Academic literature on the topic 'Consumer behavior Forecasting'

Subject. The article addresses the issue of forecasting changes in consumer behavior patterns in the medium and long term. Objectives. The focus is on consumer behavior prediction to identify potential growth points for small and medium-sized businesses in the field of trade and services. Methods. The methodological basis of the study is behavioral economics. The information base draws on analytical reports of Cerulli Associates, Profi Online Research, PricewaterhouseCoopers, Euromonitor International, and Ipsos research company, which developed and uses in its analysis a calculation methodology for the Global Consumer Confidence Index. Results. We analyzed the main trends of consumer behavior in 2022 in Russia in the context of tougher sanctions, global supply disruptions, and other crisis phenomena, from the perspective of existing knowledge about sustainable forms of Russian consumers’ reaction to crisis events in the economy. The paper highlights a decrease in loyalty to brands, expansion of online commerce to people over the age of 60, reduced demand for eco-products, and enhanced financial literacy, which is accompanied by an increase in demands for a fair price-quality ratio, as the key changes in consumer behavior that determine opportunities for businesses. Conclusions. Crises seriously affect the time frames of consumer planning. During the crisis, the time frames are reduced and blurred, and now consumers proceed from even greater uncertainty.

Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.

A short-range residential consumer’s demand forecasting at the distinct and cumulative level, by an analysis of data using consumer based centric approach. Energy intake behavior might fluctuate among various seasonal factors; the consumed current will change from one season to other. So hereby we are building a model which helps to calculate future electricity consumption data from the obtain ability of past smart meter data. Currently utility companies accumulate the data, use it, share for further practice, and abandon usage data at their discretion, with no input from customers. In many cases, consumers do not even have entree to their own data. But in this project Consumer can have fast admittance and control over their individual data, and also helps to choose the familiar algorithms for the data analyze rather than including third party applications. By end of analyze technique, the analyzed output will be driven to some user interactive application by creating a Graphical User Interface.

The problem of controlling the herd behavior of consumers in the product sales markets has been identified. A general outline of the mechanism for reflective management of the decision-making process on the purchase of goods and the manifestation of herd behavior by consumers in the sales markets was presented. The stages of the technology for constructing a model of the mechanism of reflective control of herd behavior of consumers in the sales market have been described and formalized. The possibility of using the model as a tool for forecasting and increasing demand for a particular product or group of products has been determined. Promising areas of research have been identified. Keywords herd behavior, consumer, reflexive control, product sales market, decision making.

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.

The rapid spread of the COVID-19 pandemic has severely impacted many sectors including the electricity sector. The restrictions such as lockdowns, remote-working, and -schooling significantly altered the consumers' behaviors and demand structure especially due to a large number of people working at home. Accurate demand forecasts and detailed production plans are crucial for cost-efficient generation and transmission of electricity. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the impact of the restrictions on total demand using a multiple linear regression model. In addition, the model is utilized to forecast the electricity demand in pandemic conditions and to analyze how different types of restrictions impact the total electricity demand. It is found that among three levels of COVID-19 restrictions, age-specific restrictions and the complete lockdown have different effects on the electricity demand on weekends and weekdays. In general, new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches as COVID-19 significantly changed the consumer behavior, which appears as altered daily and weekly load profiles of the country. Long-term policy implications for the energy transition and lessons learned from the COVID-19 experience are also discussed.

В маркетинговом исследовании, представленном в данной статье, выявлены основные типы потребителя на рынке продуктов питания для последующего анализа и прогноза их поведения. В зависимости от доминирующих мотивов и возможностей потребления выделены пять основных типов потребителей продуктов питания: «Сдержанный», «Безразличный», «Органический», «Социальный или VIP» и «Активный». We distinguish the main types of consumers in the food market for further analysis and forecasting of their behavior. Depending on the dominant motives and consumption potential five main types of food consumers are studied, namely, “Discreet”, “Indifferent”, “Organic”, “Social or VIP” and “Active”.

