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European Journal of Marketing

ISSN : 0309-0566

Article publication date: 1 February 2003

This paper presents a literature review of, and classification scheme for, Internet marketing (IM) research. The review covers 270 journal articles published between 1987 and 2000 in three types of journals: marketing; economics, business and management; and information systems and information technology. The results show that an increasing volume of IM research has been conducted in a diverse range of areas. A total of 96 percent of the papers were published in the last five years (1996‐2000) of this 14‐year study, and the significance of IM to e‐commerce will increase in the future. The 270 articles are classified into five distinct categories: the IM environment, IM functions, special IM applications, IM research, and other topics. A comprehensive list of references is presented. This review will provide a source for anyone interested in IM research, and will help to simulate further interest in the area.

Ngai, E.W.T. (2003), "Internet marketing research (1987‐2000): a literature review and classification", European Journal of Marketing , Vol. 37 No. 1/2, pp. 24-49. https://doi.org/10.1108/03090560310453894

Copyright © 2003, MCB UP Limited

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Competitive pricing on online markets: a literature review

• Research Article
• Open access
• Published: 14 June 2022
• Volume 21 , pages 596–622, ( 2022 )

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• Torsten J. Gerpott 1 &
• Jan Berends 1  

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Past reviews of studies concerning competitive pricing strategies lack a unifying approach to interdisciplinarily structure research across economics, marketing management, and operations. This academic void is especially unfortunate for online markets as they show much higher competitive dynamics compared to their offline counterparts. We review 132 articles on competitive posted goods pricing on either e-tail markets or markets in general. Our main contributions are (1) to develop an interdisciplinary framework structuring scholarly work on competitive pricing models and (2) to analyze in how far research on offline markets applies to online retail markets.

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Introduction

Setting prices relative to competitors, i.e., competitive pricing, Footnote 1 is a classical marketing problem which has been studied extensively before the emergence of e-commerce (Talluri and van Ryzin 2004 ; Vives 2001 ). Although literature on online pricing has been reviewed in the past (Ratchford 2009 ), interrelations between pricing and competition were rarely considered systematically (Li et al. 2017 ). As less than 2% of high-impact journal articles address pricing issues (Toni et al. 2017 ), pricing strategies do not receive proper research attention according to their practical relevance. This research gap holds even more for competitive pricing. In the past, the monopolistic assumption that demand for homogeneous goods mostly depends on prices set by a single firm may have been a viable simplification since price comparisons were difficult. Today, consumer search costs Footnote 2 shrink as the prices of most goods can be compared on relatively transparent online markets. Therefore, demand is increasingly influenced by prices of competitors which therefore should not be ignored (Lin and Sibdari 2009 ).

In the early 1990s, few people anticipated that business-to-consumer (B2C) online goods retail markets Footnote 3 would develop from a dubious alternative to conventional “brick-and-mortar” retail stores to an omnipresent distribution channel for all kinds of products in less than two decades (Balasubramanian 1998 ; Boardman and McCormick 2018 ). In 2000, e-commerce accounted for a mere 1% of overall retail sales. In 2025, e-retail sales are projected to account for nearly 25% of global retail sales (Lebow 2019 ). Traditional offline channels are nowadays typically complemented by online technologies (Gao and Su 2018 ). With digitization of various societal sectors in general and the COVID-19 pandemic in particular, the shift toward online channels is unlikely to stop in the future. Besides direct online shops, two-thirds of e-commerce sales are sold through online marketplaces/platforms like Alibaba, Amazon or eBay (Young 2022 ). The marketplace operator acts as an intermediary (two-sided platform) who matches demand (online consumers) with supply (retailers). Whereas the retailer retains control over product assortment and prices, he has to pay a commission to the marketplace operator (Hagiu 2007 ). However, these intermediaries often act as sellers themselves, thereby posing direct competition to retailers who have to decide between direct or marketplace channels (Ryan et al. 2012 ).

Online consumer markets fundamentally differ from offline settings (Chintagunta et al. 2012 ; Lee and Tan 2003 ; Scarpi et al. 2014 ; Smith and Brynjolfsson 2001 ). Factors which make competition even more prevalent for online than for offline markets are summarized in Table 1 .

To date, a number of scholarly articles reviews various aspects of pricing under competition or online pricing (Boer 2015a ; Chen and Chen 2015 ; Cheng 2017 ; Kopalle et al. 2009 ; Ratchford 2009 ; Vives 2001 ). Vives ( 2001 ) provides an overview of the history of pricing theory and its evolution from the early work of Bertrand ( 1883 ) who studied a duopoly with unconstrained capacity and identical products to Dudey ( 1992 ) who set the foundation for today’s dynamic pricing Footnote 4 research with constrained capacities and a finite sales horizon. Ratchford ( 2009 ) reviews the influence of online markets on pricing strategies. Although he depicts factors shaping the competitive environment of online markets and compares online versus offline channels, he does not include competitive strategies specifically. This also holds for review papers on dynamic pricing which treat competition rather novercally (Boer 2015a ; Gönsch et al. 2009 ). With emphasis on mobility barriers, multimarket contact and mutual forbearance, Cheng ( 2017 ) studies competition mechanisms across strategic groups. Kopalle et al. ( 2009 ) discuss competitive effects in retail focusing on different aspects such as manufacturer interaction and cross-channel competition. To the best of our knowledge, Chen and Chen ( 2015 ) are the only scholars who review existing competitive pricing research by classifying model characteristics along product uniqueness (identical vs. differentiated), type of customer (myopic vs. strategic), pricing policy (contingent vs. preannounced) and number of competitors (duopoly vs. oligopoly). However, competition is only one of three pricing problems they analyze forcing them to reduce scope and depth and to exclude online peculiarities. In addition, significant competitive pricing contributions were published since 2015 (chapter 2.2). Overall, given the limitations of previous reviews of the pricing literature makes revisiting the current state of research a worthwhile undertaking.

Most often, competitive pricing literature uses simplifying assumptions limiting the applicability of presented models. The simplifications are required to circumvent challenges like the curse of dimensionality (Harsha et al. 2019 ; Kastius and Schlosser 2022 ; Li et al. 2017 ; Schlosser and Boissier 2018 ), endogeneity problems (Cebollada et al. 2019 ; Chu et al. 2008 ; Fisher et al. 2018 ; Villas-Boas and Winer 1999 ), uncertain information (Adida and Perakis 2010 ; e.g., Bertsimas and Perakis 2006 ; Chung et al. 2012 ; Ferreira et al. 2016 ; Keskin and Zeevi 2017 ; Shugan 2002 ) and simultaneity bias (Li et al. 2017 ). As a consequence, early work on pricing strategies with competition was restricted to theoretical discussions (Caplin and Nalebuff 1991 ; Mizuno 2003 ; Perloff and Salop 1985 ). This holds especially true in combination with other practical circumstances such as capacity constraints, time-varying demand or a finite selling horizon (Gallego and Hu 2014 ).

Armstrong and Green ( 2007 ) find empirical evidence that competitive pricing, especially for the sake of gaining market share, harms profitability. Similarly, some researchers cursorily ascribe competitor-based pricing as a sign of a poor management because it signals a lack of capabilities to set prices independently (Larson 2019 ). Revenue management researchers therefore often assume that monopoly pricing models implicitly capture the dynamic effects of competition. The so-called market response hypothesis is the key rationale to neglect the effects of competition altogether (Phillips 2021 ; Talluri and van Ryzin 2004 ). According to this reasoning, competition does not have to be considered as all relevant effects are already included in historical sales data. However, this intuitive argument can be easily rebutted as Simon ( 1979 ) already showed that price elasticities change over time. Furthermore, Cooper et al. ( 2015 ) study the validity of the market response hypothesis and conclude that this monopolistic view is rarely adequate. Monopolistic pricing models can only be applied to stable markets with little time-varying demand and little expected competitive reactions.

Detrimental outcomes of ignoring competition in pricing strategies are shown by Anufriev et al. ( 2013 ), Bischi et al. ( 2004 ), Isler and Imhof ( 2008 ), Schinkel et al. ( 2002 ), and Tuinstra (2004). The negative effects are even more harmful in fierce competitive settings such as situations with a high number of competitors or price sensitive customers (van de Geer et al. 2019 ). Empirical evidence on the influence of competition on pricing decisions is provided by Richards and Hamilton ( 2006 ) who find that retailers compete on price and variety for market share. Li et al. ( 2017 ) observe that competition-based variables explained 30.2% of hotel price variations in New York—compared to 22.3% attributed to demand-side variables. Similarly, Hinterhuber ( 2008 ) assesses competitor-based pricing as a dominant strategy from a practical perspective. Li et al. ( 2008 ) argue that because of its relevance, competition should be considered in operational revenue management and not be treated stepmotherly as an abstract strategic constraint.

Although striving to simplify pricing models is desirable, researchers should thus not simply ignore effects of competition on price setting in a non-monopolistic (online) world. Blindly pegging pricing strategies to competitors or undercutting competitors to gain market share may favor detrimental price wars and not profit-maximizing market structures. Nevertheless, no significant market player can operate isolated on online markets—decisions made always affect competing firms and consumer demand (Chiang et al. 2007 ). In such dynamic markets (chapter 3.3), competition must be considered with time-varying attributes (Schlosser et al. 2016 ).

Against this background, we suggest a conceptual framework to structure research covering competitive online pricing. It can serve scholars as a map to direct future research on the one hand and provide practicing managers with a guide to locate relevant pricing contributions on the other hand. Although the framework can be applied to a variety of markets with competitive dynamics, we concentrate our review on research covering B2C online goods retail markets. Thus, related research with a focus on auction pricing, multichannel peculiarities, behavioral pricing and multi-dimensional pricing approaches such as Everything-as-a-Service (XaaS) or bundle pricing is only assessed when findings are crucial to the competition-related discussion. In the remainder, we proceed as follows. The next chapter provides a descriptive overview of the competitive pricing literature for the subsequent discussion. Chapter 3 puts the identified literature into the perspective of online retail markets considering product and environmental characteristics. Section 4 concludes with practical implications and directions for future research.

Overview of competitive pricing research

Initially, properties of the reviewed literature are briefly summarized. Besides (a) the journal representation, (b) the historical development of online market considerations and (c) research domains, we classify research according to (d) the geographical and industry context as well as (e) research design and empirical foundation.

We identified relevant references through a semi-structured multi-pronged search strategy. Following Tranfield et al. ( 2003 ), we firstly screened the literature reviews mentioned in chapter 1 to obtain an overview of existing research streams. Second, we created a set of potentially relevant contributions by searching multiple keywords in the journal databases EBSCO, Scopus and Web of Science (c.f. Baloglu and Assante 1999 ). Footnote 5 Third, high-impact journals (see Appendix 1) in the academic fields economics, marketing management, and operations were screened. With focus on highly cited (> 10 citations in Scopus), recent (published later than 2000) research, we identified an initial sample of 996 unique papers. Fourth, we studied the abstract and skimmed the text of all papers for relevance to competitive online pricing, reducing our initial set to 174 papers. Fifth, we screened the references of the papers and identified literature cited which we not already included in our set. Sixth, especially for research areas with limited coverage in peer-reviewed journals, we uncovered gray literature through searches with Google Scholar. As a result, this study concentrates on papers published between 2000 and 2022 and only sparsely utilizes literature from the pre-internet era. The final sample of the papers with relevance to competitive B2C online pricing encompasses 132 entries. A complete list of the papers reviewed in great depth is provided in Appendix 2. 94% are peer-reviewed articles. Book chapters, conference papers and preprint/working papers each account for 2%.

