Discretionary services typically refer to professional work and complex service work by physicians, software developers, web designers, lawyers, or financial analysts, where there are no standard working processes and customer perceived quality of service increases with the time spent on it. Recently, research on these services, especially the corresponding speed-quality tradeoff problem, has gained more and more attention. This paper reviews both the analytical models and the empirical studies in this area, highlighting their contributions and pointing out potential directions for future research.

The Maximum Likelihood Estimation (MLE) method is an established statistical method to estimate unknown parameters of a distribution. A disadvantage of the MLE method is that it requires an analytically tractable density, which is not available in many cases. This is the case, for example, with applications in service systems, since waiting models from queueing theory typically have no closed-form solution for the underlying density. This problem is addressed in this paper. MLE is used in combination with Stochastic Approximation (SA) to calibrate the arrival parameter θ of a G/G/1 queue via waiting time data. Three different numerical examples illustrate the application of the proposed estimator. Data sets of an M/G/1 queue, G/M/1 queue and model mismatch are considered. In a model mismatch, a mismatch is present between the used data and the postulated queuing model. The results indicate that the estimator is versatile and can be applied in many different scenarios.

Although online reverse commerce (recommerce) is convenient and efficient, it is not without caveats. It limits recommerce firms’ flexibility to offer personalized prices and may cause mismatched grading between the firms and sellers of used products. This study examines a recommerce firm’s decision on grading criteria and prices. We find that the firm’s optimal policy exhibits two distinctly different patterns depending on the trade value of the product. We demonstrate that sellers’ overestimate and underestimate errors have qualitatively different effects on firm profitability, and the effects crucially rely on the type of optimal policy. These findings can apprise firms on how to preset sorting criteria and prices as well as reduce grading errors.

This paper discusses optimal dual-channel dynamic pricing of a retailer who sells perishable products in a finite horizon. The type of product which is equipped with different attenuation coefficients of demands on different sales channels is considered. Novel demand functions for the two channels are proposed according to attenuation coefficients of demands, and then a decision model is constructed, which can be handled stage-by-stage. It is shown that the sales price and the sales quantity of the channel which possesses more market shares are both higher than the ones of the other channel at each sales stage. More importantly, by analyzing the reasonability of the obtained solution, a necessary and sufficient condition is proposed to guarantee that both of the two channels will not stop selling through the entire period. We also propose an approach by the elimination method to deal with cases in which some channel stops selling. Further, we demonstrate that the channel which possesses more market shares is the optimal option when only one channel runs. Finally, numerical examples are presented to investigate the change of sales prices of the two channels under different market potential demands.

The technology, which enables creating new types of products, processes and services (i.e., things), which outcomes alter traditional competition and industry boundaries and create new lasting value. The digitalization process uses digital technologies to provide the possibilities of new revenue and value-producing, i.e., it changes business models and offers new value propositions. This change is ongoing. The most important ten strategic technology trends in 2019 include Edge computing, Blockchain, event and data-driven strategies, Digital Twins, and the maintenance of transparency (i.e., traceability), Intelligent Apps and Analytics (Gartner 2018). In this paper, we experiment with the capabilities of intelligent applications to match the industrial business needs. This paper aims to bring insights closer to business objectives. Digitalization’s technological advantages can be achieved through data-driven strategies and wherein cognitive services are integrated into IoT (Internet of Things) and big data. We experiment with the Industrial IoT (IIoT) business models and value propositions to match the intelligent insights of cognitive solutions to business objectives. The IIoTs support and demand transparency and thus also data-driven objective insights, and because cognitive solutions can enhance insights on a product, a process or service and therefore provide measurable business objectives. Functional indicators enable interconnected smart things to collaborate.

As a kind of the most significantly popular information in markets, the sales ranking has great impacts on consumer choice. However, there are few discussions on how sales ranking should be provided to consumers in the literature. This paper aims to answer the following two questions: 1) To what extent does the sales ranking influence consumer choices; 2) When the sales ranking should be provided to consumers. To do so, this paper first constructs a sales ranking model and then provides detailed simulation experiments to demonstrate the model. The experimental results show that for markets where consumer preferences are dramatically different, such as music and movie markets, sales rankings do not have significant influences on consumer choices and should not be provided to consumers until a large number of early independent consumer choices have been accumulated. But for markets in which consumer preferences are similar, such as markets for official supplies, sales rankings have more influences on consumer choices and should be provided to consumers earlier. Furthermore, an evolution strategy is proposed to ascertain the most suitable sales rankings (characterised by suitable influence strength and suitable release time) for some specified online markets. The comparison results show that the optimized sales rankings not only can help consumers discover higher-quality products but also can improve overall sales.