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1 – 7 of 7Lukas Jürgensmeier, Jan Bischoff and Bernd Skiera
Large digital platforms face intense scrutiny over self-preferencing, which involves a platform provider favoring its own offers over those of competitors. In online marketplaces…
Abstract
Purpose
Large digital platforms face intense scrutiny over self-preferencing, which involves a platform provider favoring its own offers over those of competitors. In online marketplaces, also called retail or e-commerce platforms, much of the academic and regulatory debate focuses on determining whether the marketplace provider gives preference to its own private labels, such as “Amazon Basics” or Walmart’s “Great Value” products. However, we outline, both conceptually and empirically, that self-preferencing can also occur through other dimensions of vertical integration – namely, retailing and fulfillment.
Design/methodology/approach
This article contributes by conceptualizing three dimensions of vertical integration in online marketplaces – private labels, retailing and fulfillment. Then, two studies empirically assess (1) which of the 20 most-visited global online marketplaces vertically integrates which dimension and (2) which share of 600 m available offers is vertically integrated to which degree in eleven international Amazon marketplaces.
Findings
The majority of the leading marketplaces vertically integrate all three dimensions, implying ample opportunities for self-preferencing. Across international Amazon marketplaces, only 0.02% of available offers consist of an Amazon private-label product. However, Amazon is a retailer for around 31% and fulfills around 38% of all available offers in its marketplaces. Hence, self-preferencing on Amazon can occur most frequently through retailing and fulfillment but comparatively infrequently through private-label offers. Still, these shares differ substantially by country – every second offer is vertically integrated in the USA, but only one in ten in India.
Originality/value
Most of the self-preferencing debate often focuses on private-label products. Instead, we present large-scale empirical results showing that self-preferencing on Amazon could occur most often through retailing and fulfillment because these channels affect much larger shares of offers. We also measure the variation of these shares across countries and relate them to regulatory environments.
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V. Kumar, Veena Chattaraman, Carmen Neghina, Bernd Skiera, Lerzan Aksoy, Alexander Buoye and Joerg Henseler
The purpose of this paper is to provide insights into the benefits of data‐driven services marketing and provide a conceptual framework for how to link traditional and new sources…
Abstract
Purpose
The purpose of this paper is to provide insights into the benefits of data‐driven services marketing and provide a conceptual framework for how to link traditional and new sources of customer data and their metrics. Linking data and metrics to strategic and tactical business insights and integrating a variety of metrics into a forward‐looking dashboard to measure marketing ROI and guide future marketing spend is explored.
Design/methodology/approach
A detailed synthesis of the literature is conducted and contemporary sources of marketing data are categorized into traditional, digital and neurophysiological. The benefits and drawbacks of each data type are described and advantages of integrating different sources of data are proposed.
Findings
The findings point to the importance and untapped potential of data in its ability to inform tactical and strategic marketing decisions. Future challenges, including top management support, ethical considerations and developing data and analytic capabilities, are discussed.
Practical implications
The results demonstrate the need for executive service marketing dashboards that include key metrics that are service‐relevant, complementary and forward‐looking, with proven linkages to business outcomes.
Originality/value
This paper provides a synthesis of data‐driven services marketing and the value of traditional and contemporary metrics. Since the true potential of data‐driven service management in a connected world is still largely unexplored, this paper also delineates fruitful avenues for future research.
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This purpose of this article is to solve the problem of bidding on keywords in newly set-up search engine advertising campaigns. Advertisers setting up search engine advertising…
Abstract
Purpose
This purpose of this article is to solve the problem of bidding on keywords in newly set-up search engine advertising campaigns. Advertisers setting up search engine advertising campaigns for the first time need to place bids on keywords, but typically lack experience and data to determine ranks that maximize a keyword’s profit (generally referred to as a cold-start problem).