Journal representation

Competitive pricing literature is widely dispersed over a broad range of journals as roughly half of the articles considered are from journals with less than three articles in our review. Notably, journals with a higher density of competitive pricing contributions are from the fields of operations, economics or are interdisciplinary. Table 2 reports the distribution of articles among the journals with the highest representation. In addition, it provides the considered articles subject to a content analysis in chapter 3.

Online pricing contributions over time

Between 1976 and the end of the second millennium, the number of papers on competitive pricing in an internet context is naturally limited (Fig.  1 ). Parallel to the dissemination of online use among residential households, interest of researchers in online pricing in a competitive environment started to take off. 71.8% of the papers published from 2015 to 2022 consider online settings specifically. The corresponding statistic from 2000 to 2005 amounts to 43.8%.

Competitive pricing literature and its consideration of online peculiarities accumulated by year

Development of research domains

Competitive pricing literature typically can be assigned to one of the following research domains:

The economics domain takes a market perspective across individual firms. It elaborates on the existence and uniqueness of competitive equilibria also including all subjects regarding econometrics.

The marketing management domain analyzes competitive pricing problems from the perspective of a single firm with a focus on customer reactions to pricing decisions. It includes all subjects linked to marketing, strategy, business, international, technology, innovation, and general management.

The operations domain considers quantitative pricing solutions for, among others, quantity planning, choice of distribution channels, and detection of algorithm driven price collusion. It includes all subjects regarding computer science, industrial and manufacturing engineering, and mathematics.

Separating the last 47 years of competitive pricing research into three intervals, all reviewed papers are assigned to their most affiliated research domain. Although the domains are similarly represented in our review (see Fig.  2 ), we see differences in their temporal change. Whereas rather theoretical economic subjects are covered relatively constant over time, more practice-oriented marketing management and operations subjects gained momentum since 2000. This suggests a shift from model conceptualization toward applicable research, frequently based on empirical data.

Distribution of competitive pricing literature over research domain and time interval

Geographical and industry context

As the origin of revenue management lies in transportation and hospitality optimization problems, one could expect that competitive pricing research also originates in these dynamic sectors. However, our analysis reveals a different picture: Almost half of the papers in our review do not concentrate on a specific industry. Besides, most industry-specific competitive pricing articles focus on retail, with 38% concentrating on the retail industry versus 8% and 4% on transportation and hospitality, respectively (see Fig.  3 ). This supports our proposition in chapter 1 that effects of competition on industry-specific pricing are particularly relevant for online markets.

Competitive pricing research by focal industry and location of lead authors’ institution

Competitive pricing literature is predominantly driven by researchers employed by U.S. institutions (60%). The remaining 40% consist of Europe (19%), Asia (17%) and Canada (4%).

Research approach

A lack of empirical testing is an issue that hampers competitive pricing research. Liozu ( 2015 ) reported that only 15% of general pricing literature include empirical data. For competitive pricing, the situation appears even more aggravated. In addition to parameters such as price elasticities and stock levels of the company under study, comprehensive, real-time information of other market participants is crucial to add practical value.

For instance, to solve a simple Bertrand equilibrium, Footnote 6 full information of all competitors is needed, which is rarely available in real-life settings. Therefore, many problems covered in the literature are of a theoretical nature. In accordance with Liozu ( 2015 ), we find that only 18% of reviewed articles use empirical evidence to validate hypotheses. An additional 23% strive to ameliorate this shortage through simulation data and numerical examples. The remaining 59% fail to bring any empirical evidence or numerical examples.

As can be taken from Fig.  4 , missing empirical support is particularly prevalent for equilibrium models which use empirical data in only 7% of all papers.

Competitive pricing research by research design and empirical validation

Competitive pricing on online markets

In this chapter, we assess the applicability of competitive pricing work to online markets. Typical characteristics of competitive B2C pricing models were derived from literature described in chapter 2. Competitive pricing literature can be classified along four characteristics depicted in Table 3 that form the market environment in which firms compete for consumer demand.

In the remainder of chapter 3, we discuss the four key questions in more depth and elaborate on their applicability to online retail markets.

Product similarity

In general, products in competitive pricing models are either identical (homogeneous) or differentiated by at least one quality parameter (heterogeneous). In case of homogeneous products, pricing is the only purchase decision variable—a perfectly competitive setting (Chen and Chen 2015 ). However, many firms strive to differentiate their products as this shifts the focus from the price as competitive lever to other product-related features (Afeche et al. 2011 ; Boyd and Bilegan 2003 ; Thomadsen 2007 ). According to Lancaster ( 1979 ), there are two types of product differentiation: vertical and horizontal differentiation. Vertical differentiation Footnote 7 encompasses all product distinctions which are objectively measurable and quantifiable regarding their quality level. Horizontal differentiation Footnote 8 can manifest in many variants and includes all product-related aspects which cannot be quantified according to their quality levels. Footnote 9 A key difference in the modeling of substitutable yet differentiated versus identical goods is that customers have heterogenous preferences among products. Footnote 10 A recent stream of literature approaches unknown differentiation criteria by assessing online consumer-generated content (DeSarbo and Grewal 2007 ; Lee and Bradlow 2011 ; Netzer et al. 2012 ; Ringel and Skiera 2016 ; Won et al. 2022 ).

Besides the chosen price level, Cachon and Harker ( 2002 ) argue that firms compete with the operational performance level offered and perceived, i.e., service level in online retail, to differentiate an otherwise homogenous offering. In situations, where resellers with comparable service and shipping policies offer similar products, price is a major decision variable for potential buyers (Yang et al. 2020 ). Often, e-tailers do not possess the right to exclusively distribute a certain product. For example, Samsung’s Galaxy S21 5G was offered by 69 resellers on the German price comparison website Idealo.de. Footnote 11 As some products in e-tail can be differentiated and others cannot, both identical and differentiated product research have their raison d’être for competitive online pricing.

Most competitive pricing models only address the effects of single-product settings. This simplification is reasonable if there is no interdependence between products of an e-tailer (Gönsch et al. 2009 ). Taking up on the smartphone example, the prices of close substitutes, such as Huawei’s P30 Pro, nonetheless have an impact on the demand of Samsung’s Galaxy S21 5G. To further extent product differentiation, price models have to incorporate multi-product pricing problems in non-cooperative settings (Chen and Chen 2015 ). Such models have to account not only for demand impact of directly competing products but also for synergies, cannibalization/substitution effects of (own) differentiated goods. Although there is a recent research stream regarding product assortment (Besbes and Sauré 2016 ; Federgruen and Hu 2015 ; Heese and Martínez-de-Albéniz 2018 ; Nip et al. 2020 ; Sun and Gilbert 2019 ), multi-product work is still underdeveloped. Thus, competitive multi-product pricing constitutes an area which should be addressed in future research.

Product durability

The durability of products is an important feature to differentiate between competitive pricing model types. Durable (non-perishable) products do not have an expiration date, for example consumer durables such as household appliances. Perishable products can only be sold for a limited time interval and have a finite sales horizon. After expiration date, unused capacity is lost or significantly devalued to a salvage value. Footnote 12 Combined with limited capacities, the firm objective is thus most often to maximize turnover under capacity constraints and finite sales horizon (Gallego and van Ryzin 1997 ; McGill and van Ryzin 1999 ; Weatherford and Bodily 1992 ).

Perishability can be of relevance for products with seasonality effects or short product life cycles (i.e., finite selling horizon) such as apparel, food groceries or winter sports equipment. This is especially relevant because online retailers of perishable products are severely restricted in their shipment, return handle policies and supply chain length (Cattani et al. 2007 ). Sellers cannot replenish their inventory after the planning phase and cannot retain goods for future sales periods (Perakis and Sood 2006 ). Some products like apparel—albeit reducing in value after a selling season—still have a certain salvage value and can be sold at reduced prices (Anand and Girotra 2007 ).

It depends on the type of product to decide whether perishability should be included in competitive pricing models. There is a fundamental distinction in the underlying optimization objective for models with or without perishability. Whereas models with perishable products tend to focus on revenue maximization over a definite short-term time horizon, models with durable products tend to focus on profit maximization over an indefinite or at least long-term time horizon by balancing current revenues of existing and future revenues of new customers. To account for this trade-off, models with durable products need to discount future cash flows incorporating time value of money, stock-keeping, opportunity and other costs related to prolonged sales (Farias et al. 2012 ). To conclude, perishability cannot be treated as an extension to durable models but rather as a separate class of pricing models. Depending on the product and/or setting in focus, both are relevant for online retailing. Further research could investigate the performance of models with and without consideration of perishability in various (online) settings to determine when it is appropriate to use which class of pricing models. Also, an interesting field of future studies arises around the question which instruments (e.g., service differentiations or price diffusion) are used by online retailers to differentiate otherwise homogeneous offerings.

Time dependence

A key differentiator of competitive pricing models is the consideration of either a static (time-independent) setup with definite equilibrium or a dynamic (time-dependent) constellation with changing environmental factors and equilibria. Albeit static pricing models have no time component, many consist of multiple stages to investigate the interplay of different factors. Footnote 13 In contrast, dynamic models allow for varying competitive (re-)actions over time. Footnote 14 Within the latter category, there are models with a finite (Afeche et al. 2011 ; Levin et al. 2008 ; Liu and Zhang 2013 ; Yang and Xia 2013 ) and an infinite (Anderson and Kumar 2007 ; Li et al. 2017 ; Schlosser and Richly 2019 ; Villas-Boas and Winer 1999 ; Weintraub et al. 2008 ) time horizon.

Historically, competitive pricing models assumed fixed prices over the considered time horizon. Limited computational power made it impossible to appropriately estimate models dynamically due to dimensionality issues (Schlosser and Boissier 2018 ). A lack of reliable demand information, high menu and investment costs to implement dynamic approaches were additional reasons why pricing models remained inherently static without incorporating changing competitive responses (Ferreira et al. 2016 ). The focus in retail has conventionally rather been on long-term profit optimization and to a lesser degree on dynamically changing price optimizations (Elmaghraby and Keskinocak 2003 ).

The literature disagrees on whether firms should opt for static or dynamic pricing strategies. A static environment allows to simplify and concentrate on a specific topic such as equilibrium discussions. For instance, Lal and Rao ( 1997 ) study success factors of everyday low pricing and derive conditions for a perfect Nash equilibrium between an everyday low price retailer and a retailer with promotional pricing. With Zara as an example for a company with a successful static pricing strategy, Liu and Zhang ( 2013 ) argue that with the presence of strategic customers who prolong sales in anticipation of price decreases, firms might even be better off to deploy static over dynamic price setting processes. Studying the time-variant pricing plans in electricity markets, Schlereth et al. ( 2018 ) suggest that consumers might prefer static over dynamic pricing because of factors like choice confusion, lack of trust in price fairness, perceived economical risk or perceived additional effort. Further support for a static pricing strategy is found in Cachon and Feldman ( 2010 ) and Hall et al. ( 2009 ).

