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The purpose of this study is to test the impact of time and price sensitivity on consumer satisfaction and purchase intention on online-to-offline (O2O) takeout platforms and explore the moderating effect of purchase preference on time sensitivity and satisfaction, as well as price sensitivity and satisfaction, in order to guide market pricing.

A structural equation model (SEM) of customer purchase intention was constructed, and the relationships between the variables (time sensitivity, price sensitivity, satisfaction and purchase intention) were examined. The completed questionnaires of 349 respondents were collected from the Questionnaire Star platform in China. The research model and hypotheses were then tested. Analytic hierarchy procedure was used to determine the moderating effect of purchase preference. Finally, the study proposes a pricing strategy for customer-active selective services.

Satisfaction positively influences purchase intention, and price sensitivity significantly increases satisfaction and further increases purchase intention; however, time sensitivity negatively affects satisfaction. Specifically, purchase preference has strongly moderated the relationship between time, price sensitivity and satisfaction. In addition, the findings show that when purchase preference is high, the effect of price sensitivity on satisfaction is stronger, suggesting the importance of purchase preference in strengthening purchase intentions. The research work recommends a pricing strategy involving value-added pricing primarily for time-sensitive customers, which can help build a high-end brand image and reduce price competition. Reduced pricing is mainly for price-sensitive customers, which is conducive to stimulating consumption within a specific time. This pricing strategy is important for adjusting market sensitivity and flexibility.

This research provides new ideas for related disciplines and guidance for the differentiated pricing and promotion of takeout platforms, as well as a theoretical basis for the diversified development of takeout platforms, improvement of personalized service quality and enhancement of customer stickiness. This study fills gaps in the existing literature on the moderating effect of purchase preference on time sensitivity and satisfaction and price sensitivity and satisfaction.

This paper presents a model relating Nagle and Holden's factors of price sensitivity to expected price and willingness to pay. This work presents various perspectives on price elasticity/sensitivity, empirically tests aspects of the influence of perception of the offer (product/service) on expected price, and illustrates how the pricing methods developed within provide quantitative precision to the practice of price setting by capturing perceptions important to consumers.

The authors used a within‐subjects design to study four brands in two product categories, automobiles and computers. Model evaluation employs ordinary least squares regression.

Ten qualitative factors were studied. Overall, the results show four factors predict expected price for the target market, product and brand. The factors are perceived substitutes, quality, fairness, and unique value.

This research makes the following contributions. First, the authors are able to quantify ten factors of price sensitivity relevant to the evaluation of product pricing. Second, they are able to identify the relevant factors of price sensitivity for two product categories specific to a given target market. Third, they provide a data‐driven model that enables translation of pricing variables into quantitative values to arrive at the price of a product. The major theoretical contribution of this paper is to show that Nagle and Holden's ten factors of price sensitivity may act in the following way: the change in product/service perception may influence expected price, and then the change in expected price influences willingness to pay. The empirical focus of the current research is on the first of these two changes.

The purpose of this paper is to develop a model to hedge annuity portfolios against increases in life expectancy. Across the globe, and in the industrial nations in particular, people have seen an unprecedented increase in their life expectancy over the past decades. The benefits of this apply to the individual, but the dangers apply to annuity providers. Insurance companies often possess no effective tools to address the longevity risk inherent in their annuity portfolio. Securitization can serve as a substitute for classic reinsurance, as it also transfers risk to third parties.

This paper extends on methods insurer's can use to hedge their annuity portfolio against longevity risk with the help of annuity securitization. Future mortality rates with the Lee-Carter-model and use the Wang-transformation to incorporate insurance risk are forecasted. Based on the percentile tranching method, where individual tranches are aligned to Standard & Poor's ratings, we price an inverse survivor bond. This bond offers fix coupon payments to investors, while the principal payments are at risk and depend on the survival rate within the underlying portfolio.

The contribution to the academic literature is threefold. On the theoretical side, building on the work of Kim and Choi (2011), we adapt their pricing model to the current market situation. Putting the principal at risk instead of the coupon payments, the insurer is supplied with sufficient capital to cover additional costs due to longevity. On the empirical side, the method for the German market is specified. Inserting specific country data into the model, price sensitivities of the presented securitization model are analyzed. Finally, in a case study, the procedure to the annuity portfolio of a large German life insurer is applied and the price of hedging longevity risk is calculated.

To illustrate the implication of this bond structure, several sensitivity tests were conducted before applying the pricing model to the retail sample annuity portfolio from a leading German life insurer. The securitization structure was applied to calculate the securitization prices for a sample portfolio from a large life insurance company.

