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This chapter provides an overview of Freire's primary approach to how governments control people, why governments dictate what is being taught and why they seek out control over educators. This chapter investigates the similarities Freire found between the most oppressive governments and dictatorships that rules by violence, to how our educational institutions are controlled. The stakes might be entirely different in these scenarios, but he knew the ultimate objective in all these situations was to remove power from the people, give it to a powerful few and ensure the people would never be able to regain control once more. These ideas and suggestions are key moving forward because it provides a baseline to understand how Freire thought about manipulative behaviours, and why tactics used in extremely violent situations can essentially be watered down and used to control people in universities in much the same way.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

The purpose of this study is to extend Bippus and Young’s (2005) study and examine the effectiveness of the “I-you,” “I,” “You,” “We,” “But” and Question-based “Why” statements from Winer’s (2021) verbal coding program of conflict management using Bandura’s (1977) social learning theory (SLT).

Mixed methods were used using 175 university students from Texas and New York. A cross-sectional convenience sampling approach was conducted. Survey data was collected using Qualtrics.

Descriptive results demonstrated that the “We” statement was the most passive, the “I-you” statement was the most assertive and the “But,” “I,” “You” and Question statements were perceived to be aggressive. In addition, assertive “I-You” statements were perceived to be more effective in resolving the conflict and maintaining a relationship, whereas aggressive statements were less likely to resolve the conflict and maintain the relationship. Qualitative themes also support the “I-You” statement as the most assertive, while the “But,” “You” and “I” statements were found to be the most aggressive statements.

Implications and applications are discussed to stimulate future research among researchers and practitioners when addressing conflict. Being aware of the verbal statements that de-escalate conflict may be helpful in solving conflict in interpersonal, family and professional relationships. Future trainings can adopt effective verbal statements to resolve conflict when experiencing anger issues. Future research can continue to investigate verbal communication statements using SLT to help practitioners and managers address conflict in interpersonal relationships.

This study examines verbal statements in relation to communication styles and conflict management.

The case describes a hypothetical hedge fund manager who is examining whether to invest in bitcoin. The case discusses potential risks and rewards of investing in bitcoin, the role of bitcoin and digital currencies more broadly, and financial innovation in the space, such as ICOs. It can be taught as part of a second-year MBA elective course in investments, financial institutions/capital markets, or fintech.

This study aims to analyze the determinants influencing Chinese manufacturing companies in implementing lean manufacturing (LM).

The determinants to be explored in this study consist of technological, organizational and environmental (TOE) dimensions. A questionnaire survey was conducted on Chinese manufacturing companies, and 208 samples were analyzed.

The findings show that the relative advantage of LM and organizational support have significantly positive effects on Chinese manufacturing firms’ adoption of LM. The complexity of LM, quality of human resources, organizational readiness, customer pressure, international situation, governmental support and environmental uncertainty do not have significant effects.

This paper contributes to the literature by using the TOE model to explore the factors influencing LM adoption in the Chinese manufacturing industry.

The aim of this study is to investigate the extent to which organizational justice (OJ) mediates between responsible leadership (RL) and employee turnover intention (TI).

Both online and offline questionnaire was used to collect the data from 387 Indian health-care employees, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM) with the help of SmartPLS 4.

The study’s findings demonstrated a significant positive association between RL and OJ and a negative association between OJ and employee TI. Furthermore, results also confirmed the mediating role of OJ between RI and TI.

The generalizability of the study‘s data collection is limited because it is based on the responses of Indian health-care sector employees to an online and offline survey. The authors propose that the health-care sector uses RL as an approach that takes a broad view of the parties with a stake and focuses on creating fairness in acts and justice at the workplace to address the major issue of employee turnover.

This study expanded on previous research by demonstrating that the influence of responsible leadership on employee TI is mediated by OJ in the context of India’s health-care sector. It also contributes to the literature regarding RI, OJ and TI. The study also enriched the body of knowledge about using the PLS-SEM approach to predict employee TI.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

The purpose of this paper is to examine the purchase behavior of consumers towards green products by adapting and extending the theory of planned behavior with the inclusion of three pertinent environmental factors posited to reflect environmental consciousness in the form of environmental concern, environmental knowledge and environmental values.

The data was collected from 410 consumers at shopping malls with retail stores selling green and non-green products in a developing country using cluster sampling and analyzed using covariance-based structural equation modeling.

The findings of this study indicate that environmental factors reflecting environmental consciousness positively influence consumers’ attitude towards purchasing green products, wherein consumers’ environmental values have a stronger influence than their environmental concern and environmental knowledge. The findings also reveal that subjective norm, attitude and perceived behavioral control toward purchasing green products positively shape green purchase intention. The same positive effect is also witnessed between green purchase intention and behavior. However, perceived behavioral control towards purchasing green products had no significant influence on green purchase behavior.

This study suggests that green marketers should promote environmental consciousness among consumers to influence and shape their planned behavior towards green purchases. This could be done by prioritizing efforts and investments in inculcating environmental values, followed by enhancing environmental knowledge and finally inducing environmental concern among consumers. Green marketers can also leverage subjective norm and perceptions of behavioral control toward purchasing green products to reinforce green purchase intention, which, in turn, strengthens green purchase behavior. This green marketing strategy should also be useful to address the intention–behavior gap as seen through the null effect of perceived behavioral control on purchase behavior toward green products when this strategy is present.

This study contributes to theoretical generalizability by reaffirming the continued relevance of the theory of planned behavior in settings concerning the environment (e.g. green purchases), and theoretical extension by augmenting environmental concern, environmental knowledge and environmental values with the theory of planned behavior, resulting in an environmentally conscious theory of planned behavior. The latter is significant and noteworthy, as this study broadens the conceptualization and operationalization of environmental consciousness from a unidimensional to a multidimensional construct.