Nevertheless, to generalize that static should strictly be preferred over dynamic pricing models could be short-sighted. Firms cannot generally infer future behavior of competitors from past observations to assess how competitive (re-) actions may influence the optimal pricing policy (Boer 2015b ). Corresponding to the surge of revenue management systems in the airline industry during the 70s and 80s, increased price and demand transparency, low menu costs and an abundance of decision support software created fierce competition among online retailers (Fisher et al. 2018 ). Taking up on the above mentioned example by Liu and Zhang ( 2013 ), Caro and Gallien ( 2012 ) show that even Zara does not solely rely on static pricing. They supported Zara’s pricing team in designing and implementing a dynamic clearance pricing optimization system—to generate a competitive advantage in addition to the fast-fashion retail model Zara mainly pursues (Caro 2012 ). Zhang et al. ( 2017 ) discuss various duopoly pricing models with static and dynamic pricing under advertising. They find that market surplus is highest when one firm prices dynamically, profiting from the static behavior of the other. Chung et al. ( 2012 ) provide numerical evidence that a dynamic pricing model with an appropriately specified demand estimation always outperforms static pricing strategies—also in settings with incomplete information. Xu and Hopp ( 2006 ) show that dynamic pricing outperforms preannounced pricing, especially with effective inventory management and elastic demand. Further support for advantages of dynamic pricing can be found by Popescu ( 2015 ), Wang and Sun ( 2019 ), and Zhang et al. ( 2018b ). Empirical evidence of the negative consequences of sticking to a static strategy in a changing environment is found in the cases of Nokia, Kodak, and Xerox.

While some scholars distinguish between discrete and continuous dynamic pricing systems (Vinod 2020 ), we suggest to classify dynamic pricing models according to their level of sophistication into two evolutionary stages: the (in e-commerce widely applied) manual rule-based pricing approach and the data-driven algorithmic optimization approach (Popescu 2015 ; Le Chen et al. 2016 ). Footnote 15 For the rule-based approach, “if-then-else rules” are defined and updated manually. Footnote 16 However, the mere number of stock-keeping units (SKUs) in today’s retailer offerings aggravate the initial setup and handling of rule-based pricing and make real-time adjustments unmanageable (Schlosser and Boissier 2018 ). In addition, rule-based approaches are rather subjective than sufficiently data-driven. Faced with a large range of SKUs, competitor responses and heterogeneous demand elasticities, canceling out the human decision-making process on an operational level is the next evolutionary step for competitive pricing systems (Calvano et al. 2020 ). Data-driven algorithmic pricing strategies use observable market Footnote 17 data to predict sales probabilities based on consumer demand and competitive responses (Schlosser and Richly 2019 ).

As online marketplaces benefit from an increased number of retailers on their platforms, they typically support sellers to establish automated dynamic pricing systems (Kachani et al. 2010 ). Footnote 18 However, Schlosser and Richly ( 2019 ) claim that current dynamic pricing systems are not able to deal with the complexity of competitor-based pricing and therefore most often ignore competition altogether or solely rely on manually adjusted rule-based mechanics. Challenges include the indefinite spectrum of changing competitor strategies, asymmetric access to competitor knowledge, a large solution space under limited information and the black-box character of dynamic systems, which exacerbates an intervention in case of a pricing system malfunction. Besides, researchers did not yet identify an algorithm which consistently outperforms other methodologies in competitive situations. Instead, it depends on the specific setting and other competitors’ pricing behavior to assess which pricing algorithm is optimal (van de Geer et al. 2019 ) exacerbating the application of such systems.

Reflecting the literature findings for both static and dynamic pricing strategies, we conclude that pricing managers should develop dynamic pricing models in most e-commerce situations. As long as demand and competitor price responses vary over time on online markets, dynamic models are naturally superior to time-independent approaches. Static models on the other hand are only appropriate in market constellations with little time-varying demand and competitor behavior. As static research can be expected to remain a vivid field of literature, further research with regard to the transferability of static models to dynamic settings is desirable. In addition, more research is needed that helps to better understand the implications of widely applied rule-based dynamic pricing methods and their transition toward algorithmic approaches (Boer 2015a ; van de Geer et al. 2019 ; Kastius and Schlosser 2022 ; Könönen 2006 ).

Market structure

The market structure describes the number of competing firms such as duopoly or oligopoly in a demand setting with an indefinite number of consumers. 60% of the reviewed papers studied duopolies, 49% oligopolies, 7% monopolistic competition, and 3% perfect competition. Footnote 19

Especially for research in the economics stream, many papers assume a perfectly competitive market. Pricing research with perfectly competitive markets (e.g., van Mieghem and Dada 1999 or Yang and Xia 2013 ) is likely to be of very limited value to online retailers. Building on the notion of Diamond ( 1971 ), Salop ( 1976 ) argues that if customers have positive information gathering costs, no perfect competition can occur as firms have room to slightly increase prices without losing demand. Christen ( 2005 ) found evidence that even with strong competition and low information costs, cost uncertainty could decrease the detrimental effect of competition for sellers and could increase prices above Bertrand levels. Similarly, Bryant ( 1980 ) showed that perfect competition is not possible in a market with uncertain demand, even if the number of firms is large and customers have no search costs. Rather, price dispersion reflects uncertain demand (Borenstein and Rose Nancy L. 1994 ; Cavallo 2018 ; Clemons et al. 2002 ; Obermeyer et al. 2013 ; Wang et al. 2021 ). Israeli et al. ( 2022 ) empirically show that the market power of individual firms does not only depend on the number and intensity of competitors but also on the firm’s ability to adjust prices in response to varying inventory levels of product substitutes, especially with low consumer search costs. This is of relevance for e-commerce as e-tailers could exploit this dependence by incorporating competitors’ stock levels into pricing decisions (Fisher et al. 2018 ).

Some papers discuss (quasi) monopolistic competition (e.g., Xu and Hopp 2006 ) in which small firms charge the (higher) monopoly price rather than the (lower) competitive price. From an empirical study in the U.S. airline industry, Chen (2018) concludes that, as firms can price discriminate late-arriving consumers, competition is softened, profits are increased, and the only single-price equilibrium could be at the monopoly price. This supports Lal and Sarvary ( 1999 ) who show that online retailers enjoy a certain amount of monopoly power in cases where buyers cannot switch suppliers for repeated purchases (e.g., technical incompatibility reasons). In such cases, switching costs could increase online prices (Chen and Riordan 2008 ). However, this contradicts Deck and Gu ( 2012 ) who empirically show that, although the distribution of buyer values of competing products might theoretically lead to higher prices through competition, intensity of competition rarely allows for an occurrence of this phenomenon in e-tail settings.

Although duopoly settings can serve to assess the relevant strength of pricing strategies, which is not directly possible for oligopoly markets due to the curse of dimensionality (Kastius and Schlosser 2022 ), they cannot be transferred to more competitive environments (van de Geer et al. 2019 ). In online retail, a duopoly market structure is a rare exemption. Like for perfectly competitive markets, findings of duopoly research must be carefully assessed in terms of their applicability to online retail oligopolies.

Bresnahan and Reiss ( 1991 ) found empirical evidence that markets with an increasing number of dealers have lower prices than in less competitive market structures such as monopolies or duopolies. Although applicable to many online retail markets, where retailers face dozens, if not hundreds of thousands of competitors (Schlosser and Boissier 2018 ), few research attention is currently given toward a structure with a large number of competitors in an imperfect market (cf. Li et al. 2017 ). A way to assess the current competitive structure of markets is the utilization of online consumer-generated content such as forum entries (Netzer et al. 2012 ; Won et al. 2022 ) or clickstream data (Ringel and Skiera 2016 ) and actual sales data (Kim et al. 2011 ).

In many countries with well-developed B2C online markets, one or few major retailers dominate on an oligopolistic market. For example, the top three online retailers in the United States accounted for over 50% of the revenue generated on the national e-commerce market in 2021. Footnote 20 Due to lower locational limitations in conjunction with substantial economies of scale and scope, online markets tend to become more concentrated than their offline counterparts (Borsenberger 2015 ). Although one could expect that increased market transparency leads to a higher intensity of competition (Cao and Gruca 2003 ), limiting the market power of established firms and leaving growth potential for smaller firms (Zhao et al. 2017 ), it appears reasonable to predict that most online markets will ultimately resemble an oligopoly setting with a with a relatively small number of players—enabling increased tacit pricing algorithm collusion in the future (Calvano et al. 2020 ). With few exceptions (e.g., Noel 2007 ), there is little research (Brown and Goolsbee 2002 ; Wang et al. 2021 ; Cavallo 2018 ) exploring what type of competitor-based pricing strategies are used and what competitive dynamics are found on e-tail markets. Thus, more research is needed to investigate the current state of market structure and intensity of competition in today’s e-commerce markets as drivers of the selection and the outcomes of pricing approaches.

Implications and directions

We contribute to the literature by providing an interdisciplinary review of competitive online retail research. Competitive pricing problems can most often be assigned to one of the academic fields of economics, marketing management or operations. In a first step, this review offered a descriptive portrayal of the relevant literature. Motivated by practical issues and common features in competitive pricing research, we then structured competitive pricing contributions along four properties of pricing models. First, do firms compete with identical or quality differentiated products? Second, are products to be considered as perishable or durable goods? Third, is the market setting to be regarded time-independent or not? Fourth, which market structure prevails on e-tail markets? The framework is derived from an analysis of pricing research not exclusively restricted to online retail settings. Therefore, it could be extended to other online or offline markets, with little loss of generalizability.

We focused on e-tail markets because the relevance of competition for pricing strategies is disproportionally higher in such environments. On e-tail online markets, products are rarely offered exclusively so that the likelihood of substitutive competition is high. Nevertheless, products can be differentiated through other factors than prices such as generous shipping, customer retention (e.g., loyalty reward programs) or return and issue handling policies. With a look on product similarities, accounting for product interdependencies and multi-product situations are important improvements of prevailing pricing models. Second, pricing models with both a focus on perishable and/or durable products are relevant on e-tail markets. However, further research is needed exploring which of the respective perishability considerations are appropriate for different settings. Third, we conclude that, albeit time-independent static models may occasionally serve to simplify pricing issues, dynamic models outperform their static counterparts in constantly changing market environments such as in e-commerce. Fourth, we show that in most practical settings, online markets resemble either an oligopolistic market structure or a structure with many firms under imperfect competition. Thus, future research should consider these two “real” competitive settings instead of further looking at simplifying market structures such as monopolistic or duopolistic competition. This should ease a transfer of theoretical insights into practical applications. To sum, firms should be able to improve their competitive position by developing a profit optimizing dynamic pricing strategy for identical products in an oligopolistic setting with a varying number and relevance of competitors.

Due to space limitations, we had to focus on competitive pricing model characteristics related to four overall product and market attributes. Thus, more work is needed on other characteristics of competitive pricing models, particularly firm- and consumer-related characteristics. Firm-related characteristics encompass various additional properties of interacting firms (e.g., similarity or capacity constraints). Similarly, consumer-related characteristics entail further properties of interacting buyers (e.g., certainty, discreteness, sophistication, and homogeneity of demand).

In the selection process of literature, this study only considered papers in peer-reviewed journals and conference proceedings in English. Subsequent research could complement our findings by including industry-funded, unpublished and non-peer-reviewed articles, also in other languages. In addition, we do not claim that our research captures all competitive pricing publications of the considered field. As our study spans almost 50 years of a frequently discussed topic in the domains of economics, marketing management, and operations, we had to constrain the scope to the most influential work. Although we mutually evaluated our selection decisions and consulted outside peers for validation and further input, we cannot eliminate the element of subjectivity. Consequently, other authors could have selected slightly different papers. However, this shortcoming is unlikely to significantly affect our results as our literature selection was derived from a broad array of competitive pricing research and would therefore be only marginally influenced by a few omitted articles.

