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The purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM).

The 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response.

The simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application.

The proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy.

This chapter examines the influence of external public borrowing resources on economic progress in Tunisia. The study focuses on two stages: First, the influence is studied in a direct sense and then in an indirect sense, i.e., through a transmission channel of this influence. By applying the autoregressive distributed technique with staggered lags (ARDL), over a period ranging from 1986 to 2019, the results showed that the influence of external borrowing resources on growth seems to be unfavorable in the short term but positive in the long term, hence the importance of the empirical technique chosen. Second, three interaction variables were tested, namely total government expenditure, government investment expenditure, and the real effective exchange rate. The results obtained call for better attention to the channels identified to maximize the positive influence of external public debt on the country's economic progress.

This study aims to use emotions-as-social information theory to investigate how physical (customer perceived store atmosphere) and social servicescapes (customer information searching) influence the effects of employees’ positive affective displays on customer outcomes via customer positive moods and positive expectation disconfirmation.

This research included two studies, each using a distinct research design, to empirically test the proposed model. Study 1 involved 200 observational data points on objective purchase amounts from designer watch shops. In Study 2, data were collected from 230 customers in designer jewelry stores.

The results of path analyses revealed that: employee positive affective displays are positively associated with customer purchase outcomes; employee positive affective displays had positive indirect effects on customer purchase outcomes by enhancing customer positive moods and positive expectation disconfirmation; these positive indirect effects were strengthened when customers engaged in information search behaviors; and these positive indirect effects were attenuated when customers perceive store atmosphere as favorable, indicating a substitution effect of customer perceived store atmosphere.

Previous research has not thoroughly examined the role of the servicescape in moderating the effects of employees’ positive affective displays on customer purchase outcomes. This present study not only clarified the affective and cognitive mechanisms that link employees’ positive affective displays on purchase outcomes but also identified servicescape as a critical boundary condition of these effects.

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

Firms are driven to ride on the digital wave in today’s open innovation ecosystem. This study aims to explore the effect of digital transformation (DT) on knowledge-intensive business services (KIBS) firms’ innovation ambidexterity, namely, radical versus incremental innovation, respectively. Meanwhile, the authors evaluated the moderating role of the complexity of R&D collaboration portfolio (i.e. organizational diversity and geographic diversity) in the above relationships.

Using a panel data set of 171 Chinese listed firms in the information and communications technology services industry from 2010 to 2018, the proposed hypotheses were empirically attested.

It is found that DT has a positive relationship with radical innovation and an inverted U-shaped relationship with incremental innovation. In terms of the R&D collaboration portfolio, organizational diversity positively moderates the relationships between DT and innovation ambidexterity, respectively. The geographic diversity weakens the inverted U-shaped effect of DT on incremental innovation; however, its moderating role in the link between DT and radical innovation is not empirically verified.

Extant scholars mainly addressed the interplay between KIBS firms and their manufacturing clients, while this study reveals the different consequences of DT on KIBS firms’ innovation ambidexterity to highlight the role of KIBS firms is an independent and essential innovator in a knowledge-driven economy. Notably, the findings contribute to knowledge management (KM) and R&D literature by confirming the diversity of the R&D collaboration portfolio is a critical KM strategy for KIBS firms to develop and promote external knowledge resources.

The vast amounts of waste generated today threaten economies and societies due to high environmental and management costs. The aim is to investigate the short- and long-term patterns of municipal waste generation (MWG) in response to socio-economic and demographic growth variables at national and regional levels.

A panel data approach employing ordinary least squares (OLS), fixed effects (FE), random effects (RE), fully modified least squares (FMOLS) and error correction model (ECM) techniques. A sample of 28 European countries (2000–2020) and 44 European Union (EU) regions (2000–2018) were selected.

During periods of economic growth and higher employment rates, consumer confidence tends to increase, leading to elevated levels of consumer spending and consumption. Intensification in the production factors, specifically capital and employment, results in an upsurge in MWG, thereby creating a cycle where waste generation becomes deeply entrenched in the economic system in both the short and long terms. Rapid population growth, attributed to higher fertility rates, is associated with increased MWG. At the regional level, a double-aging process and a shift toward an aging population exert less pressure on MWG in both the short and long term. Promoting higher levels of environment-oriented human development yields various benefits, including the generation of greater knowledge spillovers, enhanced environmental literacy, a shift toward circular thinking and the promotion of greener entrepreneurship. Increased R&D expenditures facilitate the development of innovative waste reduction technologies, fostering improvements in waste management techniques, recycling processes and the utilization of sustainable materials.

