Assessing consumers’ propensity towards product-service systems in the fashion industry: a cross-national comparison between Russia and Italy

To assess the propensity of Russian and Italian fashion consumers to use Product-Service Systems (PSSs), the study identifies the drivers and barriers for their adoption to improve decision-making and targeting of consumers when launching PSSs in markets with different socio-cultural and economic characteristics. After providing a comprehensive list of the services that are currently implemented in the fashion industry, the study also compares the level of interest in PSSs for Russian and Italian consumers, as well as their willingness to recommend, actual usage of, and attitude towards PSSs. The methodology relies on an exploratory empirical study between Russian and Italian consumers, using a survey leading to a final sample of 328 participants. From the findings, it emerges that the national context plays a pivotal role in determining the propensity of fashion consumers to adopt PSSs. We find that Italian consumers are more inclined to adopt PSSs vis-a-vis their counterparts. Further, by identifying the key drivers and barriers, we also highlight potential opportunities and threats for the adoption of PSSs in the future. Given that the widespread adoption at an international level of PSSs is strictly related to their potential in terms of scalability, the findings have highly significant implications for both theory and practice.</p

Theoretical advancements and applications in singular spectrum analysis.

Singular Spectrum Analysis (SSA) is a nonparametric time series analysis and forecasting technique which has witnessed an augment in applications in the recent past. The increased application of SSA is closely associated with its superior filtering and signal extraction capabilities which also differentiates it from the classical time series methods. In brief, the SSA process consists of decomposing a time series for signal extraction and then reconstructing a less noisy series which is used for forecasting. The aim of this research is to develop theoretical advancements in SSA, supported by empirical evidence to further promote the value, effectiveness and applicability of the technique in the field of time series analysis and forecasting. To that end, this research has four main contributions. Initially, given the reliance of this research towards improving forecasting processes, it is mandatory to compare and distinguish between the predictive accuracy of forecasts for statistically significant differences. The first contribution of this research is the introduction of a complement statistical test for comparing between the predictive accuracy of two forecasts. The proposed test is based on the principles of cumulative distribution functions and stochastic dominance, and is evaluated via both a simulation study and empirical evidence. Governments, practitioners, researchers and private organizations publish a variety of forecasts each year. Such forecasts are generally computed using multivariate models and are widely used in decision making processes given the considerably high level of anticipated forecast accuracy. The classical multivariate methods consider modelling multiple information pertaining to the same time period or with a time lag into the past. Multivariate Singular Spectrum Analysis (MSSA) is a relatively new and alternative technique for generating forecasts from multiple time series. The second contribution of this research is the introduction of a novel theoretical development which seeks to exploit the information contained in published forecasts (which represent data with a time lag into the future) for generating a new and improved (comparatively more accurate) forecast by taking advantage of the MSSA technique’s capability at modelling time series with different series lengths. In brief, the proposed multivariate theoretical development seeks to exploit the forecastability of forecasts by considering not only official and professional forecasts, but also forecasts obtained via other time series models. The productive application of SSA and MSSA depends largely on the selection of SSA and MSSA parameters, i.e. the Window Length, L, and the number of eigenvalues r which are used for decomposition and reconstruction of time series. Over the years, a variety of mathematically complex, time consuming and labour intensive approaches which require detailed knowledge on the theory underlying SSA have been proposed and developed for the selection of SSA and MSSA parameters. However, the highly labour intensive and complex nature of such approaches have not only discouraged the application of this method by those not conversant with the underlying theory, but also limited SSA and MSSA to offline applications. The third and final contribution of this research proposes new, automated and optimized, SSA and MSSA algorithms for the selection of SSA parameters and thereby enables obtaining optimal SSA and MSSA forecasts (optimized by minimising a loss function). This development opens up the possibility of using SSA and MSSA for online forecasting in the future

Assessing consumers’ propensity towards products-service systems in the fashion industry – A cross-national comparison between Russia and Italy.