Competitive pricing includes all activities and processes to price products with the consideration of competitors. This does not only include rigidly pegging prices to competitor prices but rather a comprehensive consideration of current and expected price (re-)actions of competing firms to sustainably ensure profit maximization. In this article, the terms competitive pricing, competitor-oriented pricing and competitor-based pricing are used synonymously.

Search costs are defined as the costs of time and resources to acquire information with respect to price, assortment, and quality characteristics of the goods provided by different sellers. The internet dramatically reduces search costs through price comparison websites such as Google Shopping, Shopzilla (USA) or Idealo (Germany).

Business-to-consumer (B2C) online retail sales encompass all forms of electronic commerce markets in which residential end customers can directly buy goods from a seller over the internet through a web browser or a mobile app. In this paper, the terms business-to-consumer (B2C) online goods retail, online retail, e-tail, e-retail and e-commerce markets are used synonymously.

In contrast to classical quantity-based revenue management, dynamic pricing, also known as surge pricing, is the practice of adjusting prices according to current market demand (Boer 2015a ). Revenue management, also known as yield management, is a type of price discrimination which originates from the airline and hospitality industries. Typically, revenue management models assume fixed capacities, low marginal cost, varying demand and highly perishable inventory (Talluri and van Ryzin 2004 ).

Keywords used for abstract, title and keyword screening were “competitive pricing”, “competitor-based pricing”, “competition” AND “pricing”. To find literature for online pricing in particular, the search was combined with the keywords “online”, “e-retail”, “ecommerce” and “e-commerce”. Whereas the combination was scanned in great depths, the three competitive keywords were screened for influential papers with implications for online markets.

Bertrand competition is a simplified model of competition to explain price competition among (at least) two firms for an identical product at equal unit cost of production. Prices are set simultaneously, and consumers buy without search costs from the firm with the lowest price. When all firms charge the same price, consumer demand is split evenly between firms. A firm is willing to supply unlimited amounts of quantities above the unit cost of production and is indifferent to supply at unit cost as it will earn zero profit. The only Bertrand equilibrium exists when prices are equal to unit cost (i.e., competitive price) as each firm otherwise would have an incentive to undercut all other competitors and thereby rake in the entire market demand. Therefore, there can be no equilibrium at prices above the competitive price and price dispersion cannot occur.

In vertical differentiation, consumer choice depends on specific quality levels of product attributes. At the same price, all consumers prefer one product over other products, for example because of superior design. In the simplest form, products differ in one attribute and customers are willing to pay marginal increments of this attribute.

In horizontal differentiation, consumer choice depends on preferences for products. At the same price, some customers would buy one product and others other products.

We consider product differentiation only to product-related differentiation attributes. However, in competitive pricing literature firm-related differences such as firm loyalty or distribution channels are occasionally attributed to differentiation. For example, Abhishek et al. ( 2016 ) differentiate online distribution channels of otherwise homogeneous products and firms.

Heterogeneous customer preferences are a key requirement for product differentiation, otherwise price constitutes the only driver of the buying decision (Li et al. 2017 ). Without heterogeneity in consumers’ marginal willingness to pay for different levels of product quality, there can be no product differentiation (Pigou 1920 ).

Accessed 14–03-2022.

Salvage value is defined as the residual cash-flow of a good after its expiration date.

For example, game settings on the foundation of Stackelberg games necessarily comprise ≥ 2 stages (Geng and Mallik 2007 ; Gupta et al.; Wang et al. 2020 ; Yao and Liu 2005 ). Another example would be Anand and Girotra ( 2007 ) who propose a 3-stage model in which they include the supply chain configuration and determination of production quantities in addition to the actual price setting.

As such, we classify n-stage models as dynamic models when not all individual stages serve a specific time-independent purpose.

For instance, the Brandenburg consumer advice center (Verbraucherzentrale Brandenburg) examined dynamic price differentiation in online retail and found that 15 of the 16 observed German online shops dynamically changed their prices in 2018 (Dautzenberg et al. 2018 ).

A typical rule would be to set prices always x% lower than competitor prices up to a certain profit threshold.

Observable market data include price and stock levels of competitors (Fisher et al. 2018 ) or clickstream and keyword data of customers (Li et al. 2017 ).

Examples for support programs by online marketplaces are Amazon’s Seller Central ( https://sellercentral.amazon.com/gp/help/external/G201994820?language=en_US&ref=efph_G201994820_cont_43381 ; Accessed 14–03-2022), eBay’s Seller Tools ( https://pages.ebay.com/sell/automation.html ; Accessed 14–03-2022) or Idealo’s Partner Program ( https://partner.idealo.com/de ; Accessed 14–03-2022).

Cumulatively, these values exceed 100% as some articles discussed more than one kind of market structure. Applied by economists to simplify real markets as the foundation of price theory, perfect competition relates to a market structure which is controlled entirely by market forces and not by individual firms. Instead, individual firms only act as price takers and cannot earn any economic profit. The conditions for a perfect competition, such as full information, homogeneous products, fully rational buyers, no scale, network or externality effects, no entry barriers, and no transaction costs, are rarely attainable in practical settings (Stigler 1957 ). If not all conditions for perfect competition are fulfilled, the market structure is imperfect which applies to most practical settings. Besides a monopoly with only one seller on the market, three market structures with competing firms exist: Monopolistic, duopolistic, and oligopolistic competition. An oligopoly is characterized by a small number of firms in which the behavior of one firm drives the actions of other firms. A duopoly is a particular case of an oligopoly in which two firms control the market. An extreme case of imperfect competition is (quasi) monopolistic competition in which products are differentiated and firms maintain a certain spare capacity giving them a certain degree of pricing power to maximize their (short-term) profits. In consequence, prices can be higher than corresponding the competitive (Bertrand) price (Vives 2001 ).

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Influencer advertising has emerged as an integral part of social media marketing. Within this realm, consumer engagement is a critical indicator for gauging the impact of influencer advertisements, as it encompasses the proactive involvement of consumers in spreading advertisements and creating value. Therefore, investigating the mechanisms behind consumer engagement holds significant relevance for formulating effective influencer advertising strategies. The current study, grounded in self-determination theory and employing a stimulus-organism-response framework, constructs a general model to assess the impact of influencer factors, advertisement information, and social factors on consumer engagement. Analyzing data from 522 samples using structural equation modeling, the findings reveal: (1) Social media influencers are effective at generating initial online traffic but have limited influence on deeper levels of consumer engagement, cautioning advertisers against overestimating their impact; (2) The essence of higher-level engagement lies in the ad information factor, affirming that in the new media era, content remains ‘king’; (3) Interpersonal factors should also be given importance, as influencing the surrounding social groups of consumers is one of the effective ways to enhance the impact of advertising. Theoretically, current research broadens the scope of both social media and advertising effectiveness studies, forming a bridge between influencer marketing and consumer engagement. Practically, the findings offer macro-level strategic insights for influencer marketing.

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Introduction.

Recent studies have highlighted an escalating aversion among audiences towards traditional online ads, leading to a diminishing effectiveness of traditional online advertising methods (Lou et al., 2019 ). In an effort to overcome these challenges, an increasing number of brands are turning to influencers as their spokespersons for advertising. Utilizing influencers not only capitalizes on their significant influence over their fan base but also allows for the dissemination of advertising messages in a more native and organic manner. Consequently, influencer-endorsed advertising has become a pivotal component and a growing trend in social media advertising (Gräve & Bartsch, 2022 ). Although the topic of influencer-endorsed advertising has garnered increasing attention from scholars, the field is still in its infancy, offering ample opportunities for in-depth research and exploration (Barta et al., 2023 ).

Presently, social media influencers—individuals with substantial follower bases—have emerged as the new vanguard in advertising (Hudders & Lou, 2023 ). Their tweets and videos possess the remarkable potential to sway the purchasing decisions of thousands if not millions. This influence largely hinges on consumer engagement behaviors, implying that the impact of advertising can proliferate throughout a consumer’s entire social network (Abbasi et al., 2023 ). Consequently, exploring ways to enhance consumer engagement is of paramount theoretical and practical significance for advertising effectiveness research (Xiao et al., 2023 ). This necessitates researchers to delve deeper into the exploration of the stimulating factors and psychological mechanisms influencing consumer engagement behaviors (Vander Schee et al., 2020 ), which is the gap this study seeks to address.

The Stimulus-Organism-Response (S-O-R) framework has been extensively applied in the study of consumer engagement behaviors (Tak & Gupta, 2021 ) and has been shown to integrate effectively with self-determination theory (Yang et al., 2019 ). Therefore, employing the S-O-R framework to investigate consumer engagement behaviors in the context of influencer advertising is considered a rational approach. The current study embarks on an in-depth analysis of the transformation process from three distinct dimensions. In the Stimulus (S) phase, we focus on how influencer factors, advertising message factors, and social influence factors act as external stimuli. This phase scrutinizes the external environment’s role in triggering consumer reactions. During the Organism (O) phase, the research explores the intrinsic psychological motivations affecting individual behavior as posited in self-determination theory. This includes the willingness for self-disclosure, the desire for innovation, and trust in advertising messages. The investigation in this phase aims to understand how these internal motivations shape consumer attitudes and perceptions in the context of influencer marketing. Finally, in the Response (R) phase, the study examines how these psychological factors influence consumer engagement behavior. This part of the research seeks to understand the transition from internal psychological states to actual consumer behavior, particularly how these states drive the consumers’ deep integration and interaction with the influencer content.

Despite the inherent limitations of cross-sectional analysis in capturing the full temporal dynamics of consumer engagement, this study seeks to unveil the dynamic interplay between consumers’ psychological needs—autonomy, competence, and relatedness—and their varying engagement levels in social media influencer marketing, grounded in self-determination theory. Through this lens, by analyzing factors related to influencers, content, and social context, we aim to infer potential dynamic shifts in engagement behaviors as psychological needs evolve. This approach allows us to offer a snapshot of the complex, multi-dimensional nature of consumer engagement dynamics, providing valuable insights for both theoretical exploration and practical application in the constantly evolving domain of social media marketing. Moreover, the current study underscores the significance of adapting to the dynamic digital environment and highlights the evolving nature of consumer engagement in the realm of digital marketing.

Literature review

Stimulus-organism-response (s-o-r) model.

The Stimulus-Response (S-R) model, originating from behaviorist psychology and introduced by psychologist Watson ( 1917 ), posits that individual behaviors are directly induced by external environmental stimuli. However, this model overlooks internal personal factors, complicating the explanation of psychological states. Mehrabian and Russell ( 1974 ) expanded this by incorporating the individual’s cognitive component (organism) into the model, creating the Stimulus-Organism-Response (S-O-R) framework. This model has become a crucial theoretical framework in consumer psychology as it interprets internal psychological cognitions as mediators between stimuli and responses. Integrating with psychological theories, the S-O-R model effectively analyzes and explains the significant impact of internal psychological factors on behavior (Koay et al., 2020 ; Zhang et al., 2021 ), and is extensively applied in investigating user behavior on social media platforms (Hewei & Youngsook, 2022 ). This study combines the S-O-R framework with self-determination theory to examine consumer engagement behaviors in the context of social media influencer advertising, a logic also supported by some studies (Yang et al., 2021 ).

