PSS as a service based strategy for western manufacturers

A product-service system (PSS) is a subtle blend of products and services that are offered as an integrated solution to customers. Much existing work on PSS has originated from Scandinavia and has been motivated by a sustainability agenda. Although valuable, this form has limited appeal to western manufacturers. However, by expanding the concepts of PSS to also embrace leading thinking on large scale complex service systems and informated products and services, it is possible to put forward the basis of a service business model that offers the means to differentiate from competitors who simply offer lower priced products. This paper aims to build this case. It reports the state-of-the-art of PSS, defines the concept, reports on its origin and features, discusses examples of applications, and finally proposes a research strategy for future work on this topic.

Putting service relations to the test:how can negative consumer reactions of price increases be reduced?

Price increases seem to be an adequate way to improve the earnings of companies. This fact becomes especially crucial because of increased price competition in many markets. Price increases might lead to negative customer reactions, such as a lower perceived utility or a lower loyalty intention. Therefore, the question for managers remains how prices can be increased without losing customers. Results of our experimental study suggest that customers of energy suppliers rate the perceived utility of the offer relatively better when the price increase is combined with an additional modification of the product or accompanied by a new service. It becomes clear that intensifying service relations can offset the negative effects of price increases.

Pragmatic development of service based real-time change data capture

This thesis makes a contribution to the Change Data Capture (CDC) field by providing an empirical evaluation on the performance of CDC architectures in the context of realtime data warehousing. CDC is a mechanism for providing data warehouse architectures with fresh data from Online Transaction Processing (OLTP) databases. There are two types of CDC architectures, pull architectures and push architectures. There is exiguous data on the performance of CDC architectures in a real-time environment. Performance data is required to determine the real-time viability of the two architectures. We propose that push CDC architectures are optimal for real-time CDC. However, push CDC architectures are seldom implemented because they are highly intrusive towards existing systems and arduous to maintain. As part of our contribution, we pragmatically develop a service based push CDC solution, which addresses the issues of intrusiveness and maintainability. Our solution uses Data Access Services (DAS) to decouple CDC logic from the applications. A requirement for the DAS is to place minimal overhead on a transaction in an OLTP environment. We synthesize DAS literature and pragmatically develop DAS that eciently execute transactions in an OLTP environment. Essentially we develop effeicient RESTful DAS, which expose Transactions As A Resource (TAAR). We evaluate the TAAR solution and three pull CDC mechanisms in a real-time environment, using the industry recognised TPC-C benchmark. The optimal CDC mechanism in a real-time environment, will capture change data with minimal latency and will have a negligible affect on the database's transactional throughput. Capture latency is the time it takes a CDC mechanism to capture a data change that has been applied to an OLTP database. A standard definition for capture latency and how to measure it does not exist in the field. We create this definition and extend the TPC-C benchmark to make the capture latency measurement. The results from our evaluation show that pull CDC is capable of real-time CDC at low levels of user concurrency. However, as the level of user concurrency scales upwards, pull CDC has a significant impact on the database's transaction rate, which affirms the theory that pull CDC architectures are not viable in a real-time architecture. TAAR CDC on the other hand is capable of real-time CDC, and places a minimal overhead on the transaction rate, although this performance is at the expense of CPU resources.

A strategy formulation process for the delivery of technology enabled service delivery systems

The Product Service Systems, servitization, and Service Science literature continues to grow as organisations seek to protect and improve their competitive position. The potential of technology applications to deliver service delivery systems facilitated by the ability to make real time decisions based upon ‘in the field’ performance is also significant. Research identifies four key questions to be addressed. Namely: how far along the servitization continuum should the organisation go in a single strategic step? Does the organisation have the structure and infrastructure to support this transition? What level of condition monitoring should it employ? Is the product positioned correctly in the value chain to adopt condition monitoring technology? Strategy consists of three dimensions, namely content, context, and process. The literature relating to PSS, servitization, and strategy all discuss the concepts relative to content and context but none offer a process to deliver an aligned strategy to deliver a service delivery system enabled by condition based management. This paper presents a tested iterative strategy formulation methodology which is the result of a structured development programme.

Making energy service companies (ESCOS) future fit

Energy service companies (ESCOs) are faced with a range of challenges and opportunities associated with the rapidly changing and flexible requirements of energy customers (end users) and rapid improvements in technologies associated with energy and ICT. These opportunities for innovation include better prediction of energy demand, transparency of data to the end user, flexible and time dependent energy pricing and a range of novel finance models. The liberalisation of energy markets across the world has leads to a very small price differential between suppliers on the unit cost of energy. Energy companies are therefore looking to add additional layers of value using service models borrowed from the manufacturing industry. This opens a range of new product and service offerings to energy markets and consumers and has implications for the overall efficiency, utility and price of energy provision.

