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Feature fatigue (FF) will lead to negative Word-Of-Mouth (WOM), which damages the brand’s long-term profit and ultimately decreases the manufacturer’s customer equity (CE). It becomes severer in multi-generation products because of the significant impacts of earlier generation products on the CE of later ones. The purpose of this paper is to alleviate FF, it is imperative for designers to decide what features should be integrated to balance initial revenue and long-term profit so as to maximize CE.

In this paper, a novel method based on the Norton-Bass model is proposed to alleviate FF of multi-generation products to help designers find optimal feature combination that maximizes CE. The authors take the effects of adding features on product capability and usability into account, and integrate product capability, usability, WOM and earlier-generation product’s effects into the Norton-Bass model to predict the impacts of FF on CE in current product development. A case study of a virtual product is presented to illustrate and validate the proposed method.

The advantage of the proposed method is highlighted in the cases of large feature number, high-product complexity (low-product usability) and multi-generation products. The experiments show that the earlier generations do affect the later ones from the perspective of maximizing CE. The superiority of the proposed method compared with the traditional way to put all potential features into a product during the product development is demonstrated. And the more features, the larger CE obtained using the proposed model than the one obtained by traditional way.

Although, there are reports attempting to analyze and alleviate FF, most of these studies still suffer the limitations that cannot point out what features should be added to the product with the objective of maximizing CE. In addition, few studies have been carried out to alleviate FF of multi-generation products. A novel method based on the Norton-Bass model and a genetic algorithm is proposed to alleviate FF of multi-generation products to help designers find optimal feature combination that maximizes CE.

The purpose of this paper is to solve the problem of low accuracy in new product demand forecasting caused by the absence of historical data and inadequate consideration of influencing factors.

A hybrid new product demand forecasting model combining clustering analysis and deep learning is proposed. Based on the product similarity measurement, the weight of product similarity attributes is realized by using the method of fuzzy clustering-rough set, which provides a basis for the acquisition and collation of historical sales data of similar products and the determination of product similarity. Then the prediction error of Bass model is adjusted based on similarity through a long short-term memory neural network model, where the influencing factors such as product differentiation, seasonality and sales time on demand forecasting are embedded. An empirical example is given to verify the validity and feasibility of the model.

The results emphasize the importance of considering short-term impacts when forecasting new product demand. The authors show that useful information can be mined from similar products in demand forecasting, where the seasonality, product selling cycles and sales dependencies have significant impacts on the new product demand. In addition, they find that even in the peak season of demand, if the selling period has nearly passed the growth cycle, the Bass model may overestimate the product demand, which may mislead the operational decisions if it is ignored.

This study is valuable for showing that with the incorporation of the evaluation method on product similarity, the forecasting model proposed in this paper achieves a higher accuracy in forecasting new product sales.

The purpose of the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).

The study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.

While higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.

The study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

The effect of customers’ forward-looking behavior on firms’ profit has been highlighted by many researchers and practitioners. This study aims to develop a mathematical model for new generation products to analyze the optimal pricing and advertising policies in the presence of homogeneous forward-looking customers. A firm that produces and sells a new generation product was considered. This firm aimed to determine the optimal pricing and advertising expenditure by maximizing the total profit.

The demand was presented as a diffusion model inspired by the Bass diffusion model. This paper used Pontryagin’s maximum principle to analyze the proposed model. The presented model was implemented in some numerical examples by proposing a heuristic solution method. Numerical examples confirmed the theoretical results.

This paper found a threshold on the optimal advertising policy depends on customers’ forward-looking behavior, advertising coefficient (both direct and word-of-mouth advertising) and discount rate. The funding showed that the optimal pricing path of the first generation was monotonically decreasing or increasing and, then, decreasing. Results revealed that, by increasing the customers’ forward-looking behavior, the firm should reduce the price and advertising expenditure. Also, the price was shown to be negatively affected by the discount rate and word-of-mouth advertising. The profitability will improve if the firm spends more budget on advertising by increasing the discount rate and advertising effectiveness. Further, when the word-of-mouth advertising effect is high, the firm should increase the advertising expenditure first and, then, decrease it.

Nowadays, forward-looking customers’ anticipation for releasing a new generation can harm the firms’ profit. In this regard, this research analyzed optimal pricing and advertising policies for a new generation product in a market populated by homogeneous forward-looking customers. To the best of the knowledge, this is the first study that investigated these two marketing policies jointly in the presence of forward-looking customers.

In view of the particularity of innovative product diffusion under the background of market competition, this paper firstly uses consumer behavior theory to logically deduce the dynamic process of consumer behavior from the perspective of experience theory.

Bass and Lotka-Volterra model are used to describe and model the consumers' perceptual behavior in competitive environment. On this basis, interactive modeling technology is used to model and simulate the diffusion process of innovative products. Finally, the validity of the model is verified by comparing two scenarios with an example.

The research shows that the stronger the enterprise's competitiveness, the higher the market share of innovative products, and the positive impact on consumer perceived value, consumer perceived value can promote consumers' second purchase behavior. Positive word-of-mouth and advertising positively affect consumers' purchasing decisions; negative word-of-mouth negatively affects consumers' purchasing decisions.