Self-determination theory

Self-determination theory, proposed by Richard and Edward (2000), is a theoretical framework exploring human behavioral motivation and personality. The theory emphasizes motivational processes, positing that individual behaviors are developed based on factors satisfying their psychological needs. It suggests that individual behavioral tendencies are influenced by the needs for competence, relatedness, and autonomy. Furthermore, self-determination theory, along with organic integration theory, indicates that individual behavioral tendencies are also affected by internal psychological motivations and external situational factors.

Self-determination theory has been validated by scholars in the study of online user behaviors. For example, Sweet applied the theory to the investigation of community building in online networks, analyzing knowledge-sharing behaviors among online community members (Sweet et al., 2020 ). Further literature review reveals the applicability of self-determination theory to consumer engagement behaviors, particularly in the context of influencer marketing advertisements. Firstly, self-determination theory is widely applied in studying the psychological motivations behind online behaviors, suggesting that the internal and external motivations outlined within the theory might also apply to exploring consumer behaviors in influencer marketing scenarios (Itani et al., 2022 ). Secondly, although research on consumer engagement in the social media influencer advertising context is still in its early stages, some studies have utilized SDT to explore behaviors such as information sharing and electronic word-of-mouth dissemination (Astuti & Hariyawan, 2021 ). These behaviors, which are part of the content contribution and creation dimensions of consumer engagement, may share similarities in the underlying psychological motivational mechanisms. Thus, this study will build upon these foundations to construct the Organism (O) component of the S-O-R model, integrating insights from SDT to further understand consumer engagement in influencer marketing.

Consumer engagement

Although scholars generally agree at a macro level to define consumer engagement as the creation of additional value by consumers or customers beyond purchasing products, the specific categorization of consumer engagement varies in different studies. For instance, Simon and Tossan interpret consumer engagement as a psychological willingness to interact with influencers (Simon & Tossan, 2018 ). However, such a broad definition lacks precision in describing various levels of engagement. Other scholars directly use tangible metrics on social media platforms, such as likes, saves, comments, and shares, to represent consumer engagement (Lee et al., 2018 ). While this quantitative approach is not flawed and can be highly effective in practical applications, it overlooks the content aspect of engagement, contradicting the “content is king” principle of advertising and marketing. We advocate for combining consumer engagement with the content aspect, as content engagement not only generates more traces of consumer online behavior (Oestreicher-Singer & Zalmanson, 2013 ) but, more importantly, content contribution and creation are central to social media advertising and marketing, going beyond mere content consumption (Qiu & Kumar, 2017 ). Meanwhile, we also need to emphasize that engagement is not a fixed state but a fluctuating process influenced by ongoing interactions between consumers and influencers, mediated by the evolving nature of social media platforms and the shifting sands of consumer preferences (Pradhan et al., 2023 ). Consumer engagement in digital environments undergoes continuous change, reflecting a journey rather than a destination (Viswanathan et al., 2017 ).

The current study adopts a widely accepted definition of consumer engagement from existing research, offering operational feasibility and aligning well with the research objectives of this paper. Consumer engagement behaviors in the context of this study encompass three dimensions: content consumption, content contribution, and content creation (Muntinga et al., 2011 ). These dimensions reflect a spectrum of digital engagement behaviors ranging from low to high levels (Schivinski et al., 2016 ). Specifically, content consumption on social media platforms represents a lower level of engagement, where consumers merely click and read the information but do not actively contribute or create user-generated content. Some studies consider this level of engagement as less significant for in-depth exploration because content consumption, compared to other forms, generates fewer visible traces of consumer behavior (Brodie et al., 2013 ). Even in a study by Qiu and Kumar, it was noted that the conversion rate of content consumption is low, contributing minimally to the success of social media marketing (Qiu & Kumar, 2017 ).

On the other hand, content contribution, especially content creation, is central to social media marketing. When consumers comment on influencer content or share information with their network nodes, it is termed content contribution, representing a medium level of online consumer engagement (Piehler et al., 2019 ). Furthermore, when consumers actively upload and post brand-related content on social media, this higher level of behavior is referred to as content creation. Content creation represents the highest level of consumer engagement (Cheung et al., 2021 ). Although medium and high levels of consumer engagement are more valuable for social media advertising and marketing, this exploratory study still retains the content consumption dimension of consumer engagement behaviors.

Theoretical framework

Internal organism factors: self-disclosure willingness, innovativeness, and information trust.

In existing research based on self-determination theory that focuses on online behavior, competence, relatedness, and autonomy are commonly considered as internal factors influencing users’ online behaviors. However, this approach sometimes strays from the context of online consumption. Therefore, in studies related to online consumption, scholars often use self-disclosure willingness as an overt representation of autonomy, innovativeness as a representation of competence, and trust as a representation of relatedness (Mahmood et al., 2019 ).

The use of these overt variables can be logically explained as follows: According to self-determination theory, individuals with a higher level of self-determination are more likely to adopt compensatory mechanisms to facilitate behavior compared to those with lower self-determination (Wehmeyer, 1999 ). Self-disclosure, a voluntary act of sharing personal information with others, is considered a key behavior in the development of interpersonal relationships. In social environments, self-disclosure can effectively alleviate stress and build social connections, while also seeking societal validation of personal ideas (Altman & Taylor, 1973 ). Social networks, as para-social entities, possess the interactive attributes of real societies and are likely to exhibit similar mechanisms. In consumer contexts, personal disclosures can include voluntary sharing of product interests, consumption experiences, and future purchase intentions (Robertshaw & Marr, 2006 ). While material incentives can prompt personal information disclosure, many consumers disclose personal information online voluntarily, which can be traced back to an intrinsic need for autonomy (Stutzman et al., 2011 ). Thus, in this study, we consider the self-disclosure willingness as a representation of high autonomy.

Innovativeness refers to an individual’s internal level of seeking novelty and represents their personality and tendency for novelty (Okazaki, 2009 ). Often used in consumer research, innovative consumers are inclined to try new technologies and possess an intrinsic motivation to use new products. Previous studies have shown that consumers with high innovativeness are more likely to search for information on new products and share their experiences and expertise with others, reflecting a recognition of their own competence (Kaushik & Rahman, 2014 ). Therefore, in consumer contexts, innovativeness is often regarded as the competence dimension within the intrinsic factors of self-determination (Wang et al., 2016 ), with external motivations like information novelty enhancing this intrinsic motivation (Lee et al., 2015 ).

Trust refers to an individual’s willingness to rely on the opinions of others they believe in. From a social psychological perspective, trust indicates the willingness to assume the risk of being harmed by another party (McAllister, 1995 ). Widely applied in social media contexts for relational marketing, information trust has been proven to positively influence the exchange and dissemination of consumer information, representing a close and advanced relationship between consumers and businesses, brands, or advertising endorsers (Steinhoff et al., 2019 ). Consumers who trust brands or social media influencers are more willing to share information without fear of exploitation (Pop et al., 2022 ), making trust a commonly used representation of the relatedness dimension in self-determination within consumer contexts.

Construction of the path from organism to response: self-determination internal factors and consumer engagement behavior

Following the logic outlined above, the current study represents the internal factors of self-determination theory through three variables: self-disclosure willingness, innovativeness, and information trust. Next, the study explores the association between these self-determination internal factors and consumer engagement behavior, thereby constructing the link between Organism (O) and Response (R).

Self-disclosure willingness and consumer engagement behavior

In the realm of social sciences, the concept of self-disclosure willingness has been thoroughly examined from diverse disciplinary perspectives, encompassing communication studies, sociology, and psychology. Viewing from the lens of social interaction dynamics, self-disclosure is acknowledged as a fundamental precondition for the initiation and development of online social relationships and interactive engagements (Luo & Hancock, 2020 ). It constitutes an indispensable component within the spectrum of interactive behaviors and the evolution of interpersonal connections. Voluntary self-disclosure is characterized by individuals divulging information about themselves, which typically remains unknown to others and is inaccessible through alternative sources. This concept aligns with the tenets of uncertainty reduction theory, which argues that during interpersonal engagements, individuals seek information about their counterparts as a means to mitigate uncertainties inherent in social interactions (Lee et al., 2008 ). Self-disclosure allows others to gain more personal information, thereby helping to reduce the uncertainty in interpersonal relationships. Such disclosure is voluntary rather than coerced, and this sharing of information can facilitate the development of relationships between individuals (Towner et al., 2022 ). Furthermore, individuals who actively engage in social media interactions (such as liking, sharing, and commenting on others’ content) often exhibit higher levels of self-disclosure (Chu et al., 2023 ); additional research indicates a positive correlation between self-disclosure and online engagement behaviors (Lee et al., 2023 ). Taking the context of the current study, the autonomous self-disclosure willingness can incline social media users to read advertising content more attentively and share information with others, and even create evaluative content. Therefore, this paper proposes the following research hypothesis:

H1a: The self-disclosure willingness is positively correlated with content consumption in consumer engagement behavior.

H1b: The self-disclosure willingness is positively correlated with content contribution in consumer engagement behavior.

H1c: The self-disclosure willingness is positively correlated with content creation in consumer engagement behavior.

Innovativeness and consumer engagement behavior

Innovativeness represents an individual’s propensity to favor new technologies and the motivation to use new products, associated with the cognitive perception of one’s self-competence. Individuals with a need for self-competence recognition often exhibit higher innovativeness (Kelley & Alden, 2016 ). Existing research indicates that users with higher levels of innovativeness are more inclined to accept new product information and share their experiences and discoveries with others in their social networks (Yusuf & Busalim, 2018 ). Similarly, in the context of this study, individuals, as followers of influencers, signify an endorsement of the influencer. Driven by innovativeness, they may be more eager to actively receive information from influencers. If they find the information valuable, they are likely to share it and even engage in active content re-creation to meet their expectations of self-image. Therefore, this paper proposes the following research hypotheses:

H2a: The innovativeness of social media users is positively correlated with content consumption in consumer engagement behavior.

H2b: The innovativeness of social media users is positively correlated with content contribution in consumer engagement behavior.

H2c: The innovativeness of social media users is positively correlated with content creation in consumer engagement behavior.

Information trust and consumer engagement

Trust refers to an individual’s willingness to rely on the statements and opinions of a target object (Moorman et al., 1993 ). Extensive research indicates that trust positively impacts information dissemination and content sharing in interpersonal communication environments (Majerczak & Strzelecki, 2022 ); when trust is established, individuals are more willing to share their resources and less suspicious of being exploited. Trust has also been shown to influence consumers’ participation in community building and content sharing on social media, demonstrating cross-cultural universality (Anaya-Sánchez et al., 2020 ).

Trust in influencer advertising information is also a key predictor of consumers’ information exchange online. With many social media users now operating under real-name policies, there is an increased inclination to trust information shared on social media over that posted by corporate accounts or anonymously. Additionally, as users’ social networks partially overlap with their real-life interpersonal networks, extensive research shows that more consumers increasingly rely on information posted and shared on social networks when making purchase decisions (Wang et al., 2016 ). This aligns with the effectiveness goals of influencer marketing advertisements and the characteristics of consumer engagement. Trust in the content posted by influencers is considered a manifestation of a strong relationship between fans and influencers, central to relationship marketing (Kim & Kim, 2021 ). Based on trust in the influencer, which then extends to trust in their content, people are more inclined to browse information posted by influencers, share this information with others, and even create their own content without fear of exploitation or negative consequences. Therefore, this paper proposes the following research hypotheses:

H3a: Information trust is positively correlated with content consumption in consumer engagement behavior.

H3b: Information trust is positively correlated with content contribution in consumer engagement behavior.