Empowered by information technology, modern consumers increasingly rely upon online word-of-mouth (WOM--e.g., product reviews) to guide their purchase decisions. This dissertation investigates how WOM information is processed by consumers and its downstream consequences. First, the value of specific types of word-of-mouth information (e.g., numeric ratings, text commentary, or both) was explored for making forecast. After proposing an anchoring-and-adjustment framework for the utilization of WOM to inform consumer forecasts, I support this framework with a series of experiments. Results demonstrate that the relative forecasting advantage of different information types is a function of the extent to which consumer and reviewer have similar product-level preferences ('source-receiver similarity'). Second, I investigate the process by which dispersion--the degree to which opinions are divided for a product or service--in WOM is interpreted. Using an attribution-based approach, I argue that the effect of WOM dispersion is dependent on the perceived cause of that dispersion, which is systematically related to perceptions of preference heterogeneity in a product category. For products for which preferences are expected to vary, dispersion is likely to be attributed to the reviewers rather than the product itself, and therefore tolerated. I provide evidence for my hypotheses in a series of experiments where WOM dispersion is manipulated and respondents make choices and indicate purchase intentions.

Using data from the Michigan Consumer Survey, I explore alternatives for constructing the Index of Consumer Sentiment (ICS) to improve its forecasting power regarding consumption and its components. Questions which seemed to matter in the past are no longer good predictors. For more recent sample periods, expectations of automobile purchases, unemployment, and current economic situations are more important than categories selected previously. An alternative index is constructed accordingly. Applying different techniques suggested in the literature, the new index significantly outperforms the ICS in both in-sample and out-of-sample tests. Furthermore, the new index also produces more accurate results when forecasting recessions.

This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method.

1. Contextual outline of the PhD Research

Climate change is today often seen as one of the most challenging issue that our civilisation will have to face during the 21st century. This is especially so now that the most recent scientific data have led to the conclusion that the globally averaged net effect of human activities since 1750 has been one of warming (IPCC 2007, p. 5) and that continued greenhouse gas emissions at or above current rates would cause further warming (IPCC, 2007 p. 13). This unequivocal link between climate change and anthropogenic activities requires an urgent, world-wide shift towards a low carbon economy (STERN 2006 p. iv) and coordinated policies and measures to manage this transition.

The climate issue is undoubtedly a typical policy question and as such, is considered amenable to economic scrutiny. Indeed, in today’s world economics is inevitable when it comes to arbitrages in the field of policy making. From the very beginning of international talks on climate change, up until the most recent discussions on a post-Kyoto international framework, economic arguments have turned out to be crucial elements of the analysis that shapes policy responses to the climate threat. This can be illustrated by the prominent role that economics has played in the different analyses produced by the Intergovernmental Panel on Climate Change (IPCC) to assess the impact of climate change on society.

The starting point and the core idea of this PhD research is the long-held observation that the threat of climate change calls for a change of climate in economics. Borrowing from the jargon used in climate policy, adaptation measures could also usefully target the academic discipline of economics. Given that inherent characteristics of the climate problem (e.g. complexity, irreversibility, deep uncertainty, etc.) challenge core economic assumptions, mainstream economic theory does not appear as appropriately equipped to deal with this crucial issue. This makes that new assumptions and analyses are needed in economics in order to comprehend and respond to the problem of climate change.

In parallel (and without environmental considerations being specifically the driving force to it), the mainstream model in economics has also long been (and still is) strongly criticised and disputed by numerous scholars - both from within and outside the field of economics. For the sake of functionality, these criticisms - whether they relate to theoretical inconsistencies or are empirically-based - can be subsumed as all challenging part of the Cartesian/Newtonian legacy of economics. This legacy can be shown to have led to a model imprinted with what could be called “mechanistic reductionism”. The mechanistic side refers to the Homo oeconomicus construct while reductionism refers to the quest for micro-foundations materialised with the representative agent hypothesis. These two hypotheses constitute, together with the conjecture of perfect markets, the building blocks of the framework of general equilibrium economics.

Even though it is functional for the purpose of this work to present them separately, the flaws of economics in dealing with the specificities of the climate issue are not considered independent from the fundamental objections made to the theoretical framework of mainstream economics. The former only make the latter seem more pregnant while the current failure of traditional climate policies informed by mainstream economics render the need for complementary approaches more urgent.

2. Overview of the approach and its main insights for climate policy

Starting from this observation, the main objective of this PhD is thus to assess the implications for climate policy that arise from adopting an alternative analytical economic framework. The stance is that the coupling of insights from the framework of evolutionary economics with the perspective of ecological economics provides a promising way forward both theoretically as well as on a more applied basis with respect to a better comprehension of the socioeconomic aspects related to the climate problem. As claimed in van den Bergh (2007, p. 521), ecological economics and evolutionary economics “share many characteristics and can be combined in a fruitful way" - which renders the coupling approach both legitimate and promising.

