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Standard economic theory predicts that any increase in public spending is accompanied by a rise in inflation in an economy. This paper presents empirical proof that prices do not always rise with an increase in public expenditure but only up to a certain threshold level. The primary aim of this paper is to unearth the government size-inflation nexus in India for the period from 1971 to 2019.

The logistic STAR (smooth transition autoregression) model is employed to unravel the government size-inflation nexus for the Indian economy from a non-linear perspective.

The finding of our study confirm the non-linear relationship between the size of the government and inflation in India. The estimated threshold level for government size is precisely found to be 9.27%. The size of the government exerts a negative influence on inflation until it reaches the optimal or threshold level. Any further increase in the size of government beyond this threshold level would result in a rise in inflation.

The findings have implications for the conduct of fiscal policy. Policymakers can increase government spending in a regime of small government size without having any inflationary impacts by generating revenues from taxes and other sources instead of relying much on the central bank. In the regime of a large-sized government, adhering strictly to the discipline in the conduct of fiscal and monetary policies would help curb inflation and enhance growth synchronously, hence alleviating any loss of welfare.

To the best of the authors’ knowledge, this study is an attempt to revisit the government size-inflation nexus in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model for the first time.

Utilizing the theoretical foundation of the stimulus-organism-response framework, the present work developed and investigated a conceptual model. The work explores the effects of perceived risk of COVID-19 on tourists' choice hesitation and choice confidence. Furthermore, it examines the impacts of choice hesitation and choice confidence on psychological distress, which, in turn, influences purchase intentions and risk-protective behavior. Additionally, the study assesses the boundary effects of vulnerability on the association between choice hesitation, choice confidence, and psychological distress.

An online survey was administered in China during COVID-19 to assess the postulated hypotheses. We collected 491 responses using purposive sampling, and covariance-based structural equation modeling (CB-SEM) was performed to investigate the relationships.

Results show that the perceived risk of COVID-19 positively influences the choice hesitation and negatively impact choice confidence. It was also found that choice hesitation and choice confidence positively developed psychological distress, which, in turn, negatively triggered purchase intentions and positively developed risk-protective behavior. Additionally, perceived vulnerability had a significant moderating impact on the proposed relationships, strengthening psychological distress.

In the current context, this study measures bipolar behavioral outcomes using the S-O-R model. Because cognitive processes influence participation in health preventative behavior during the spread of diseases, we highlighted how the perception of risk and vulnerability to a pandemic serves as a reliable indicator of certain behaviors. This study advances understanding of how the psychological mindset of tourists copes with such circumstances. Due to the pandemic, tourists face limitations in their choices and are placing greater emphasis on adopting protective measures to mitigate associated risks.

Properly planned road illumination systems are collectively a public wealth and the commissioning of such systems may require extensive planning, simulation and testing. The purpose of this simulative work is to offer a simple approach to facilitate luminance-based road lighting calculations that can be easier to comprehend and apply to practical designing problems when compared to complex multi-objective algorithms and other convoluted simulative techniques.

Road illumination systems were photometrically simulated with a created model in a validated software platform for specified system design configurations involving high-pressure sodium (HPS) and light-emitting diode (LED) luminaires. Multiple regression analyses were conducted with the simulatively obtained data set to propound a linear model of estimating average luminance, overall uniformity of luminance and energy efficiency of lighting installations, and the simulatively obtained data set was used to explore luminaire power–road surface average luminance characteristics for common geometric design configurations involving HPS and LED luminaires, and four categories of road surfaces.

The six linear equations of the propounded linear model were found to be well-fitted with their corresponding observation sets. Moreover, it was found that the luminaire power–road surface average luminance characteristics were well-fitted with linear trendlines and the increment in road surface average luminance level per watt increment of luminaire power was marginally higher for LEDs.

This neoteric approach of estimating road surface luminance parameters and energy efficiency of lighting installations, and the compendia of luminaire power–road surface average luminance characteristics offer new insights that can prove to be very useful for practical purposes.

Institutional framings of doctoral education mostly do not recognize the existential dimension of doctoral experience. This paper aims to offer an expanded understanding of experiences of doctoral researchers in the humanities with the concept of entangled becoming. This concept is developed through an existential lens by using Søren Kierkegaard’s philosophy – particularly his emphasis on emotions such as passion, anxiety and despair – and Denise Batchelor’s derived concept of vulnerable voices.

The conceptual framing is used for an empirical study based on ethnographic interviews with 10 doctoral researchers and supplementary observational notes from fieldwork at a university in Denmark. Two of the interview cases were selected to showcase variation across lived experiences and how doctoral researchers voice their entangled becoming.

Common experiences such as loneliness, insecurity(ies), vulnerability(ies) or passion for one’s research were identified across the interviews. On the other hand, this study shows that each doctoral journey in the humanities envelops a distinct web of entanglements, entailing distinct navigation, that makes each case a unique story and each doctoral voice a specific one.

Combining an existential philosophical perspective with a qualitative study, the paper offers an alternative perspective for doctoral education. It connects the humanities doctoral experience to the broader condition of human existence and the sophisticated uniqueness of each researcher’s becoming.

Understanding the role of corporate strategies in sustainability has become a hot topic for scholarly research. Meanwhile, firms strive to innovate and shape their positive image in the contemporary business arena. Past research has ignored investigating whether and how sustainability-oriented corporate strategies could drive innovation and firm image among external stakeholders. To address the said research gap, this paper examines the path through which sustainability-oriented corporate strategy and environmental regulation improve green corporate image and green innovation capabilities (i.e. green process and product innovation).

This study adopted a quantitative survey-based method. The online survey was adopted to collect data from employees working at the managerial level in the equipment manufacturing sector. The data collected from 343 managers that was complete in all aspects was used for empirical analysis using structural equation modeling. Direct and indirect relations were evaluated.

The findings reveal that sustainability-oriented corporate strategy and environmental regulation drive green innovation and green corporate image. Findings further show that external knowledge adoption underpins these effects of sustainability-oriented corporate strategy and environmental regulation.

The study delivers theoretical and practical understandings of the importance of sustainability-oriented corporate strategies to green corporate image and green innovation capabilities.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

Teamwork is necessary for engineering to address today’s complex challenges. Therefore, team members must improve their teamwork competencies for more significant team development and effectiveness. This study aimed to analyze how a non-directive coaching intervention model for an entire team influences the individual team members’ teamwork competencies.

Action research was used in this study with a quasi-experimental design featuring control and experimental groups comprising final-year engineering students from Universidad Politécnica de Madrid. The sample included 168 students, with 132 in the control group and 36 in the experimental group. The experimental group underwent a non-directive team coaching intervention involving three sessions. Competencies were evaluated using the teamwork competency test (TWCT), administered at the course’s beginning and end to measure progress.

The results show that the individuals who participated in the team coaching significantly increased their competencies, particularly “conflict resolution” and “feedback.”

This study’s value contributes to identifying the positive impacts of non-directive team coaching interventions on individual teamwork competencies, fostering collaborative skills and supporting collective goals.

Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.