H3c: Information trust is positively correlated with content creation in consumer engagement behavior.

Construction of the path from stimulus to organism: influencer factors, advertising information factors, social factors, and self-determination internal factors

Having established the logical connection from Organism (O) to Response (R), we further construct the influence path from Stimulus (S) to Organism (O). Revisiting the definition of influencer advertising in social media, companies, and brands leverage influencers on social media platforms to disseminate advertising content, utilizing the influencers’ relationships and influence over consumers for marketing purposes. In addition to consumer’s internal factors, elements such as companies, brands, influencers, and the advertisements themselves also impact consumer behavior. Although factors like the brand image perception of companies may influence consumer behavior, considering that in influencer marketing, companies and brands do not directly interact with consumers, this study prioritizes the dimensions of influencers and advertisements. Furthermore, the impact of social factors on individual cognition and behavior is significant, thus, the current study integrates influencers, advertisements, and social dimensions as the Stimulus (S) component.

Influencer factors: parasocial identification

Self-determination theory posits that relationships are one of the key motivators influencing individual behavior. In the context of social media research, users anticipate establishing a parasocial relationship with influencers, resembling real-life relationships. Hence, we consider the parasocial identification arising from users’ parasocial interactions with influencers as the relational motivator. Parasocial interaction refers to the one-sided personal relationship that individuals develop with media characters (Donald & Richard, 1956 ). During this process, individuals believe that the media character is directly communicating with them, creating a sense of positive intimacy (Giles, 2002 ). Over time, through repeated unilateral interactions with media characters, individuals develop a parasocial relationship, leading to parasocial identification. However, parasocial identification should not be directly equated with the concept of social identification in social identity theory. Social identification occurs when individuals psychologically de-individualize themselves, perceiving the characteristics of their social group as their own, upon identifying themselves as part of that group. In contrast, parasocial identification refers to the one-sided interactional identification with media characters (such as celebrities or influencers) over time (Chen et al., 2021 ). Particularly when individuals’ needs for interpersonal interaction are not met in their daily lives, they turn to parasocial interactions to fulfill these needs (Shan et al., 2020 ). Especially on social media, which is characterized by its high visibility and interactivity, users can easily develop a strong parasocial identification with the influencers they follow (Wei et al., 2022 ).

Parasocial identification and self-disclosure willingness

Theories like uncertainty reduction, personal construct, and social exchange are often applied to explain the emergence of parasocial identification. Social media, with its convenient and interactive modes of information dissemination, enables consumers to easily follow influencers on media platforms. They can perceive the personality of influencers through their online content, viewing them as familiar individuals or even friends. Once parasocial identification develops, this pleasurable experience can significantly influence consumers’ cognitions and thus their behavioral responses. Research has explored the impact of parasocial identification on consumer behavior. For instance, Bond et al. found that on Twitter, the intensity of users’ parasocial identification with influencers positively correlates with their continuous monitoring of these influencers’ activities (Bond, 2016 ). Analogous to real life, where we tend to pay more attention to our friends in our social networks, a similar phenomenon occurs in the relationship between consumers and brands. This type of parasocial identification not only makes consumers willing to follow brand pages but also more inclined to voluntarily provide personal information (Chen et al., 2021 ). Based on this logic, we speculate that a similar relationship may exist between social media influencers and their fans. Fans develop parasocial identification with influencers through social media interactions, making them more willing to disclose their information, opinions, and views in the comment sections of the influencers they follow, engaging in more frequent social interactions (Chung & Cho, 2017 ), even if the content at times may be brand or company-embedded marketing advertisements. In other words, in the presence of influencers with whom they have established parasocial relationships, they are more inclined to disclose personal information, thereby promoting consumer engagement behavior. Therefore, we propose the following research hypotheses:

H4: Parasocial identification is positively correlated with consumer self-disclosure willingness.

H4a: Self-disclosure willingness mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H4b: Self-disclosure willingness mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H4c: Self-disclosure willingness mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Parasocial identification and information trust

Information Trust refers to consumers’ willingness to trust the information contained in advertisements and to place themselves at risk. These risks include purchasing products inconsistent with the advertised information and the negative social consequences of erroneously spreading this information to others, leading to unpleasant consumption experiences (Minton, 2015 ). In advertising marketing, gaining consumers’ trust in advertising information is crucial. In the context of influencer marketing on social media, companies, and brands leverage the social connection between influencers and their fans. According to cognitive empathy theory, consumers project their trust in influencers onto the products endorsed, explaining the phenomenon of ‘loving the house for the crow on its roof.’ Research indicates that parasocial identification with influencers is a necessary condition for trust development. Consumers engage in parasocial interactions with influencers on social media, leading to parasocial identification (Jin et al., 2021 ). Consumers tend to reduce their cognitive load and simplify their decision-making processes, thus naturally adopting a positive attitude and trust towards advertising information disseminated by influencers with whom they have established parasocial identification. This forms the core logic behind the success of influencer marketing advertisements (Breves et al., 2021 ); furthermore, as mentioned earlier, because consumers trust these advertisements, they are also willing to share this information with friends and family and even engage in content re-creation. Therefore, we propose the following research hypotheses:

H5: Parasocial identification is positively correlated with information trust.

H5a: Information trust mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H5b: Information trust mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H5c: Information trust mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Influencer factors: source credibility

Source credibility refers to the degree of trust consumers place in the influencer as a source, based on the influencer’s reliability and expertise. Numerous studies have validated the effectiveness of the endorsement effect in advertising (Schouten et al., 2021 ). The Source Credibility Model, proposed by the renowned American communication scholar Hovland and the “Yale School,” posits that in the process of information dissemination, the credibility of the source can influence the audience’s decision to accept the information. The credibility of the information is determined by two aspects of the source: reliability and expertise. Reliability refers to the audience’s trust in the “communicator’s objective and honest approach to providing information,” while expertise refers to the audience’s trust in the “communicator being perceived as an effective source of information” (Hovland et al., 1953 ). Hovland’s definitions reveal that the interpretation of source credibility is not about the inherent traits of the source itself but rather the audience’s perception of the source (Jang et al., 2021 ). This differs from trust and serves as a precursor to the development of trust. Specifically, reliability and expertise are based on the audience’s perception; thus, this aligns closely with the audience’s perception of influencers (Kim & Kim, 2021 ). This credibility is a cognitive statement about the source of information.

Source credibility and self-disclosure willingness

Some studies have confirmed the positive impact of an influencer’s self-disclosure on their credibility as a source (Leite & Baptista, 2022 ). However, few have explored the impact of an influencer’s credibility, as a source, on consumers’ self-disclosure willingness. Undoubtedly, an impact exists; self-disclosure is considered a method to attempt to increase intimacy with others (Leite et al., 2022 ). According to social exchange theory, people promote relationships through the exchange of information in interpersonal communication to gain benefits (Cropanzano & Mitchell, 2005 ). Credibility, deriving from an influencer’s expertise and reliability, means that a highly credible influencer may provide more valuable information to consumers. Therefore, based on the social exchange theory’s logic of reciprocal benefits, consumers might be more willing to disclose their information to trustworthy influencers, potentially even expanding social interactions through further consumer engagement behaviors. Thus, we propose the following research hypotheses:

H6: Source credibility is positively correlated with self-disclosure willingness.

H6a: Self-disclosure willingness mediates the impact of Source credibility on content consumption in consumer engagement behavior.

H6b: Self-disclosure willingness mediates the impact of Source credibility on content contribution in consumer engagement behavior.

H6c: Self-disclosure willingness mediates the impact of Source credibility on content creation in consumer engagement behavior.

Source credibility and information trust

Based on the Source Credibility Model, the credibility of an endorser as an information source can significantly influence consumers’ acceptance of the information (Shan et al., 2020 ). Existing research has demonstrated the positive impact of source credibility on consumers. Djafarova, in a study based on Instagram, noted through in-depth interviews with 18 users that an influencer’s credibility significantly affects respondents’ trust in the information they post. This credibility is composed of expertise and relevance to consumers, and influencers on social media are considered more trustworthy than traditional celebrities (Djafarova & Rushworth, 2017 ). Subsequently, Bao and colleagues validated in the Chinese consumer context, based on the ELM model and commitment-trust theory, that the credibility of brand pages on Weibo effectively fosters consumer trust in the brand, encouraging participation in marketing activities (Bao & Wang, 2021 ). Moreover, Hsieh et al. found that in e-commerce contexts, the credibility of the source is a significant factor influencing consumers’ trust in advertising information (Hsieh & Li, 2020 ). In summary, existing research has proven that the credibility of the source can promote consumer trust. Influencer credibility is a significant antecedent affecting consumers’ trust in the advertised content they publish. In brand communities, trust can foster consumer engagement behaviors (Habibi et al., 2014 ). Specifically, consumers are more likely to trust the advertising content published by influencers with higher credibility (more expertise and reliability), and as previously mentioned, consumer engagement behavior is more likely to occur. Based on this, the study proposes the following research hypotheses:

H7: Source credibility is positively correlated with information trust.

H7a: Information trust mediates the impact of source credibility on content consumption in consumer engagement behavior.

H7b: Information trust mediates the impact of source credibility on content contribution in consumer engagement behavior.

H7c: Information trust mediates the impact of source credibility on content creation in consumer engagement behavior.

Advertising information factors: informative value

Advertising value refers to “the relative utility value of advertising information to consumers and is a subjective evaluation by consumers.” In his research, Ducoffe pointed out that in the context of online advertising, the informative value of advertising is a significant component of advertising value (Ducoffe, 1995 ). Subsequent studies have proven that consumers’ perception of advertising value can effectively promote their behavioral response to advertisements (Van-Tien Dao et al., 2014 ). Informative value of advertising refers to “the information about products needed by consumers provided by the advertisement and its ability to enhance consumer purchase satisfaction.” From the perspective of information dissemination, valuable advertising information should help consumers make better purchasing decisions and reduce the effort spent searching for product information. The informational aspect of advertising has been proven to effectively influence consumers’ cognition and, in turn, their behavior (Haida & Rahim, 2015 ).

Informative value and innovativeness

As previously discussed, consumers’ innovativeness refers to their psychological trait of favoring new things. Studies have shown that consumers with high innovativeness prefer novel and valuable product information, as it satisfies their need for newness and information about new products, making it an important factor in social media advertising engagement (Shi, 2018 ). This paper also hypothesizes that advertisements with high informative value can activate consumers’ innovativeness, as the novelty of information is one of the measures of informative value (León et al., 2009 ). Acquiring valuable information can make individuals feel good about themselves and fulfill their perception of a “novel image.” According to social exchange theory, consumers can gain social capital in interpersonal interactions (such as social recognition) by sharing information about these new products they perceive as valuable. Therefore, the current study proposes the following research hypothesis:

H8: Informative value is positively correlated with innovativeness.

H8a: Innovativeness mediates the impact of informative value on content consumption in consumer engagement behavior.

H8b: Innovativeness mediates the impact of informative value on content contribution in consumer engagement behavior.

H8c: Innovativeness mediates the impact of informative value on content creation in consumer engagement behavior.

