the best of our knowledge, none of the existing methods can detect the optimal TPs.

The objective function of our model maximizes the profit of adopting all the trading strategies. The decision variables represent whether or not to detect the breakpoints as TPs. The assumptions of the model are as follows. Short-selling is possible. The time value for the money is not considered. Detection of consecutive buying (selling) TPs is not possible.

Empirical results with 20 data sets from Shanghai Stock Exchange indicate that the model detects the optimal TPs.

The proposed model, in contrast to the other methods, can detect the optimal TPs. Additionally, the proposed model, in contrast to the other methods, requires transaction cost as its only input parameter. This advantage reduces the process’ calculations.

This paper aims to discuss the constitution of the Memorial of Resistance of São Paulo (MRSP) by adopting the ANTi-History approach, thereby providing greater transparency to the socio-political relations of multiple actors involved in the constitution of this place as a site of memory.

By adopting the principles of ANTi-History, the researchers focused on the socio-politics of different human and nonhuman actors to examine how this site of memory was constituted. The researchers sought to turn the process of constituting the MRSP more transparent, putting together one of the possible historical versions of this phenomenon. The data collected included oral and documental sources (interviews, videos, books, newspapers and websites).

The findings support the notion of history as a socially constructed narrative that emerges through associations of heterogeneous actors in a dynamic and continuous process of (re)configuration. Additionally, by exposing the power relations and negotiations manoeuvres used by multiple actors, it allowed the researchers to highlight the complexity of the Memorial’s history as a black box, contextualizing them in a period of intense social upheaval, re-democratization and transitional justice within Brazilian society.

The paper outlines the process of listing the DEOPS/SP building and the social mobilization movements during the 1980s and 1990s in Brazil, illustrating some of the results obtained from a more extensive research project that dealt with the MRSP’s constitution process. It offers an in-depth example of using the ANTi-History approach in Organizational Studies, allowing the researchers to remove the organization’s veil of apparent simplicity and to bring back to life voices and actors that have been erased, disguised and silenced by the dominant version of events.

Although as an example of “moonlighting” public house entertainment can be categorised as part of the irregular economy, all these terms tend to lose their rigour when confronted with the complexity of empirical reality. The occupational status of public house entertainers differs greatly (for some, a second/part‐time occupation, for others, their only occupation), and shifts in status are common. An important benchmark in such careers is the point at which performers turn professional, but this status‐passage is usually informal and is influenced by the individual's evaluation of their day‐job, perception of professionals‘ lives, availability of opportunities, and commitment to entertaining. Twin job‐holding activity demonstrates a rich occupational complexity, and the balance between two jobs, in such performers’ lives, is liable to change, as are the reasons for participation.

The purpose of this paper is to present work which is a part of the Comprehensive Automation for Specialty Crops project (CASC). Desired trajectory tracking objective has been previously performed by using a non‐model based approach in this project. Long distance autonomous drive has been achieved; however the results haven't met the expectations of the project requirements. In order to provide these requirements, this study is conducted. In this study, long distance autonomous trajectory tracking for an orchard vehicle is studied. Besides longitudinal motion, lateral motion of the vehicle is also considered. The longitudinal and lateral errors are objected to keep into a region of less than 10 cm.

Car‐like robot kinematic modeling approach is used to create desired trajectory. In order to control longitudinal velocity and steering angle of the vehicle, a controller methodology is proposed. Stability of the controller proposed is shown by using Lyapunov stability approach.

The proposed model is adapted into a four‐wheeled autonomous orchard vehicle and tested in an experimental orchard for long distance autonomous drives. More than 15 km autonomous drive is successfully achieved and the details are presented in this paper.

In this study, long distance autonomous trajectory tracking for an orchard vehicle is focused. A model based control strategy, including the information about longitudinal and lateral motion of the vehicle, is constructed. A new approach to create steering angles for turning operations of the orchard vehicle is introduced. It is objected that the longitudinal and lateral errors should be less than 10 cm during the trajectory tracking task.

The purpose of this paper is to develop an automated human body measurement extraction system using simple inexpensive equipment with minimum requirement of human assistance. This research further leads to the comparison of extracted measurements to established methods to analyze the error. The extracted measurements can be used to assist the production of custom-fit apparel. This is an effort to reduce the cost of expensive 3-D body scanners and to make the system available to the user at home.

A single camera body measurement system is proposed, implemented, and pilot tested. This system involves a personal computer and a webcam operating within a space of controlled lighting. The system will take two images of the user, extract body silhouettes, and perform measurement extraction. The camera is automatically calibrated using the software each time of scanning considering the scanning space. The user will select a front view and a side view among the images captured, and specify the height. In this pilot study, 31 subjects were recruited and the accuracy of 8 human body measurements were compared with the manual measurements and measurements extracted from a commercial 3-D body scanner.

The system achieved reasonable measurement performance within 10 percent accuracy for seven out of the eight measurements, while four out of eight parameters obtained a performance similar to the commercial scanner. It is proved that human body measurement extraction can be done using inexpensive equipment to obtain reasonable results.

This study is aimed at developing a proof-of-concept for inexpensive body scanning system, with an effort to benchmark measurement accuracy, available to an average user providing the ability to acquire self-body measurements to be used to purchase custom-fit apparel. This system can potentially boost the customization of apparel and revolutionize online shopping of custom-fit apparel.

The main purpose of this research is exploring the tipping points for a radical shift in supplier (dis)satisfaction. This study identifies triggers and links them to consequences for the buyer–supplier relationship.

The Critical Incident Technique (CIT) was used to interview Dutch supplier representatives in the infrastructure sector, resulting in rich descriptions of 29 critical incidents, extracting first-hand information.

Safety issues, technical disputes and recruitment of supplier’s technical staff have been identified as tipping points for suppliers to become dissatisfied. Implementing performance-based contracting is another critical incident that caused irritation and disappointment. On a more operational level, dissatisfaction was provoked by tender errors and price discussions with the buyer. This study also identified tipping points by which dissatisfied suppliers abruptly turned into satisfied suppliers. The effect of a solution-oriented buyer intervention appears to be most powerful if this behaviour transcends prior expectations.

Consequences of misunderstandings and discussion between supplier and buyer may be manageable or repairable, depending on the causes and triggers that influenced a supplier’s dissatisfaction. An early warning system could prove its worth, so that buyers are not faced with unpleasant surprises.

Despite the growing number of studies, processes of how antecedents lead to supplier (dis)satisfaction are not well understood. Antecedents are predominantly investigated by cross-sectional survey data, giving little insights into micro-processes and actual interaction between buyers and suppliers. Although CIT has been applied in many disciplines, the technique is hardly used within the context of purchasing and supply management research.