The findings contribute to the current discussion about how insurers can face longevity risk within their annuity portfolios. The fact that the rating structure has such a severe impact on the overall hedging costs for the insurer implies that companies that are willing to undergo an annuity securitization should consider their deal structure very carefully. In addition, we have pointed out that in imperfect markets, the retention of the equity tranche by the originator might be advantageous. Nevertheless, one has to bear in mind that by this behavior, the insurer is able to reduce the overall default risk in his balance sheet by securitizing a life insurance portfolio; however, the fraction of first loss pieces from defaults increases more than proportionally. The insurer has to take care to not be left with large, unwanted remaining risk positions in his books.

In this paper, we extend on methods insurer's can use to hedge their annuity portfolio against longevity risk with the help of annuity securitization. To do so, we take the perspective of the issuing insurance company and calculate the costs of hedging in a four-step process. On the theoretical side, building on the work of Kim and Choi (2011), we adapt their pricing model to the current market situation. On the empirical side, we specify the method for the German market. Inserting specific country data into the model, price sensitivities of the presented securitization model are analyzed.

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization. A modest attempt has been made in the study to capture the relationship between the sales promotion, price discount and the batch procurement strategy of a particular product category to maximize sales volume and profitability.

Time series data relating to sales have been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products that considerably impact the sales promotion and intelligent pricing decisions. A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic.

The model captures the lag effect of sales promotion and price discounting strategy; other strategies have been formulated based upon the sales forecast that was done for taking the lot sizing decisions regarding procurement of products in the selected category. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact.

There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories.

The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization.

Time series data relating to sales has been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products and selection of suppliers. A hybrid model has been proposed and explained with a hypothetical case, which considerably impacts the sales promotion and intelligent pricing decisions.

A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. The model imitates sales promotion and price discounting strategy. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact.

There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories.

The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

This study explores hedging strategies that use the KTB futures to hedge the price risk of the KTB spot portfolio. The study establishes the price sensitivity, risk-minimization, bivariate GARCH (1,1) models as hedging models, and analyzes their hedging performances. The sample period covers from September 29, 1999 to September 18, 2001. Time-matched prices at 11:00 (11:30) of the KTB futures and spot were used in the analysis. The most important findings may be summarized as follows. First, while the average hedge ration of the price sensitivity model is close to one, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios tend to be greater for daily data than for weekly data. Second, for the daily in-sample data, hedging effectiveness is the highest for the GARCH model with time-varying hedge ratios, but the risk-minimization model with constant hedge ratios is not far behind the GARCH model in its hedging performance. In the case of out-of-sample hedging effectiveness, the GARCH model is the best for the KTB spot portfolio, and the risk-minimization model is the best for the corporate bond portfolio. Third, for daily data, the in-sample hedge shows a better performance than the out-of-sample hedge, except for the risk-minimization hedge against the corporate bond portfolio. Fourth, for the weekly in-sample hedges, the price sensitivity model is the worst and the risk-minimization model is the best in hedging the KTB spot portfolio. While the GARCH model is the best against the KTB +corporate bond portfolio, the risk-minimization model is generally as good as the GARCH model. The risk-minimization model performs the best for the weekly out-of-sample data, and the out-of-sample hedges are better than the in-sample hedges. Fifth, while the hedging performance of the risk-minimization model with daily moving window seems somewhat superior to the traditional risk-minimization model when the trading volume increased one year after the inception of the KTB futures, on the average the traditional model is better than the moving-window model. For weekly data, the traditional model exhibits a better performance. Overall, in the Korean bond markets, investors are encouraged to use the simple risk-minimization model to hedge the price risk of the KTB spot and corporate bond portfolios.

Integrating new technological attributes into existing products is a common way to innovate and is supposed to meet consumers’ functional needs. This paper aims to demonstrate how adding such attributes also increases willingness to pay (WTP) a premium for a product by activating consumers’ social need to feel unique.

The data were collected through a quantitative survey based on a nationally representative sample (N = 345). A choice-based conjoint analysis was used to estimate the perceived value of the new technological attribute and WTP a premium.

The perceived value of the new technological attribute has a positive effect on WTP a premium only for consumers with a high degree of social innovativeness (linked to their need for uniqueness) because they interpret this innovation as an opportunity to differentiate themselves from others.

When companies innovate by introducing new technological attributes, their communication should emphasize and trigger these attributes’ high performance and uniqueness. Thus, consumers seeking social differentiation through innovation will be much less sensitive to price and will be more prone to pay a premium for these products.

The main contribution of this article is to show that integrating and emphasizing a new technological attribute can increase consumers’ WTP a premium beyond that of the attribute’s functional value. Thus, new technological attributes will decrease the price sensitivity of consumers high in social innovativeness and increase their WTP a premium for the product, because they consider it as a means to stand out from others.