The interaction modeling technology and AnyLogic software platform are used to simulate the complexity of consumers' experiential perception so as to build the interaction and competition mechanism among different Agent, which realizes the dynamic simulation of the diffusion process of innovative products. This study provides guidance for enterprises to formulate effective marketing strategies.

The purpose of this study is to explore market entry strategies in a high-tech successive generations (HTSGs) market, by investigating entry mode via entry timing and path differentiation and the performance outcomes of entry mode.

The methodology of building a theory from a longitudinal case study is adopted by using useful cases in a HTSGs market after constructing an integrated research framework to explore market entry mode. Different entry modes were investigated by studying entry timing and migration path of three firms’ case in logic semiconductor market. In addition, performance outcomes of different entry modes were measured and correlated with each other.

The results identified three major entry modes suitable for a HTSGs market. The three firms differentiated their entry modes by exploiting different entry timings from the earliest to the last and different migration paths including switching, leapfrogging and new entrance path to enter a market. First mover advantage also exists in a HTSGs market, and it was found uniquely that the financial performance denoted by entry mode outcomes was correlated with technological knowledge.

This study extends the theory of extant entry strategy from general consumer or industrial market to HTSGs market, in which intense competition exits and technological innovation is important. Moreover, this study verified that the causality between early entry and positive performance was also effective in HTSGs market with a shorter duration of early entry advantage.

This study has managerial implications for firms to establish market entry strategy in HTSGs market and other markets. To become a product leader, a fast follower or a late follower, firms can differentiate their entry mode by adjusting the entry timing and migration path in the context of market and technology.

This study examined market entry strategies suitable for HTSGs market based on its unique characteristics and extended relevant theory into HTSGs market. Further, an integrated research framework, which explores the market entry mode, was constructed to facilitate further exploration of entry mode into other markets.

The aim of this study is to examine the trend over time of the demand for .it domain names.This study first assesses whether there is a phase of growth and expansion or at a point of saturation. Second, this research can be useful also to compare researches that have considered other internet metrics and other models.

This paper describes the forecasting methods used to analyze the internet diffusion in Italy. The domain names under the country code top-level domain “.it” have used as metrics. To predict domain names .it the seasonal auto regressive integrated moving average (SARIMA) model and the Holt-Winters (H-W) methods have been used.

The results show that, to predict domain names .it the SARIMA model is better than the H-W methods. According to the findings, notwithstanding the forecast of a growth in domain names, the increase is however limited (about 3%), tending to reach a phase of saturation of the market of domain names .it.

In general many authors have studied internet diffusion applying statistical models that follow an S-shaped behavior. On the other hand, the more used diffusion models that follow an S-shape not always provide an adequate description of the Internet growth pattern. To achieve this goal, this paper demonstrates how the time series models, in particular SARIMA model and H-W models, fit well in explaining the spread of the internet.

Production operations managers have long been concerned about new product development and the life cycle of these products. Because many products do not sell at constant levels throughout their lives, product life cycles (PLCs) must be considered when developing sales forecasts. Innovation diffusion models have successfully been employed to investigate the rate at which goods and/or services pass through the PLC. This research investigates innovation diffusion models and their relation to the PLC. The model is developed and then tested using modem sales from June 1994 to May 2006.

The advent of the 3G world of mobile commerce has suffered from the wait‐and‐see mode over the last years. Existing barriers pose a challenge to all actors in the mobile commerce industry (MCI). Learning from the future and discovering a route to a desired future are keys to successful mobile commerce business. In this paper we argue that it is very important that all actors in the MCI use an industry foresight approach in order to discover a successful route to future markets. We present a framework for creating industry foresights and for understanding the future of mobile commerce. We focus on the mobile commerce industry as a whole and introduce two broad variables; (1) adoption and diffusion of mobile commerce products and services; and (2) the macro‐economic development trend. Based on these variables we build four foresight scenarios: Rapid‐Up, Rapid‐Down, Slow‐Down and Slow‐Up. On the basis of these four different scenarios we suggest some features of promising mobile commerce products and services. We are using information‐gathering agents in order to collect information for our analysis. The foresight framework will help all actors understand the future for m‐commerce.

In a world characterised by increasing environmental and social awareness, the number of corporate social responsibility and sustainability initiatives has significantly grown. Among these, the United Nations Global Compact (UNGC) is one of the most important, involving more than 12,000 companies. The purpose of this study is to investigate the UNGC’s worldwide diffusion, both at country and industry level, to understand the reasons leading to the highlighted dissemination patterns, and to propose various future projections.

The study pursues its objectives by applying the logistic curve model to data provided by the United Nations. The analysis is complemented by adopting instability and concentration indexes.

Results suggest that, while human rights and environmental safeguard in some areas and industries will remain a controversial issue, UNGC adoption will continue growing and giving the participants the required legitimacy to compete in worldwide markets.

To the best of the authors’ knowledge, this is the first paper that analyses the UNGC’s worldwide diffusion and proposes a prediction model for its future dissemination. The findings are of considerable importance in extending the knowledge of the initiative and in understanding the potential values of its adoption.