Intellectual property activity by service sector and manufacturing firms in the United Kingdom, 1996-2000

This paper provides evidence from a newly constructed database of UK firms about the extent of their intellectual property acquisition activities over five years. We focus on service sector firms, which have not previously been studied, with comparisons for firms in manufacturing and other sectors, such as agriculture. The measures of IP include both trade marks, which are most important in services, and patents, which are predominantly sought by manufacturing firms. The analysis includes patents and trade marks applied for via both the UK and European routes. While IP assets sought through the UK Patent Office remained strong, more services firms were seeking European Community trade marks and more manufacturing firms were seeking patents via European Patent Office through time. Firm characteristics that are positively correlated with IP activity include larger firm size, stock market listed status and high product market diversification.

Consumer trial, continuous use, and economic benefits of a retail service innovation:the case of the personal shopping assistant

Service innovations in retailing have the potential to benefit consumers as well as retailers. This research models key factors associated with the trial and continuous use of a specific self-service technology (SST), the personal shopping assistant (PSA), and estimates retailer benefits from implementing that innovation. Based on theoretical insights from prior SST studies, diffusion of innovation literature, and the technology acceptance model (TAM), this study develops specific hypotheses and tests them on a sample of 104 actual users of the PSA and 345 nonusers who shopped at the retail store offering the PSA device. Results indicate that factors affecting initial trial are different from those affecting continuous use. More specifically, consumers' trust toward the retailer, novelty seeking, and market mavenism are positively related to trial, while technology anxiety hinders the likelihood of trying the PSA. Perceived ease of use of the device positively impacts continuous use while consumers' need for interaction in shopping environments reduces the likelihood of continuous use. Importantly, there is evidence on retailer benefits from introducing the innovation since consumers using the PSA tend to spend more during each shopping trip. However, given the high costs of technology, the payback period for recovery of investments in innovation depends largely upon continued use of the innovation by consumers. Important implications are provided for retailers considering investments in new in-store service innovations. Incorporation of technology within physical stores affords opportunities for the retailer to reduce costs, while enhancing service provided to consumers. Therefore, service innovations in retailing have the potential to benefit consumers as well as retailers. This research models key factors associated with the trial and continuous use of a specific SST in the retail context, the PSA, and estimates retailer benefits from implementing that innovation. In so doing, the study contributes to the nascent area of research on SSTs in the retail sector. Based on theoretical insights from prior SST studies, diffusion of innovation literature, and the TAM, this study develops specific hypotheses regarding the (1) antecedent effects of technological anxiety, novelty seeking, market mavenism, and trust in the retailer on trial of the service innovation; (2) the effects of ease of use, perceived waiting time, and need for interaction on continuous use of the innovation; and (3) the effect of use of innovation on consumer spending at the store. The hypotheses were tested on a sample of 104 actual users of the PSA and 345 nonusers who shopped at the retail store offering the PSA device, one of the early adopters of PSA in Germany. Data were analyzed using logistic regression (antecedents of trial), multiple regression (antecedents of continuous use), and propensity score matching (assessing retailer benefits). Results indicate that factors affecting initial trial are different from those affecting continuous use. More specifically, consumers' trust toward the retailer, novelty seeking, and market mavenism are positively related to trial, while technology anxiety hinders the likelihood of trying the PSA. Perceived ease of use of the device positively impacts continuous use, while consumers' need for interaction in shopping environments reduces the likelihood of continuous use. Importantly, there is evidence on retailer benefits from introducing the innovation since consumers using the PSA tend to spend more during each shopping trip. However, given the high costs of technology, the payback period for recovery of investments in innovation depends largely upon continued use of the innovation by consumers. Important implications are provided for retailers considering investments in new in-store service innovations. The study contributes to the literature through its (1) simultaneous examination of antecedents of trial and continuous usage of a specific SST, (2) the demonstration of economic benefits of SST introduction for the retailer, and (3) contribution to the stream of research on service innovation, as against product innovation.

We know what you want to buy:a demographic-based system for product recommendation on microblogs

Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time. © 2014 ACM.

Towards a definition of PLM-integrated dimensional measurement

Product Lifecycle Management (PLM) enables knowledge about products to be captured and reused. Since dimensional measurement is used to determine the size and shape of the products about which PLM is centered, we contend that it is an important process to integrate. Building on emerging industry-accepted standards, a framework was developed in an effort to define what integrating dimensional measurement with PLM involves. Following a survey of the state-of-the-art against this framework and a critical review, technology gaps are identified, and key challenges and research priorities are highlighted. © 2013 The Authors.