Informative value and information trust

Trust is a multi-layered concept explored across various disciplines, including communication, marketing, sociology, and psychology. For the purposes of this paper, a deep analysis of different levels of trust is not undertaken. Here, trust specifically refers to the trust in influencer advertising information within the context of social media marketing, denoting consumers’ belief in and reliance on the advertising information endorsed by influencers. Racherla et al. investigated the factors influencing consumers’ trust in online reviews, suggesting that information quality and value contribute to increasing trust (Racherla et al., 2012 ). Similarly, Luo and Yuan, in a study based on social media marketing, also confirmed that the value of advertising information posted on brand pages can foster consumer trust in the content (Lou & Yuan, 2019 ). Therefore, by analogy, this paper posits that the informative value of influencer-endorsed advertising can also promote consumer trust in that advertising information. The relationship between trust in advertising information and consumer engagement behavior has been discussed earlier. Thus, the current study proposes the following research hypotheses:

H9: Informative value is positively correlated with information trust.

H9a: Information trust mediates the impact of informative value on content consumption in consumer engagement behavior.

H9b: Information trust mediates the impact of informative value on content contribution in consumer engagement behavior.

H9c: Information trust mediates the impact of informative value on content creation in consumer engagement behavior.

Advertising information factors: ad targeting accuracy

Ad targeting accuracy refers to the degree of match between the substantive information contained in advertising content and consumer needs. Advertisements containing precise information often yield good advertising outcomes. In marketing practice, advertisers frequently use information technology to analyze the characteristics of different consumer groups in the target market and then target their advertisements accordingly to achieve precise dissemination and, consequently, effective advertising results. The utility of ad targeting accuracy has been confirmed by many studies. For instance, in the research by Qiu and Chen, using a modified UTAUT model, it was demonstrated that the accuracy of advertising effectively promotes consumer acceptance of advertisements in WeChat Moments (Qiu & Chen, 2018 ). Although some studies on targeted advertising also indicate that overly precise ads may raise concerns about personal privacy (Zhang et al., 2019 ), overall, the accuracy of advertising information is effective in enhancing advertising outcomes and is a key element in the success of targeted advertising.

Ad targeting accuracy and information trust

In influencer marketing advertisements, due to the special relationship recognition between consumers and influencers, the privacy concerns associated with ad targeting accuracy are alleviated (Vrontis et al., 2021 ). Meanwhile, the informative value brought by targeting accuracy is highlighted. More precise advertising content implies higher informative value and also signifies that the advertising content is more worthy of consumer trust (Della Vigna, Gentzkow, 2010 ). As previously discussed, people are more inclined to read and engage with advertising content they trust and recognize. Therefore, the current study proposes the following research hypotheses:

H10: Ad targeting accuracy is positively correlated with information trust.

H10a: Information trust mediates the impact of ad targeting accuracy on content consumption in consumer engagement behavior.

H10b: Information trust mediates the impact of ad targeting accuracy on content contribution in consumer engagement behavior.

H10c: Information trust mediates the impact of ad targeting accuracy on content creation in consumer engagement behavior.

Social factors: subjective norm

The Theory of Planned Behavior, proposed by Ajzen ( 1991 ), suggests that individuals’ actions are preceded by conscious choices and are underlain by plans. TPB has been widely used by scholars in studying personal online behaviors, these studies collectively validate the applicability of TPB in the context of social media for researching online behaviors (Huang, 2023 ). Additionally, the self-determination theory, which underpins this chapter’s research, also supports the notion that individuals’ behavioral decisions are based on internal cognitions, aligning with TPB’s assertions. Therefore, this paper intends to select subjective norms from TPB as a factor of social influence. Subjective norm refers to an individual’s perception of the expectations of significant others in their social relationships regarding their behavior. Empirical research in the consumption field has demonstrated the significant impact of subjective norms on individual psychological cognition (Yang & Jolly, 2009 ). A meta-analysis by Hagger, Chatzisarantis ( 2009 ) even highlighted the statistically significant association between subjective norms and self-determination factors. Consequently, this study further explores its application in the context of influencer marketing advertisements on social media.

Subjective norm and self-disclosure willingness

In numerous studies on social media privacy, subjective norms significantly influence an individual’s self-disclosure willingness. Wirth et al. ( 2019 ) based on the privacy calculus theory, surveyed 1,466 participants and found that personal self-disclosure on social media is influenced by the behavioral expectations of other significant reference groups around them. Their research confirmed that subjective norms positively influence self-disclosure of information and highlighted that individuals’ cognitions and behaviors cannot ignore social and environmental factors. Heirman et al. ( 2013 ) in an experiment with Instagram users, also noted that subjective norms could promote positive consumer behavioral responses. Specifically, when important family members and friends highly regard social media influencers as trustworthy, we may also be more inclined to disclose our information to influencers and share this information with our surrounding family and friends without fear of disapproval. In our subjective norms, this is considered a positive and valuable interactive behavior, leading us to exhibit engagement behaviors. Based on this logic, we propose the following research hypotheses:

H11: Subjective norms are positively correlated with self-disclosure willingness.

H11a: Self-disclosure willingness mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H11b: Self-disclosure willingness mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H11c: Self-disclosure willingness mediates the impact of subjective norms on content creation in consumer engagement behavior.

Subjective norm and information trust

Numerous studies have indicated that subjective norms significantly influence trust (Roh et al., 2022 ). This can be explained by reference group theory, suggesting people tend to minimize the effort expended in decision-making processes, often looking to the behaviors or attitudes of others as a point of reference; for instance, subjective norms can foster acceptance of technology by enhancing trust (Gupta et al., 2021 ). Analogously, if a consumer’s social network generally holds positive attitudes toward influencer advertising, they are also more likely to trust the endorsed advertisement information, as it conserves the extensive effort required in gathering product information (Chetioui et al., 2020 ). Therefore, this paper proposes the following research hypotheses:

H12: Subjective norms are positively correlated with information trust.

H12a: Information trust mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H12b: Information trust mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H12c: Information trust mediates the impact of subjective norms on content creation in consumer engagement behavior.

Conceptual model

In summary, based on the Stimulus (S)-Organism (O)-Response (R) framework, this study constructs the external stimulus factors (S) from three dimensions: influencer factors (parasocial identification, source credibility), advertising information factors (informative value, Ad targeting accuracy), and social influence factors (subjective norms). This is grounded in social capital theory and the theory of planned behavior. drawing on self-determination theory, the current study constructs the individual psychological factors (O) using self-disclosure willingness, innovativeness, and information trust. Finally, the behavioral response (R) is constructed using consumer engagement, which includes content consumption, content contribution, and content creation, as illustrated in Fig. 1 .

Consumer engagement behavior impact model based on SOR framework.

Materials and methods

Participants and procedures.

The current study conducted a survey through the Wenjuanxing platform to collect data. Participants were recruited through social media platforms such as WeChat, Douyin, Weibo et al., as samples drawn from social media users better align with the research purpose of our research and ensure the validity of the sample. Before the survey commenced, all participants were explicitly informed about the purpose of this study, and it was made clear that volunteers could withdraw from the survey at any time. Initially, 600 questionnaires were collected, with 78 invalid responses excluded. The criteria for valid questionnaires were as follows: (1) Respondents must have answered “Yes” to the question, “Do you follow any influencers (internet celebrities) on social media platforms?” as samples not using social media or not following influencers do not meet the study’s objective, making this question a prerequisite for continuing the survey; (2) Respondents had to correctly answer two hidden screening questions within the questionnaire to ensure that they did not randomly select scores; (3) The total time taken to complete the questionnaire had to exceed one minute, ensuring that respondents had sufficient time to understand and thoughtfully answer each question; (4) Respondents were not allowed to choose the same score for eight consecutive questions. Ultimately, 522 valid questionnaires were obtained, with an effective rate of 87.00%, meeting the basic sample size requirements for research models (Gefen et al., 2011 ). Detailed demographic information of the study participants is presented in Table 1 .

Measurements

To ensure the validity and reliability of the data analysis results in this study, the measurement tools and scales used in this chapter were designed with reference to existing established research. The main variables in the survey questionnaire include parasocial identification, source credibility, informative value, ad targeting accuracy, subjective norms, self-disclosure willingness, innovativeness, information trust, content consumption, content contribution, and content creation. The measurement scale for parasocial identification was adapted from the research of Schramm and Hartmann, comprising 6 items (Schramm & Hartmann, 2008 ). The source credibility scale was combined from the studies of Cheung et al. and Luo & Yuan’s research in the context of social media influencer marketing, including 4 items (Cheung et al., 2009 ; Lou & Yuan, 2019 ). The scale for informative value was modified based on Voss et al.‘s research, consisting of 4 items (Voss et al., 2003 ). The ad targeting accuracy scale was derived from the research by Qiu Aimei et al., 2018 ) including 3 items. The subjective norm scale was adapted from Ajzen’s original scale, comprising 3 items (Ajzen, 2002 ). The self-disclosure willingness scale was developed based on Chu and Kim’s research, including 3 items (Chu & Kim, 2011 ). The innovativeness scale was formulated following the study by Sun et al., comprising 4 items (Sun et al., 2006 ). The information trust scale was created in reference to Chu and Choi’s research, including 3 items (Chu & Choi, 2011 ). The scales for the three components of social media consumer engagement—content consumption, content contribution, and content creation—were sourced from the research by Buzeta et al., encompassing 8 items in total (Buzeta et al., 2020 ).

All scales were appropriately revised for the context of social media influencer marketing. To avoid issues with scoring neutral attitudes, a uniform Likert seven-point scale was used for each measurement item (ranging from 1 to 7, representing a spectrum from ‘strongly disagree’ to ‘strongly agree’). After the overall design of the questionnaire was completed, a pre-test was conducted with 30 social media users to ensure that potential respondents could clearly understand the meaning of each question and that there were no obstacles to answering. This pre-test aimed to prevent any difficulties or misunderstandings in the questionnaire items. The final version of the questionnaire is presented in Table 2 .

Data analysis

Since the model framework of the current study is derived from theoretical deductions of existing research and, while logically constructed, does not originate from an existing research model, this study still falls under the category of exploratory research. According to the analysis suggestions of Hair and other scholars, in cases of exploratory research model frameworks, it is more appropriate to choose Smart PLS for Partial Least Squares Path Analysis (PLS) to conduct data analysis and testing of the research model (Hair et al., 2012 ).

Measurement of model

In this study, careful data collection and management resulted in no missing values in the dataset. This ensured the integrity and reliability of the subsequent data analysis. As shown in Table 3 , after deleting measurement items with factor loadings below 0.5, the final factor loadings of the measurement items in this study range from 0.730 to 0.964. This indicates that all measurement items meet the retention criteria. Additionally, the Cronbach’s α values of the latent variables range from 0.805 to 0.924, and all latent variables have Composite Reliability (CR) values greater than the acceptable value of 0.7, demonstrating that the scales of this study have passed the reliability test requirements (Hair et al., 2019 ). All latent variables in this study have Average Variance Extracted (AVE) values greater than the standard acceptance value of 0.5, indicating that the convergent validity of the variables also meets the standard (Fornell & Larcker, 1981 ). Furthermore, the results show that the Variance Inflation Factor (VIF) values for each factor are below 10, indicating that there are no multicollinearity issues with the scales in this study (Hair, 2009 ).

The current study then further verified the discriminant validity of the variables, with specific results shown in Table 4 . The square roots of the average variance extracted (AVE) values for all variables (bolded on the diagonal) are greater than the Pearson correlation coefficients between the variables, indicating that the discriminant validity of the scales in this study meets the required standards (Fornell & Larcker, 1981 ). Additionally, a single-factor test method was employed to examine common method bias in the data. The first unrotated factor accounted for 29.71% of the variance, which is less than the critical threshold of 40%. Therefore, the study passed the test and did not exhibit serious common method bias (Podsakoff et al., 2003 ).