To assess the propensity of Russian and Italian fashion consumers to use Product-Service Systems (PSSs), the study identifies the drivers and barriers for their adoption to improve decision-making and targeting of consumers when launching PSSs in markets with different socio-cultural and economic characteristics. After providing a comprehensive list of the services that are currently implemented in the fashion industry, the study also compares the level of interest in PSSs for Russian and Italian consumers, as well as their willingness to recommend, actual usage of, and attitude towards PSSs. The methodology relies on a comparative quantitative study between Russian and Italian consumers, using a survey leading to a final sample of 328 participants. From the findings, it emerges that the national context plays a pivotal role in determining the propensity of fashion consumers to adopt PSSs. We find that Italian consumers are more inclined to adopt PSSs vis-a-vis their counterparts. Further, by identifying the key drivers and barriers, we also highlight potential opportunities and threats for the adoption of PSSs in the future. Given that the widespread adoption at an international level of PSSs is strictly related to their potential in terms of scalability, the findings have highly significant implications for both theory and practice

Artificial Intelligence (AI) or Intelligence Augmentation (IA): What is the future?

Artificial intelligence (AI) is a rapidly growing technological phenomenon that all industries wish to exploit to benefit from efficiency gains and cost reductions. At the macrolevel, AI appears to be capable of replacing humans by undertaking intelligent tasks that were once limited to the human mind. However, another school of thought suggests that instead of being a replacement for the human mind, AI can be used for intelligence augmentation (IA). Accordingly, our research seeks to address these different views, their implications, and potential risks in an age of increased artificial awareness. We show that the ultimate goal of humankind is to achieve IA through the exploitation of AI. Moreover, we articulate the urgent need for ethical frameworks that define how AI should be used to trigger the next level of IA

The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study zooms in on the early outbreak phase from when ChatGPT was launched in November 2022 to early June 2023. It aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By retrieving data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's early footprint in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations, and innovations to enhance ChatGPT's capabilities and impact across domains.</p

Forecasting the price of gold

This article seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate and statistically significant forecasts for gold price. We report the results from the nine most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the random walk (RW) as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the RW in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the RW at horizons of 1 and 9 steps ahead, and on average, the exponential smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24-month forecasting horizons. Moreover, we find that the univariate models used in this article are able to outperform the Bayesian autoregression and Bayesian vector autoregressive models, with exponential smoothing reporting statistically significant results in comparison with the former models, and classical autoregressive and the vector autoregressive models in most cases

Bicoid signal extraction with a selection of parametric and nonparametric signal processing techniques.

The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average

From Nature to Maths: Improving Forecasting Performance in Subspace–based methods using Genetics Colonial Theory

Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research

Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis

This study examines the potential advantages of using Singular Spectrum Analysis (SSA) for forecasting tourism demand. To do this it examines the performance of SSA forecasts using monthly data for tourist arrivals into the United States over the period 1996 to 2012. The SSA forecasts are compared to those from a range of other forecasting approaches previously used to forecast tourism demand. These include ARIMA, exponential smoothing and neural networks. The results presented show that the SSA approach produces forecasts which perform (statistically) significantly better than the alternative methods in forecasting total tourist arrivals into the U.S. Forecasts using the SSA approach are also shown to offer a significantly better forecasting performance for arrivals into the U.S. from individual source countries. Of the alternative forecasting approaches exponential smoothing and feed-forward neural networks in particular were found to perform poorly. The key conclusion is that Singular Spectrum Analysis (SSA) offers significant advantages in forecasting tourist arrivals into the US and is worthy of consideration for other forecasting studies of tourism demand

Forecasting Inflation Under Varying Frequencies

This paper seeks to determine the impact of monthly and annual data frequencies on the accuracy of inflation forecasts attainable via econometric and subspace-based methods. The application considers food inflation across short and long run horizons in Colombia, a country with an inflation targeting regime. The data includes all 54 components of the food consumer price index (CPI) in Colombia from Jan. 1999 – Oct. 2012, and the study forecasts the food CPI, and inflation using the parametric and nonparametric techniques of ARIMA, Exponential Smoothing (ETS), Holt-Winters (HW) and Singular Spectrum Analysis (SSA). We find that when forecasting the index, ARIMA forecasts are on average best, whilst for monthly inflation forecasting SSA is comparatively better and for annual, the results vary between SSA and ARIMA. These statistically significant findings give policy makers an option to select an apt forecasting model which suits their requirements