Non-stationary stochastic inventory lot-sizing with emission and service level constraints in a carbon cap-and-trade system

Firms worldwide are taking major initiatives to reduce the carbon footprint of their supply chains in response to the growing governmental and consumer pressures. In real life, these supply chains face stochastic and non-stationary demand but most of the studies on inventory lot-sizing problem with emission concerns consider deterministic demand. In this paper, we study the inventory lot-sizing problem under non-stationary stochastic demand condition with emission and cycle service level constraints considering carbon cap-and-trade regulatory mechanism. Using a mixed integer linear programming model, this paper aims to investigate the effects of emission parameters, product- and system-related features on the supply chain performance through extensive computational experiments to cover general type business settings and not a specific scenario. Results show that cycle service level and demand coefficient of variation have significant impacts on total cost and emission irrespective of level of demand variability while the impact of product's demand pattern is significant only at lower level of demand variability. Finally, results also show that increasing value of carbon price reduces total cost, total emission and total inventory and the scope of emission reduction by increasing carbon price is greater at higher levels of cycle service level and demand coefficient of variation. The analysis of results helps supply chain managers to take right decision in different demand and service level situations.

Motivations for servitization:the impact of product complexity

Purpose – The purpose of this paper is to identify the commonalities and differences in manufacturers’ motivations to servitise. Design/methodology/approach – UK study based on interviews with 40 managers in 25 companies in 12 sectors. Using the concept of product complexity, sectors were grouped using the Complex Products and Systems (CoPS) typology: non-complex products, complex products and systems. Findings – Motivations to servitise were categorised as competitive, demand based (i.e. derived from the customer) or economic. Motivations to servitise vary according to product complexity, although cost savings and improved service quality appear important demand-based motivations for all manufacturers. Non-complex product manufacturers also focus on services to help product differentiation. For CoPS manufacturers, both risk reduction and developing a new revenue stream were important motivations. For uniquely complex product manufacturers, stabilising revenue and increased profitability were strong motivations. For uniquely systems manufacturers, customers sought business transformation, whilst new service business models were also identified. Research limitations/implications – Using the CoPS typology, this study delineates motivations to servitise by sector. The findings show varying motivations to servitise as product complexity increases, although some motivational commonality existed across all groups. Manufacturers may have products of differing complexity within their portfolio. To overcome this limitation the unit of analysis was the strategic business unit. Practical implications – Managers can reflect on and benchmark their motivation for, and opportunities from, servitisation, by considering product complexity. Originality/value – The first study to categorise servitisation motivations by product complexity. Identifying that some customers of systems manufacturers seek business transformation through outsourcing.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Frontline employees’ innovative service behavior as key to customer loyalty:insights into FLEs’ resource gain spiral

Many service firms require frontline service employees (FLEs) to follow routines and standardized operating procedures during the service encounter, to deliver consistently high service standards. However, to create superior, pleasurable experiences for customers, featuring both helpful services and novel approaches to meeting their needs, firms in various sectors also have begun to encourage FLEs to engage in more innovative service behaviors. This study therefore investigates a new and complementary route to customer loyalty, beyond the conventional service-profit chain, that moves through FLEs' innovative service behavior. Drawing on conservation of resources (COR) theory, this study introduces a resource gain spiral at the service encounter, which runs from FLEs' emotional job engagement to innovative service behavior, and then leads to customer delight and finally customer loyalty. In accordance with COR theory, the proposed model also includes factors that might hinder (customer aggression, underemployment) or foster (colleague support, supervisor support) FLEs' resource gain spiral. A multilevel analysis of a large-scale, dyadic data set that contains responses from both FLEs and customers in multiple industries strongly supports the proposed resource gain spiral as a complementary route to customer loyalty. The positive emotional job engagement-innovative service behavior relationship is undermined by customer aggression and underemployment, as hypothesized. Surprisingly though, and contrary to the hypotheses, colleague and supervisor support do not seem to foster FLEs' resource gain spiral. Instead, colleague support weakens the engagement-innovative service behavior relationship, and supervisor support does not affect it. These results indicate that if FLEs can solicit resources from other sources, they may not need to invest as many of their individual resources. In particular, colleague support even appears to serve as a substitute for FLEs' individual resource investments in the resource gain spiral.

A value-oriented model for managing service supply chains

Service supply chain (SSC) has attracted more and more attention from academia and industry. Although there exists extensive product-based supply chain management models and methods, they are not applicable to the SSC as the differences between service and product. Besides, the existing supply chain management models and methods possess some common deficiencies. Because of the above reasons, this paper develops a novel value-oriented model for the management of SSC using the modeling methods of E3-value and Use Case Maps (UCMs). This model can not only resolve the problems of applicability and effectiveness of the existing supply chain management models and methods, but also answer the questions of ‘why the management model is this?’ and ‘how to quantify the potential profitability of the supply chains?’. Meanwhile, the service business processes of SSC system can be established using its logic procedure. In addition, the model can also determine the value and benefits distribution of the entire service value chain and optimize the operations management performance of the service supply.