This paper aims to investigate the effect of product price image (PPI) on perceived value (PV) and repurchase intention (RI) of Brazilian customers' craft beer. Moreover, this research also verifies the moderating effect of customer experience (CE) and price sensitivity.

The survey data analysis was performed using Smart-PLS 3.3.9 and Process 4.1 software with 329 customers.

The results show that PV is a full mediation variable in the relationship between PPI and RI. As a mediated moderation, lower CE and price sensitivity better explain the indirect effect of PPI on RI via PV.

Thus, managers may reinforce the PV of low price sensitivity and low CE. These customers learn about companies' prices compared with higher price-sensitive customers and higher CE that already PV.

The article discusses the implications of PV as a mediator, low price sensitivity and low CE as moderators for craft beer.

1. The world's most widely consumed alcoholic beverage, following water and tea, the third-most-popular drink on earth is beer;

2. Managers should create strategies to reinforce the PV and consequently the RI by offering PPI and benefits (PV) for customers with low experience and low-price sensitivity about craft beer;

3. Low customer experience and low-price sensitive's customers are learning about companies' prices compared to higher price sensitive, and higher customer experience that already PV, PL, PF, and PEs and NEs;

4. Small producers craft beer that connotated the product's high quality and benefits; the higher may be the level of RI and consumption from customers.

The world's most widely consumed alcoholic beverage, following water and tea, the third-most-popular drink on earth is beer;

Managers should create strategies to reinforce the PV and consequently the RI by offering PPI and benefits (PV) for customers with low experience and low-price sensitivity about craft beer;

Low customer experience and low-price sensitive's customers are learning about companies' prices compared to higher price sensitive, and higher customer experience that already PV, PL, PF, and PEs and NEs;

Small producers craft beer that connotated the product's high quality and benefits; the higher may be the level of RI and consumption from customers.

This paper aims to examine the price-sensitivity of information under capital market disclosure regulation, the Australian continuous disclosure regulation (CDR).

The study tests the information content of continuous disclosures and identifies the firm characteristics that condition the price-sensitivity of information under CDR.

The study provides evidence that continuous firm disclosures are significantly associated with stock price adjustment to information. Further results are consistent with firm disclosure and its information content being determined by the economics of the firm.

The findings of the study support the introduction of ongoing and continuous disclosure regimes in a number of capital markets, and assist firms and regulators model the price-sensitivity of information under CDR.

The study highlights the sources of an informed market, and contributes to our understanding of the conditions under which the CDR reveals unexpected information. The results provide evidence of an association between firm disclosure and stock price synchronicity, consistent with managerial incentives to disclose information.

The online direct selling mode has been widely accepted by enterprises in the O2O era. However, the dual-channel (online/offline, forward/backward) operations of the closed-loop supply chain (CLSC) changed the relationship between manufacturers and retailers, thus resulting in channel conflict. The purpose of this paper is to take a dual-channel operations of CLSC as the research target, where a manufacturer sells a single product through a direct e-channel as well as a conventional retail channel; the retailer are responsible for collecting used products in the reverse supply chain and the manufacturer are responsible for remanufacturing.

The authors build a benchmark model of dual-channel price and service competition and take the return rate, which is considered to be related to the service level of the retailer, as the function of the service level to extend the model in the reverse SC. The authors then analyze the optimal pricing and service decision under centralization and decentralization, respectively. Finally, with the revenue-sharing factor, wholesale price and recycling price transfer payment coefficient as contract parameters, the paper also designs a revenue-sharing contract led by the manufacturer and explores in what situation the contract could realize the Pareto optimization of all players.

In the baseline model, the results show that optimal price and service level correlate positively in centralization; however, the relation relies on consumers’ price sensitivity in decentralization. In the extension model, the relationship between price and service level also relies on the relative value of increased service cost and remanufacturing saved cost. When the return rate correlates with the service level, a recycling transfer payment can elevate the service level and thus raise the return rate. Through analyzing the parameters in revenue-sharing contract, a point can be reached where lowering the wholesale price and raising the transfer payment coefficient will promote retailers to share revenue.

Many enterprises establish the dual-channel distribution system both online and offline, which need to understand how to resolve their channel conflict. The conflict is especially strong in CLSC with remanufacturing. The result helps the node enterprises realize the coordination of the dual-channel CLSC.

It takes into account the fact that there are two complementary relationships, such as online selling and offline delivery; used product recycling and remanufacturing. The authors optimize the strategy of product pricing and service level in order to solve channel conflict and double marginalization in the closed-loop dual-channel distribution network.