Towards customer-induced service orchestration - requirements for the next step of customer orientation

Alt, Rainer; Ehmke, Jan Fabian; Haux, Reinhold; Henke, Tino; Mattfeld, Dirk C.; Oberweis, Andreas; Paech, Barbara; Winter, Alfred

doi:10.1007/s12525-019-00340-3

Cited by 36 publications

(26 citation statements)

References 65 publications

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“…Previous research often takes a provider perspective by focusing on individual services, while neglecting the actual customer need for bundled services. This applies to different fields of service, such as education, finance, health, and in particular mobility (Alt et al 2019;Willing et al 2017aWilling et al , 2017b. Willing et al (2017b, p. 178) point out a potential way to close this research gap, stating that "researchers can help to identify and define what constitutes a good intermodal [i.e., combination of single-trip multiple mobility services] value proposition regarding platform [i.e., a smart mobility app] design".…”

Section: Responsible Editor: Rainer Altmentioning

confidence: 99%

Smart mobility – an analysis of potential customers’ preference structures

et al. 2020

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Cities around the world face major challenges caused by the extensive use of private cars. To counteract these problems, a new paradigm is necessary which promotes alternative mobility services. ‘Smart mobility’ refers to a new mobility behaviour that makes use of innovative technical solutions, such as the IT-supported combination of different alternative mobility services during a trip from an origin to a destination. Unfortunately, relatively few customers use apps that provide recommendations for smart mobility and there is limited knowledge about the desires, priorities and needs of potential customers. To fill this gap, we use conjoint analysis to explore differences in smart mobility app preferences across groups of people with varying mobility behaviour. Our study also considers the effect of age and place of residence on preference structures. Our results show, for example, that only car drivers do not consider the price of the smart mobility app to be particularly important for their selection decision.

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Section: Responsible Editor: Rainer Altmentioning

confidence: 99%

Smart mobility – an analysis of potential customers’ preference structures

et al. 2020

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“…As described above, customer understanding cannot solely be derived from available data (inside-out view, cf. [2,61]). Customers might also provide valuable personal context themselves to a service provider directly (outside-in view).…”

Section: Customer-dominant Logic: Customer Processes and Customer Contextmentioning

confidence: 99%

“…The need for customer orientation and personalized services [1][2][3] has resulted in a rising focus on personal customer data [1,4,5]. A wealth of customer data from connected people [6,7] has emerged and it seems promising to use this data to build up a deep customer understanding as the basis of customer-centered service design [1,8].…”

Section: Introductionmentioning

confidence: 99%

Customer Data Mapping - A Method for data-driven Service Innovation

Blöcher¹,

Wittwer²,

Alt³

2020

WI2020 Zentrale Tracks

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Service providers nowadays face a complex situation, which is characterized by highly-demanding customers on the one and a plethora of potentially relevant data on the other hand. Data-driven service offerings need to be based on a solid understanding of available data in order to design personal value propositions. This research proposes a visual approach to build up data understanding from a customer perspective and highlights the potential of customer data. Based on the Customer-Dominant Logic, it develops the method "Customer Data Mapping" which supports businesses in establishing customer understanding through a structured process in a collaborative setting. It guides participants from capturing customer data along the customer journey to deriving customer understanding as the foundation for data-driven services.

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“…-This leads to the fifth contribution, which is a position paper introducing the concept of customer inducement. In their position paper the authors Rainer Alt, Jan Fabian Ehmke, Reinhold Haux, Tino Henke, Dirk Christian Mattfeld, Andreas Oberweis, Barbara Paech and Alfred Winter complement the research paper from Dreyer et al (2019) and argue that current approaches to customer orientation are still largely provider-oriented (Alt et al 2019). They present the concept of customer-induced service orchestration that aims at empowering customers to bundle service from various providers along entire customer journeys.…”

Section: Special Issue Papersmentioning

confidence: 99%

Smart services: The move to customer orientation

et al. 2019

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Dear readers, Today, the attribute Bsmart^is widely used. Among the examples are smart infrastructures, smart systems, smart cities, smart products or even the smart world (Liu et al. 2019). In a preface to a special issue published in Electronic Markets on smart tourism, the guest editors mentioned that BBSmart^has become a new buzzword to describe technological, economic and social developments fueled by technologies that rely on sensors, big data, open data, new ways of connectivity and exchange of information (e.g., Internet of Things, RFID, and NFC) as well as abilities to infer and reason.^(Gretzel et al. 2015, p. 179). Since then the Bbuzz^has spread further and as with many buzzwords, their understanding is heterogeneous and differs depending on the background of the authors. For example, many researchers in the domain of smart cities adopt a technological perspective and analyze sensors, interfaces or platform architectures that enable use cases in an urban environment (e.g. Puiu et al. 2016). A view on smart products and the associated topics of cyber-physical systems reveals a more or less similar picture. On the other side, researchers from the information systems discipline bring attention to the increasing capabilities of computers, and how they are increasingly doing things that humans could once do exclusively. In their definition, smart machines are becoming like humans by recognizing voices, processing natural language, learning, and interacting with the physical world through their vision, smell, touch, and other senses, mobility and motor control with advanced artificial intelligence, intelligence augmentation and data (Demirkan et al. 2016; Matzner et al. 2018). They profoundly impat the current organization of business processes as illustrated in muliple examples by Porter and Heppelmann (2015). The present special issue on smart services follows this direction and that of the earlier on smart tourism by considering the interplay of technological, economic and social dimensions.

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Towards customer-induced service orchestration - requirements for the next step of customer orientation

Cited by 36 publications

References 65 publications

Smart mobility – an analysis of potential customers’ preference structures

Smart mobility – an analysis of potential customers’ preference structures

Customer Data Mapping - A Method for data-driven Service Innovation

Smart services: The move to customer orientation

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