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Crete is the largest island of Greece and fifth of the Mediterranean basin which produces traditional and high‐quality cheeses from sheep′s and goat′s milk. Reports a qualitative and quantitative market survey to determine which factors affect purchasing behaviour with respect to cheese. Results showed that the Cretan cheeses are much preferred by consumers. Price, convenience (packaged), hygiene and dietary value (low fat) of the cheese are the main determinants of their choice. However these depend on the age, education and economic status of respondents. There was a high degree of desire for low fat and packaged cheese.

Food purchasing behaviour is influenced by economic factors such as price and income, as well as by non‐economic factors such as concern about diet and health, growth of vegetarianism, convenience, household′s life cycle and advertising. Reports on a survey in Chania, the second largest city of Crete, to determine the factors that affect the purchasing behaviour of fresh pasteurized milk and to identify the attitudes and perceptions of consumers towards it. Reveals that the concern about health of consumers, strong advertising campaigns, as well as the age and economic status of respondents, influence consumer choice towards fresh pasteurized milk. Finally, scrutinizes the attitude of the majority of consumers towards fresh pasteurized milk although concentrated milk (the basic competitor) still has the biggest market share in Chania.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

Longevity risk, that is, the uncertainty of the demographic survival rate, is an important risk for insurance companies and pension funds, which have large, and long‐term, exposures to survivorship. The purpose of this paper is to propose a new model to describe this demographic survival risk.

The model proposed in this paper satisfies all the desired properties of a survival rate and has an explicit distribution for both single years and accumulative years.

The results show that it is important to consider the expected shift and risk premium of life table uncertainty and the stochastic behaviour of survival rates when pricing the survivor derivatives.

This model can be applied to the rapidly growing market for survivor derivatives.

A relatively simple function derived from Benouilli’s differential equation and given the class name “metalogistic” is shown to be an efficient descriptor of the dynamics of a number of important American aggregates. These include five demographic, i.e. population, urbanization, immigration, birth‐rate, and death‐rate over the 1820‐1992 time period; and six socio‐economic, that is gross national product, GNP deflator, civilian labour force, unemployment, total energy in BTU, and a surrogate of higher knowledge, these over the 1880‐1992 time period. The descriptor in each case is defined by parameters derived from the data through regression, with model efficacy measured by an R² > 0.90 in almost all cases.

This paper aims to develop a parsimonious and innovative model that captures the dynamics of new product diffusion in the recent high-technology markets and thus assist both academicians and practitioners who are eager to understand the diffusion phenomena. Accordingly, this study develops a novel diffusion model to forecast the demand by centering on the dynamic state of the product’s adoption rate. The proposed study also integrates the consumer’s psychological point of view on price change and goodwill of the innovation in the diffusion process.

In this study, a two-dimensional distribution function has been derived using Cobb–Douglas’s production function to combine the effect of price change and continuation time (goodwill) of the technology in the market. Focused on the realistic scenario of sales growth, the model also assimilates the time-to-time variation in the adoption rate (hazard rate) of the innovation owing to companies changing marketing and pricing strategies. The time-instance upon which the adoption rate alters is termed as change-point.

For validation purpose, the developed model is fitted on the actual sales and price data set of dynamic random access memory (DRAM) semiconductors, liquid crystal display (LCD) monitors and room air-conditioners using non-linear least squares estimation procedure. The results indicate that the proposed model has better forecasting efficiency than the conventional diffusion models.

The developed model is intrinsically restricted to a single generation diffusion process. However, technological innovations appear in generations. Therefore, this study also yields additional plausible directions for future analysis by extending the diffusion process in a multi-generational environment.

This study aims to assist marketing managers in determining the long-term performance of the technology innovation and examine the influence of fluctuating price on product demand. Besides, it also incorporates the dynamic tendency of adoption rate in modeling the diffusion process of technological innovations. This will support the managers in understanding the practical implications of different marketing and promotional strategies on the adoption rate.

This is the first attempt to study the value-based diffusion model that includes key interactions between goodwill of the innovation, price dynamics and change-point for anticipating the sales behavior of technological products.

This study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19; examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment; theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold; and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events.

This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach.

As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges.

Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings.

Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data.

The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges.

The authors have contributed to the chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos.

The purpose of this paper is to highlights the complex issue of developing management processes for learner centered library media production service. The work provides a practical and theoretical perspective on management tools and systems for this emerging library service.

The research was drawn from a body of peer reviewed literature covering theoretical and practical library and business management practices. The author tempered this knowledge with first‐hand development and implementation experience of formal process management tools for a student‐centered library multimedia production center.

The research offers a new theoretical perspective for viewing the emerging media rich, collaborative nature of library work. In addition, a diagramming strategy based on Human Interaction Theory is described. The tool can be used to foster the development of management processes in multimedia development centers and other library service units.

This paper discusses the multimedia production service, an emerging user service stemming from the idea of the library as collaborative workspace. Library information commons models have often combined technology and space.

The purpose of this paper is to examine a collaborative non‐profit network which is undergoing organizational change.

The authors present a case study of an employment‐services network in its first year of change, as the network implemented various activities to enhance its performance. A grounded‐theory approach was adopted to study the organizational and collaborative processes within the member‐site and Head‐Office levels.

It was found that member‐site leadership was the critical factor influencing site culture and site performance, and that high‐performing sites were initiating collaborative activities with other sites. Head‐Office leadership also influenced site performance and collaboration, but its initiatives were only moderately successful. The findings also indicate that change efforts should focus on leadership at both the site and network levels, and may need to begin with low‐performing sites.

The paper discusses the implications of leadership on the implementation of collaborative networks in the employment services sector.

The qualitative findings of the study add to, and help to explain, earlier research findings on the questions of how public sector organizations utilize various activities to implement collaborative networks and their impact on managerial practice.

This paper aims to present a forecast for renewable energy production in the USA. Growth curves are used to conduct the forecasts.

The analysis is based upon a literature review, supplemented by collection of secondary data. The study then focuses on applying the Pearl growth curve.

The authors' results show that biomass energy production is growing the fastest followed by geothermal and wind. Additionally, the forecast for solar energy production shows little to no growth over the next two decades.

If the US government hopes to achieve its goals in renewable energy, considerable funding and incentives will have to be put forth to accelerate the growth of renewable energy. Since the biomass technology is already growing nicely it makes sense to put the additional resources behind the other three technologies to close the 10.3 percent gap being forecasted. The government also needs to put more funding into dual renewable plants such as wind or solar combines with pumped hydro, this will ensure environmental and reliability are both maintained. Finally, for renewable energies to be competitive in the long term, considerable research needs to go into driving down the cost so there is not a need for subsidies.

This study provides value in providing a forecast for expected future growth for renewable energy sources.