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Drawing on the theory of planned behavior and value-belief norms theory, this study aims to explore the causes of pro-environmental behavioral intentions in the context of Sri Lanka.

Data were garnered from 503 subjects with the aid of self-reported questionnaires. The formulated hypotheses were examined using a variance-based statistical approach (PLS-SEM).

The findings of the study revealed that personal values, particularly altruistic and biosphere values, significantly strengthen the psychological drivers of pro-environmental behavioral intentions. The study further found that religiosity enhances the association between personal norms and pro-environmental behavioral intentions and the place attachment augments the relationship between attitude and pro-environmental behavioral intentions.

This study suggests that instilling altruistic and biosphere values in individuals can boost pro-environmental behavioral intentions. Campaigns and educational programs could focus on instilling these values to foster responsibility and empathy. Recognizing the influence of religiosity on pro-environmental behaviors and attitudes, this study suggests collaborative efforts between organizations and religious institutions. Religious leaders can integrate environmental stewardship into teachings, emphasizing ethical responsibilities.

The study pushes back the frontiers of environmental consciousness literature by highlighting the importance of personal values, psychological factors and contextual variables such as religiosity and place attachment in fostering pro-environmental behavior within the specific context of an emerging country – Sri Lanka.

In this chapter, the authors critically examine the application of unmeasured latent method factors (ULMFs) in human resource and organizational behavior (HROB) research, focusing on addressing common method variance (CMV). The authors explore the development and usage of ULMF to mitigate CMV and highlight key debates concerning measurement error in the HROB literature. The authors also discuss the implications of biased effect sizes and how such bias can lead HR professionals to oversell interventions. The authors provide evidence supporting the effectiveness of ULMF when a specific assumption is held: a single latent method factor contributes to the data. However, the authors dispute this assumption, noting that CMV is likely multidimensional; that is, it is complex and difficult to fix with statistical methods alone. Importantly, the authors highlight the significance of maintaining a multidimensional view of CMV, challenging the simplification of a CMV as a single source. The authors close by offering recommendations for using ULMFs in practice as well as more research into more complex forms of CMV.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

The study aims to fulfil two objectives, namely, to explore what dimensions comes into play in the current consumer EV purchase behaviour based on relevant literature identified in the past and to find out whether there are other reasons that are peculiar to the Indian automobile market which influenced consumers to adopt this eco-friendly technology.

Mixed method approach for collecting the primary data was used. The study was conducted in five cities across India, through a questionnaire administered to 266 existing EV owners using judgemental sampling, that included dimensions as to what made them purchase EVs (any variant whether hybrid or battery) and encouraged their green behaviour. Moreover, the survey also took care of gathering other motivational constructs that may have played a significant role in the Indian EV market. To take care of this, use of single open-ended question was done. Statistical techniques like exploratory factor analysis (EFA) and Partial Least Square-Structural Equation Modelling (PLS-SEM) version 4.0 were performed to gain meaningful insights from this investigation.

Based on our analysis, EV purchase behaviour in the Indian market could be explained by six configurational paths identified in conceptual framework of this study. The data analysed revealed long-term operational benefits or relative advantages, trialability or prior driving experience and positive attitude towards EVs playing an important role towards EV purchase decision followed by environmental concern, social norms and personal values of Indian consumers. Besides, several other factors were identified in open comments like favourable government policies, brand consciousness, compatibility and functional benefits in terms of comfortable and noiseless driving which tend to drive the decision of Indian consumers towards buying EVs. The identification of these motivations can help policymakers in targeting the customer market accordingly.

Although the study is restricted by its scope of sampling criteria, yet is distinct in its methodological approach and thereby adds value to existing literature on marketing and diffusion of EVs. The study explores the untapped gap in the current literature by helping identify reasons on the basis of which consumers made their choices of different EVs. Instead of analysing the potential antecedents of EV uptake, the study brings to light the actual reasons of EV purchase behaviour in the context of an emerging economy like India. That is to say, the horizon of EV motivations spans across technological, individual and regulatory attributes in the Indian automobile market.

This study is relevant and fulfils the underlying gap in the existing literature related to actual motivations that consumers look for before buying a high involvement product such as EVs. The results can be beneficial for marketers to design effective marketing strategies from the perspective of consumers. Promotional strategies addressing ease of using EVs and how battery technology is better than before needs to be clearly communicated by the marketers. Consumer education is necessary, in this regard, to make people aware of the rising air pollution levels. They have to be made understood the real advantages of owning EVs and how these non-conventional vehicles are far better when compared to traditional cars. In a nutshell, the study underscored potential determinants or reasons that made Indian consumers to go for EVs, based on statistically significant evidences which can be crucial for stakeholders operating in the Indian EV industry.

The study has important implications for members of the community. By sacrificing their egoistic tendencies or individual preferences for energy intensive products such as ICE based vehicles, people can imbibe altruistic orientation and can contribute to the collective benefit of all by shifting towards energy rich vehicles powered by electricity. This is necessary to achieve the broader goals of sustainable development and to curtail the rising climate change phenomenon.