The choice of an evolutionary line of thought initially stems from its core characteristic: given its focus on innovation and system change it provides a useful approach to start with for assessing and managing the needed transition towards a low carbon economy. Besides, its shift of focus towards a better understanding of economic dynamics together with its departure from the perfect rationality hypothesis renders evolutionary economics a suitable theoretical complement for designing environmental policies.

The notions of path-dependence and lock-in can be seen as the core elements from this PhD research. They arise from adopting a framework which is founded on a different view of individual rationality and that allows for richer and more complex causalities to be accounted for. In a quest for surmounting the above-mentioned problem of reductionism, our framework builds on the idea of ‘multi-level selection’. This means that our analytical framework should be able to accommodate not only for upward but also for downward causation, without giving analytical priority to any level over the other. One crucial implication of such a framework is that the notion of circularity becomes the core dynamic, highlighting the importance of historicity, feedbacks and emergent properties.

More precisely, the added value of the perspective adopted in this PhD research is that it highlights the role played by inertia and path-dependence. Obviously, it is essential to have a good understanding of the underlying causes of that inertia prior to devising on how to enforce a change. Providing a clear picture of the socio-economic processes at play in shaping socio-technical systems is thus a necessary first step in order to usefully complement policy-making in the field of energy and climate change. In providing an analytical basis for this important diagnosis to be performed, the use of the evolutionary framework sheds a new light on the transition towards low-carbon socio-technical systems. The objective is to suggest strategies that could prove efficient in triggering the needed transition such as it has been the case in past “lock-in” stories.

Most notably, the evolutionary framework allows us to depict the presence of two sources of inertia (i.e at the levels of individuals through “habits” and at the level of socio-technical systems) that mutually reinforce each other in a path-dependent manner. Within the broad perspective on path dependence and lock-in, this PhD research has first sketched the implications for climate policy of applying the concept of ‘technological lock-in’ in a systemic perspective. We then investigated in more details the notion of habits. This is important as the ‘behavioural’ part of the lock-in process, although explicitly acknowledged in the pioneer work of Paul David (David, 1985, p. 336), has been neglected in most of subsequent analyses. Throughout this study, the notion of habits has been studied at both the theoretical and applied level of analysis as well as from an empirical perspective.

As shown in the first chapters of the PhD, the advantage of our approach is that it can incorporate theories that so far have been presented opposite, partial and incomplete perspectives. For instance, it is shown that our evolutionary approach not only is able to provide explanation to some of the puzzling questions in economics (e.g. the problem of strong reciprocity displayed by individual in anonymous one-shot situations) but also is very helpful in bringing a complementary explanation with respect to the famous debate on the ‘no-regret’ emission reduction potential which agitates the experts of climate policy.

An emission reduction potential is said to be "no regret" when the costs of implementing a measure are more than offset by the benefits it generates such as, for instance, reduced energy bills. In explaining why individuals do not spontaneously implement those highly profitable energy-efficient investments ,it appears that most prior analyses have neglected the importance of non-economic obstacle. They are often referred to as “barriers” and partly relate to the ‘bounded rationality’ of economic agent. As developed in the different chapters of this PhD research, the framework of evolutionary economics is very useful in that it is able to provide a two-fold account (i.e. relying on both individual and socio-technical sources of inertia) of this limited rationality that prevent individuals to act as purely optimising agents.

Bearing this context in mind, the concept of habits, as defined and developed in this study, is essential in analysing the determinants of energy consumption. Indeed, this concept sheds an insightful light on the puzzling question of why energy consumption keeps rising even though there is an evident increase of awareness and concern about energy-related environmental issues such as climate change. Indeed, if we subscribe to the idea that energy-consuming behaviours are often guided by habits and that deeply ingrained habits can become “counter-intentional”, it then follows that people may often display “locked-in” practices in their daily energy consumption behaviour. This hypothesis has been assessed in our empirical analysis whose results show how the presence of strong energy-consuming habitual practices can reduce the effectiveness of economic incentives such as energy subsidies. One additional delicate factor that appears crucial for our purpose is that habits are not fully conscious forms of behaviours. This makes that individuals do not really see habits as a problem given that it is viewed as easily changed.

In sum, based on our evolutionary account of the situation, it follows that, to be more efficient, climate policies would have to both shift the incumbent carbon-based socio-technical systems (for it to shape decisions towards a reduction of greenhouse gas emissions) and also deconstruct habits that this same socio-technical has forged with time (as increased environmental awareness and intentions formulated accordingly are not sufficient in the presence of strong habits).