Design/methodology/approach
The authors suggest that advertisers collect data from the Google Keyword Planner to obtain precise estimates of the percentage increases in prices per click and click-through rates, which are needed to calculate optimal bids (exact approach). Together with the profit contribution per conversion and the conversion rate, the advertiser might then set bids that maximize profit. In case advertisers cannot afford to collect the required data, the authors suggest two proxy approaches and evaluate their performance using the exact approach as a benchmark.
Findings
The empirical study shows that both proxy approaches perform reasonably well, the easier approach to implement (Proxy 2) sometimes performs even better than the more sophisticated one (Proxy 1). As a consequence, advertisers might just use this very simple proxy when bidding on keywords in newly set-up search engine advertising campaigns.
Originality/value
This research extends the stream of literature on how to determine optimal bids, which so far focuses on campaigns that are already running and where the required data to calculate bids are already available. This research offers a novel approach of determining bids when advertisers lack the aforementioned information.
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In cooperation with a German online retail bank, the aim of this paper is to investigate how the bank should price a new fee-only financial advisory service. Two types of pricing…
Abstract
Purpose
In cooperation with a German online retail bank, the aim of this paper is to investigate how the bank should price a new fee-only financial advisory service. Two types of pricing plans differ in terms of their strategies for determining monthly prices: a fixed monthly price that is identical for all clients (i.e. a flat pricing plan) or a monthly price that varies as a function of each client's assets under management (i.e. a volume pricing plan).
Design/methodology/approach
With a discrete choice experiment, this article studies client preferences for the two types of plans. To ensure that the respondents understood the financial consequences of their decisions, a price calculator was embedded into the discrete choice experiment to enable the respondents to determine their individual monthly prices based on their assets under management.
Findings
Methodologically, the price calculator is useful for simplifying mathematically complex decisions, and it provides additional valuable information for analysis. Substantively, the results show that clients perceive both types of pricing plans as equally attractive; however, the service provider's revenues would increase by up to 12 per cent if it uses the volume pricing plan.
Originality/value
This research extends the stream of literature on the measurement of pricing plan preferences and offers guidance for service industries, such as telecommunications, cloud computing services, insurances, or transportation. It extends the use of discrete choice experiments to study client preferences for different pricing plans and also integrates a decision aid, i.e. a price calculator, in the experiment to assist clients in comparing alternatives more effectively.
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Ahmed Shahriar Ferdous, Michael Polonsky and David Hugh Blore Bednall
Frontline employees (FLEs) are a key source of competitive advantage for organizations and have a significant impact on the quality of customer–firm interactions. This study aims…
Abstract
Purpose
Frontline employees (FLEs) are a key source of competitive advantage for organizations and have a significant impact on the quality of customer–firm interactions. This study aims to use the stimulus-organism-response (S-O-R) model as a theoretical lens to examine whether internal communication (IC) (stimulus) evokes FLEs’ organizational identification (emotional) and job satisfaction (cognitive), and whether these in turn shape FLE customer-oriented behavior (response). The study also tested whether these mediated relationships are moderated by perceived communication formalization.
Design/methodology/approach
The hypothesized mediated and moderated effects were tested using data collected from a cross-sectional survey of 293 full-time salespeople working for a large general insurance company.
Findings
Both organizational identification and job satisfaction simultaneously mediate the relationship between IC and customer-oriented behavior. Perceived communication formalization was found to weaken the mediated relationship between IC and customer-oriented behavior, but only when this is via job satisfaction.
Research limitations/implications
This study has shown that where IC is positively viewed by FLEs, it can be leveraged as a key driver by organizations to evoke simultaneous positive emotional and cognitive reactions, leading to increased customer-oriented behavior.
Practical implications
This study informs both theory and practice related to effective IC among customer-contact FLEs.
Originality/value
The study shows how IC can simultaneously produce two simultaneous emotional and cognitive reactions leading to FLE customer-oriented behavior and how these mediated relationships can be moderated by perceived communication formalization. The study used the S-O-R model as the theoretical lens to test these relationships.
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