The study applied both quantitative (deductive) and qualitative (inductive) approach to study into the reasons for consumers’ purchase motivations towards electric cars. Taking a distinct methodological approach, the study enhances the knowledge on EVs by asking consumers to comment in their own words as to why they purchased EVs. This was necessary so as to better explore the needs of the Indian automobile market and which factors govern the purchase of EVs in domestic market. Past literature majorly focused on purchase intention towards EVs but this study in unexampled and analysed the purchase behaviour towards EVs. It should be noted that our investigation reveals the complexity of EV purchase reasons than those identified in past literature and it adds to the current pool of literature on EVs by explaining factors with unrivalled illustrative power.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

This paper aims to explore three operations and supply chain management (OSCM) approaches for meeting the 2 °C targets to counteract climate change: adaptation (adjusting to climatic impacts); mitigation (innovating towards low-carbon practices); and carbon-removing negative emissions technologies (NETs). We suggest that adaptation nor mitigation may be enough to meet the current climate targets, thus calling for NETs, resulting in the following question: How can operations and supply chains be reconceptualized for NETs?

We draw on the sustainable supply chain and transitions discourses along with interview data involving 125 experts gathered from a broad research project focused on geoengineering and NETs. We analyze three case studies of emerging NETs (biochar, direct air carbon capture and storage and ocean alkalinity enhancement), leading to propositions on the link between OSCM and NETs.

Although some NETs are promising, there remains considerable variance and uncertainty over supply chain configurations, efficacy, social acceptability and potential risks of unintended detrimental consequences. We introduce the concept of transformative OSCM, which encompasses policy interventions to foster the emergence of new technologies in industry sectors driven by social mandates but lack clear commercial incentives.

To the best of the authors’ knowledge, this paper is among the first that studies NETs from an OSCM perspective. It suggests a pathway toward new industry structures and policy support to effectively tackle climate change through carbon removal.

Emojis are quickly becoming a popular new language in social media and marketing. The capability to express emotions and make message understanding easier is one of the primary reasons for using emojis. The aim of this research was to determine the influence of perceived usefulness, perceived ease of use, trust, and involvement on customer engagement due to emojis used in digital marketing communications among Generation Z (Gen Z) in South Africa.

Following the descriptive research approach, quantitative research was used in this study. A questionnaire (self-administered) was utilized to test the effectiveness of using emojis among 1,000 young consumers. Structural equation modeling was used to test the hypotheses.

The findings of the study yielded positive relationships between the variables, namely between trust and involvement; involvement and the perceived ease of use; involvement and perceived usefulness; perceived ease of use and perceived usefulness; trust and customer engagement; perceived usefulness and customer engagement; involvement and customer engagement; customer engagement and intention to purchase; trust and intention to purchase; and perceived usefulness and intention to purchase.

This study can help organizations in emerging markets use emojis in their digital marketing communications to engage customers and stimulate intention to purchase among young people, especially the Gen Z cohort, who seek organizations and brands that understand and connect with them.

By investigating the effects of emojis in digital marketing communications, this study contributes to the customer-centric process and the literature on emoji usage while also involving a credible digital language when communicating with members of Gen Z. By extending TAM, the findings of this study contribute to the TAM literature by demonstrating that emoji usage in digital marketing communications positively influences various attitudinal associations among Gen Z consumers.

The article aims to establish whether the degree of aversion to inflation and the responsiveness to deviations from potential output have changed over time.

This paper assesses time variation in monetary policy rules by applying a time-varying parameter generalised methods of moments (TVP-GMM) framework.

Using monthly data until December 2022 for five inflation targeting countries (the UK, Canada, Australia, New Zealand, Sweden) and five countries with alternative monetary regimes (the US, Japan, Denmark, the Euro Area, Switzerland), we find that monetary policy has become more averse to inflation and more responsive to the output gap in both sets of countries over time. In particular, there has been a clear shift in inflation targeting countries towards a more hawkish stance on inflation since the adoption of this regime and a greater response to both inflation and the output gap in most countries after the global financial crisis, which indicates a stronger reliance on monetary rules to stabilise the economy in recent years. It also appears that inflation targeting countries pay greater attention to the exchange rate pass-through channel when setting interest rates. Finally, monetary surprises do not seem to be an important determinant of the evolution over time of the Taylor rule parameters, which suggests a high degree of monetary policy transparency in the countries under examination.

It provides new evidence on changes over time in monetary policy rules.

The purpose of this paper is to investigate the role of Customer Participation (CP) in the effectiveness of New Service Development (NSD) by examining the moderating roles of Customer Empowerment (CE) and Customer Satisfaction (CS). The research reduces the risk of failure of the NSD process and/or improves the NSD processes used by companies through the consideration of the results in the practical dimension.

This study investigates the effects of CP at different stages of NSD using a quantitative approach. Data were collected through an online survey questionnaire. Smart PLS was used to analyse the data collected from 509 newsreaders and users of the news agency’s application.

The model confirmed that CE has an impact on the effectiveness of NSD in the idea generation and commercialization stages, but not in the development stage. Empowerment and customer satisfaction did not influence the three stages of NSD indirectly but directly. The results show that CP, CS and CE do not always have a direct or indirect effect on the development of new services. Therefore, in order to design new service development projects, media news companies need to determine the level of user cooperation.

The lack of objective data, especially on company performance, forces researchers to use questionnaires to analyse NSD effectiveness. Another limitation is that newspaper users answered the questionnaires, which creates “common method variance.”

Researchers on NSD effectiveness must use questionnaires due to a lack of objective data, especially on company performance. Another limitation is “common method variance” from newspaper users answering questionnaires.

This paper is a response to a perceived need for an examination of how new service development can be successful and effective.