Accordingly, decision-makers should design measures (e.g. commitment strategies, niche management, etc.) that, as explained in this research, specifically target those change-resisting factors and their key features. This is essential as these factors tend to reduce the efficiency of traditional instruments. Micro-level interventions are thus needed as much as macro-level ones. For instance, it is often the case that external improvements of energy efficiency do not lead to lower energy consumption due to the rebound effect arising from unchanged energy-consuming habits. Bearing this in mind and building on the insights from the evolutionary approach, policy-makers should go beyond the mere subsidisation of technologies. They should instead create conditions enabling the use of the multi-layered, cumulative and self-reinforcing character of economic change highlighted by evolutionary analyses. This means supporting both social and physical technologies with the aim of influencing the selection environment so that only the low-carbon technologies and practices will survive.

Mentioned references:

David, P. A. (1985), Clio and the economics of QWERTY, American Economic Review 75/2: 332–337.

IPCC, 2007, ‘Climate Change 2007: The Physical Science Basis’, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S. D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.

Stern, N. 2006, ‘Stern Review: The economics of Climate Change’, Report to the UK Prime Minister and Chancellor, London, 575 p. (www.sternreview.org.uk)

van den Bergh, J.C.J.M. 2007, ‘Evolutionary thinking in environmental economics’, Journal of Evolutionary Economics 17(5): 521-549.

Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

Previous theories have suggested that consumers will be happier if they spend their money on experiences such as travel as opposed to material possessions such as automobiles. I test this experience recommendation and show that it may be misleading in its general form. Valence of the outcome significantly moderates differences in respondents' reported retrospective happiness with material versus experiential purchases. For purchases that turned out positively, experiential purchases lead to more happiness than do material purchases, as the experience recommendation suggests. However, for purchases that turned out negatively, experiences have no benefit over (and, for some types of consumers, induce significantly less happiness than) material possessions. I provide evidence that this purchase type by valence interaction is driven by the fact that consumers adapt more slowly to experiential purchases than to material purchases, leading to both greater happiness and greater unhappiness for experiential purchases. Moreover, I show that this difference in hedonic adaptation rates for material and experiential purchases is being, at least partially, driven by a difference in memory for those types of purchases. I also show that individuals mispredict hedonic adaptation rates for material and experiential purchases. Finally, I discuss implications for consumer choice.
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The fashion apparel industry is too diverse, volatile and uncertain due to the fast changing market scenario. Forecasting demands of consumers has become survival necessity for organizations dealing with this field. Many traditional approaches have been proposed for improving the computational time and accuracy of the forecasting system. However, most of the approaches have over-looked the uncertainty existing in the fashion apparel market due to certain unpredictable events such as new trends, new promotions and advertisements, sudden rise and fall in economic conditions and so on. In this chapter, an intelligent multi-agent based demand forecasting and replenishment system has been proposed that adopts features from nature-inspired computing for handling uncertainty of the fashion apparel industry. The proposed system is inspired from the group hunting behaviour of crocodiles such as they form temporary alliances with other crocodiles for their own benefit even after being territorial creatures.

Historically, restaurant managers used either historical data or simple logical methods to estimate customer numbers or sales volume. These techniques usually consist of an intuitive prediction based on the experience of the manager. However, restaurant sales forecasts are a complex task because they are influenced by numerous factors that can be classified as time, weather conditions, economic factors, and random events. In this case, old techniques may give insufficient results. It is aimed to compare the estimation Simit which is one of the most consumed daily snacks in Turkey sales accuracy of the learning methods and determine the model that provides the highest accuracy and determine the factors affecting the buying behavior of one of the leading Simit chain stores in Turkey in the food sector by using popular machine learning algorithms.

With Internet invading geographic boundaries and diverse demographic strata, online shopping is growing at exponential rate. Expected to grow by 45 per cent to $7.69 billion by the end of 2015, India's ecommerce market has emerged as one of the most anticipated destinations for both multinational and domestic retailers. Since their success will depend on their ability to attract shoppers to buy online, it becomes relevant for them to decipher Indian consumers' attitude and behaviour towards online shopping and to predict online buying potential in India. The effectiveness of marketing and promotional strategies and action plans also will have to be pivoted around the potential available in the market. This empirical study explores the accuracy, precision and recall of four different classifying techniques used in predicting online buying. The forecasting ability of logistic regression (LR), artificial neural network (ANN), support vector machines (SVM) and random forest (RF) in the context of willingness of shoppers' to buy online has been compared. Analysis of the data supported most of the predictions albeit with varying level of accuracy. The outcome of the study reflects the superiority of artificial neural network over the other three models in terms of the predicting power. This paper adds to the knowledge body for online retailers in reducing their vulnerability with respect to market demand and improves their preparedness to handle the market response. Managerial implications of the findings and scope for future research have been deliberated.