The research examines the short- and long-term adjustments of MWG in response to changes in macroeconomic variables from low aggregation (countries) to high aggregation (regions). By analyzing the relationship between economic growth, urbanization, healthcare system quality, labor market functioning, demographic trends, educational level, technological advancement and MWG, the study fills a research gap and enhances understanding of waste management interventions. However, data availability and waste statistics accuracy should be considered. Future research could explore the relationship between macroeconomic variables and waste generation in sectors beyond MWG, such as industrial or construction waste, for a more comprehensive understanding of waste generation as a whole.

The positive correlation between economic activity levels and waste generation in both the short and long terms, emphasizes the criticality of investing in waste reduction and recycling infrastructure to mitigate landfill waste. The negative correlation between population density and waste generation stresses the importance of strategic waste facility placement in low-density areas. To effectively manage higher MWG, tailored waste collection systems and initiatives promoting healthy lifestyles are of immense importance. The positive relationship between employment rates and waste generation underscores the necessity of waste reduction programs that generate employment opportunities. The positive correlation between fertility rates and waste generation emphasizes the need for the expansion of extended producer responsibility programs to include products and materials specifically associated with families and child-rearing. Education campaigns and governmental support for research and development (R&D) in waste reduction technologies are also integral components of an effective waste management strategy.

The short- and long-term adjustments of MWG reacts to shifts in macroeconomic variables from low aggregation (countries) to high aggregation (regions). Previous research has neglected the long-term information contained in variables by not incorporating the lagged error correction term (ETM). Neglecting this aspect could result in imprecise estimates of the elasticities.

This exploratory research examined how emphasizing a brand’s unethical behaviour through high moral intensity news framing influences consumer boycott intention.

The hypotheses were tested and validated using two experimental studies that expose customers of real retail and personal care product brands to news articles that have high and low moral intensity news frames.

The results showed high moral intensity news framing’s positive effect on consumer boycott intention. The frame’s influence is moderated by moral awareness and partially mediated by perceived moral intensity and moral judgement. The findings suggest that consumers’ perception of the frame and their attitude towards the brand will have a substantial role in boycott intention.

These research outcomes aid in the understanding of news framing effects on boycott intention, providing both insights for consumer activists and managerial implications for stewards of brands.

While previous research have examined the impact of news frames on the typical audience, there has been relatively little focus on news framing’s impact on consumers and their decision to boycott brands. This study addresses this gap by applying the work on emphasis framing to a consumer decision-making context. It also introduces moral intensity framing to the news frame classification. In addition, this study expands current conceptualizations of individual ethical decision-making to help explain consumer boycott intent.

The goal of this work is to evaluate how digital transformation disclosure in corporate news and press releases affects stock prices. We examine American and Chinese companies from the energy and industry sectors for two periods: pre-COVID-19 and during the COVID-19 pandemic.

To estimate the effects of disclosure of information related to digital transformation, we applied the bag-of-words (BOW) method. As the benchmark dictionary, we used Kindermann et al. (2021), with the addition of original dictionaries created via Latent Dirichlet allocation (LDA) analysis. We also employed panel regression analysis and random forest.

For USA energy sector, all aspects of digital transformation were insignificant in pre-COVID-19 period, while sustainability topics became significant during the pandemic. As for the Chinese energy sector, digital strategy implementation was significant in pre-pandemic period, while digital technologies adoption and business model innovation became relevant in COVID-19 period. The results show the greater significance of digital transformation aspects for industrials sectors compared to the energy sector. The result of random forest analysis proves the efficiency of the authors’ dictionary which could be applied in practice. The developed methodology can be considered relevant.

The research contributes to the existing literature in theoretical, empirical and methodological ways. It applies signaling and information asymmetry theories to the financial markets, digital transformation being used as an instrument. The methodological contribution of this article can be described in several ways. Firstly, our data collection process differs from that in previous papers, as the data are gathered “from investor’s point of view”, i.e. we use all public information published by the company. Secondly, in addition to the use of existing dictionaries based on Kindermann et al. (2021), with our own modifications, we apply the original methodology based on LDA analysis. The empirical contribution of this research is the following. Unlike past works, we do not focus on particular technologies (Hong et al., 2023) connected with digital transformation, but try to cover all multi-dimensional aspects of the transformational process and aim to discover the most significant one.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?