Along with a growing interest in tourism research is the effort to establish innovative methodologies that are useful to guide the tourist operators and the policy makers in selecting forecasting techniques. Nevertheless, predicting tourist demand is still lacking at a microeconomic level, while it has become a flourishing theme of research uniquely at a macroeconomic level. The main goal is to analyze Italian tourists' behaviours on the basis of statistical surveys on households, life conditions, incomes, consumptions, travels, and vacation. This research is set in the framework of Rough Sets Theory, a Data Mining technique that can easily manage categorical variables. Hence, it is suitable for the exploitation of databases collecting sample surveys data. A large selection of variables from database Sinottica, containing information on social, cultural, and behavioural trends in Italy collected by means of a psychographic survey is provided by a leading market research organization, GfK Eurisko. By defining some decision rules, some interesting relations between consumer behaviours and their corresponding tourism choices are obtained.

The rapid penetration of smartphones and consumers' increased usage/dependence on mobile applications (apps) has ushered favorable opportunities for retailers as well as shoppers. The traditional brick-and-mortar as well as online retailers must attract shoppers to use mobile shopping apps. For this, it is pertinent for retailers to predict users' continuous intention to buy through apps. To address this question, the present study has applied four prominent binary classifiers - logit regression, linear discriminant analysis, artificial neutral network and decision tree analysis to develop predictive models. Findings of the study shall help the marketers in accurately forecasting shoppers' buying behaviour. Various indices have been used to check the predictive accuracy of four techniques. The outcome of the study shows that the models developed using decision tree analysis and artificial neutral network provide better results in predicting consumers' continuous intention to buy through app. Based on the findings, the paper has also provided implications for the retailers.

While discrete choice analysis is prevalent in capturing consumers’ preferences and describing their choice behaviors in product design, the traditional choice modeling approach assumes that each individual makes independent decisions, without considering the social impact. However, empirical studies show that choice is social — influenced by many factors beyond engineering performance of a product and consumer attributes. To alleviate this limitation, we propose a new choice modeling framework to capture the dynamic influence from social network on consumer adoption of new products. By introducing the social influence attributes into the choice utility function, the social network simulation is integrated with the traditional discrete choice analysis in a three-stage process. Our study shows the need for considering social impact in forecasting new product adoption. Using hybrid electric vehicle as an example, our work illustrates the procedure of social network construction, social influence evaluation, and choice model estimation based on data from National Household Travel Survey. Our study also demonstrates several interesting findings on the dynamic nature of new technology adoption and how social network may influence consumers’ “green attitude” in hybrid electric vehicle adoption.

The current digital opportunities that have spawned the upgrade of the business versions of tourism and hospitality have been actualised due to the hard-to-predict pandemic nature of the COVID-19 coronavirus threat and travel bans. In Business Model 4.0. the usual forms of the relationship between the public (institutional) and the private, individual in tourism and hospitality are transformed. Research objective: to characterise the integration capabilities of automated (AI-based) travel industry business processes that personalise the tourism offers to the needs and preferences of travel lers and guests. The value-based marketing 4.0 approach, supplemented by structural, network and functional approaches to the analysis of the structure, multi-level, dynamics of commercial opportunities, consumer value of business models of organisation and the implementation of tourism products, allowed the integration possibilities of Internet services in satisfying individualised consumer demands to be satisfied. Soft culture blurs the boundaries between the public and the personal, making actors’ informational behaviour transparent, transforming existing business strategies, and giving rise to ‘mass individuality’ in tourism and hospitality. The forms of correlation between the public (group, communal) and the private, individual in the practice of tourist services at all stages of a tourist trip or guest visits to HoReCa enterprises change under the influence of BigData technologies regarding operational processes; modelling and forecasting strategies; horizontal and vertical integration. The marketplace is won by those who practise personalisation, customisation and marketing authenticity of the market offer distributed on the P2P network. The basis of a stable competitive advantage of a company able to create a unique customer value in the tourism and hospitality market is represented by predictive or prognostic analytics of big data and smart technologies.