To ensure the robustness and appropriateness of our structural equation model, we also conducted a thorough evaluation of the model fit. Initially, through PLS Algorithm calculations, the R 2 values of each variable were greater than the standard acceptance value of 0.1, indicating good predictive accuracy of the model. Subsequently, Blindfolding calculations were performed, and the results showed that the Stone-Geisser Q 2 values of each variable were greater than 0, demonstrating that the model of this study effectively predicts the relationships between variables (Dijkstra & Henseler, 2015 ). In addition, through CFA, we also obtained some indicator values, specifically, χ 2 /df = 2.528 < 0.3, RMSEA = 0.059 < 0.06, SRMR = 0.055 < 0.08. Given its sensitivity to sample size, we primarily focused on the CFI, TLI, and NFI values, CFI = 0.953 > 0.9, TLI = 0.942 > 0.9, and NFI = 0.923 > 0.9 indicating a good fit. Additionally, RMSEA values below 0.06 and SRMR values below 0.08 were considered indicative of a good model fit. These indices collectively suggested that our model demonstrates a satisfactory fit with the data, thereby reinforcing the validity of our findings.

Research hypothesis testing

The current study employed a Bootstrapping test with a sample size of 5000 on the collected raw data to explore the coefficients and significance of the paths in the research model. The final test data results of this study’s model are presented in Table 5 .

The current study employs S-O-R model as the framework, grounded in theories such as self-determination theory and theory of planned behavior, to construct an influence model of consumer engagement behavior in the context of social media influencer marketing. It examines how influencer factors, advertisement information factors, and social influence factors affect consumer engagement behavior by impacting consumers’ psychological cognitions. Using structural equation modeling to analyze collected data ( N  = 522), it was found that self-disclosure willingness, innovativeness, and information trust positively influence consumer engagement behavior, with innovativeness having the largest impact on higher levels of engagement. Influencer factors, advertisement information factors, and social factors serve as effective external stimuli, influencing psychological motivators and, consequently, consumer engagement behavior. The specific research results are illustrated in Fig. 2 .

Tested structural model of consumer engagement behavior.

The impact of psychological motivators on different levels of consumer engagement: self-disclosure willingness, innovativeness, and information trust

The research analysis indicates that self-disclosure willingness and information trust are key drivers for content consumption (H1a, H2a validated). This aligns with previous findings that individuals with a higher willingness to disclose themselves show greater levels of engagement behavior (Chu et al., 2023 ); likewise, individuals who trust advertisement information are more inclined to engage with advertisement content (Kim, Kim, 2021 ). Moreover, our study finds that information trust has a stronger impact on content consumption, underscoring the importance of trust in the dissemination of advertisement information. However, no significant association was found between individual innovativeness and content consumption (H3a not validated).

Regarding the dimension of content contribution in consumer engagement, self-disclosure willingness, information trust, and innovativeness all positively impact it (H1b, H2b, and H3b all validated). This is consistent with earlier research findings that individuals with higher self-disclosure willingness are more likely to like, comment on, or share content posted by influencers on social media platforms (Towner et al., 2022 ); the conclusions of this paper also support that innovativeness is an important psychological driver for active participation in social media interactions (Kamboj & Sharma, 2023 ). However, at the level of consumer engagement in content contribution, while information trust also exerts a positive effect, its impact is the weakest, although information trust has the strongest impact on content consumption.

In social media advertising, the ideal outcome is the highest level of consumer engagement, i.e., content creation, meaning consumers actively join in brand content creation, seeing themselves as co-creators with the brand (Nadeem et al., 2021 ). Our findings reveal that self-disclosure willingness, innovativeness, and information trust all positively influence content creation (H1c, H2c, and H3c all validated). The analysis found that similar to the impact on content contribution, innovativeness has the most significant effect on encouraging individual content creation, followed by self-disclosure willingness, with information trust having the least impact.

In summary, while some previous studies have shown that self-disclosure willingness, innovativeness, and information trust are important factors in promoting consumer engagement (Chu et al., 2023 ; Nadeem et al., 2021 ; Geng et al., 2021 ), this study goes further by integrating and comparing all three within the same research framework. It was found that to trigger higher levels of consumer engagement behavior, trust is not the most crucial psychological motivator; rather, the most effective method is to stimulate consumers’ innovativeness, thus complementing previous research. Subsequently, this study further explores the impact of different stimulus factors on various psychological motivators.

The influence of external stimulus factors on psychological motivators: influencer factors, advertisement information factors, and social factors

The current findings indicate that influencer factors, such as parasocial identification and source credibility, effectively enhance consumer engagement by influencing self-disclosure willingness and information trust. This aligns with prior research highlighting the significance of parasocial identification (Shan et al., 2020 ). Studies suggest parasocial identification positively impacts consumer engagement by boosting self-disclosure willingness and information trust (validated H4a, H4b, H4c, and H5a), but not content contribution or creation through information trust (H5b, H5c not validated). Source credibility’s influence on self-disclosure willingness was not significant (H6 not validated), thus negating the mediating effect of self-disclosure willingness (H6a, H6b, H6c not validated). Influencer credibility mainly affects engagement through information trust (H7a, H7b, H7c validated), supporting previous findings (Shan et al., 2020 ).

Advertisement factors (informative value and ad targeting accuracy) promote engagement through innovativeness and information trust. Informative value significantly impacts higher-level content contribution and creation through innovativeness (H8b, H8c validated), while ad targeting accuracy influences consumer engagement at all levels mainly through information trust (H10a, H10b, H10c validated).

Social factors (subjective norms) enhance self-disclosure willingness and information trust, consistent with previous research (Wirth et al., 2019 ; Gupta et al., 2021 ), and further promote consumer engagement across all levels (H11a, H11b, H11c, H12a, H12b, and H12c all validated).

In summary, influencer, advertisement, and social factors impact consumer engagement behavior by influencing psychological motivators, with influencer factors having the greatest effect on content consumption, advertisement content factors significantly raising higher-level consumer engagement through innovativeness, and social factors also influencing engagement through self-disclosure willingness and information trust.

Implication

From a theoretical perspective, current research presents a comprehensive model of consumer engagement within the context of influencer advertising on social media. This model not only expands the research horizon in the fields of social media influencer advertising and consumer engagement but also serves as a bridge between two crucial themes in new media advertising studies. Influencer advertising has become an integral part of social media advertising, and the construction of a macro model aids researchers in understanding consumer psychological processes and behavioral patterns. It also assists advertisers in formulating more effective strategies. Consumer engagement, focusing on the active role of consumers in disseminating information and the long-term impact on advertising effectiveness, aligns more closely with the advertising effectiveness measures in the new media context than traditional advertising metrics. However, the intersection of these two vital themes lacks comprehensive research and a universal model. This study constructs a model that elucidates the effects of various stimuli on consumer psychology and engagement behaviors, exploring the connections and mechanisms through different mediating pathways. By differentiating levels of engagement, the study offers more nuanced conclusions for diverse advertising objectives. Furthermore, this research validates the applicability of self-determination theory in the context of influencer advertising effectiveness. While this psychological theory has been utilized in communication behavior research, its effectiveness in the field of advertising requires further exploration. The current study introduces self-determination theory into the realm of influencer advertising and consumer engagement, thereby expanding its application in the field of advertising communication. It also responds to the call from the advertising and marketing academic community to incorporate more psychological theories to explain the ‘black box’ of consumer psychology. The inclusion of this theory re-emphasizes the people-centric approach of this research and highlights the primary role of individuals in advertising communication studies.

From a practical perspective, this study provides significant insights for adapting marketing strategies to the evolving media landscape and the empowered role of audiences. Firstly, in the face of changes in the communication environment and the empowerment of audience communication capabilities, traditional marketing approaches are becoming inadequate for new media advertising needs. Traditional advertising focuses on direct, point-to-point effects, whereas social media advertising aims for broader, point-to-mass communication, leveraging audience proactivity to facilitate the viral spread of content across online social networks. Secondly, for brands, the general influence model proposed in this study offers guidance for influencer advertising strategy. If the goal is to maximize reach and brand recognition with a substantial advertising budget, partnering with top influencers who have a large following can be an effective strategy. However, if the objective is to maximize cost-effectiveness with a limited budget by leveraging consumer initiative for secondary spread, the focus should be on designing advertising content that stimulates consumer creativity and willingness to innovate. Thirdly, influencers are advised to remain true to their followers. In influencer marketing, influencers attract advertisers through their influence over followers, converting this influence into commercial gain. This influence stems from the trust followers place in the influencer, thus influencers should maintain professional integrity and prioritize the quality of information they share, even when presented with advertising opportunities. Lastly, influencers should assert more control over their relationships with advertisers. In traditional advertising, companies and brands often exert significant control over the content. However, in the social media era, influencers should negotiate more creative freedom in their advertising partnerships, asserting a more equal relationship with advertisers. This approach ensures that content quality remains high, maintaining the trust influencers have built with their followers.

Limitations and future directions

while this study offers valuable insights into the dynamics of influencer marketing and consumer engagement on social media, several limitations should be acknowledged: Firstly, constrained by the research objectives and scope, this study’s proposed general impact model covers three dimensions: influencers, advertisement information, and social factors. However, these dimensions are not limited to the five variables discussed in this paper. Therefore, we call for future research to supplement and explore more crucial factors. Secondly, in the actual communication environment, there may be differences in the impact of communication effectiveness across various social media platforms. Thus, future research could also involve comparative studies and explorations between different social media platforms. Thirdly, the current study primarily examines the direct effects of various factors on consumer engagement. However, the potential interaction effects between these variables (e.g., how influencers’ credibility might interact with advertisement information quality) are not extensively explored. Future research could investigate these complex interrelationships for a more holistic understanding. Lastly, our study, being cross-sectional, offers preliminary insights into the complex and dynamic nature of engagement between social media influencers and consumers, yet it does not incorporate the temporal dimension. The diverse impacts of psychological needs on engagement behaviors hint at an underlying dynamism that merits further investigation. Future research should consider employing longitudinal designs to directly observe how these dynamics evolve over time.

The findings of the current study not only theoretically validate the applicability of self-determination theory in the field of social media influencer marketing advertising research but also broaden the scope of advertising effectiveness research from the perspective of consumer engagement. Moreover, the research framework offers strategic guidance and reference for influencer marketing strategies. The main conclusions of this study can be summarized as follows.

Innovativeness is the key factor in high-level consumer engagement behavior. Content contribution represents a higher level of consumer engagement compared to content consumption, as it not only requires consumers to dedicate attention to viewing advertising content but also to share this information across adjacent nodes within their social networks. This dissemination of information is a pivotal factor in the success of influencer marketing advertisements. Hence, companies and brands prioritize consumers’ content contribution over mere viewing of advertising content (Qiu & Kumar, 2017 ). Compared to content consumption and contribution, content creation is considered the highest level of consumer engagement, where consumers actively create and upload brand-related content, and it represents the most advanced outcome sought by enterprises and brands in advertising campaigns (Cheung et al., 2021 ). The current study posits that to pursue better outcomes in social media influencer advertising marketing, enhancing consumers’ willingness for self-disclosure, innovativeness, and trust in advertising information are effective strategies. However, the crux lies in leveraging the consumer’s subjective initiative, particularly in boosting their innovativeness. If the goal is simply to achieve content consumption rather than higher levels of consumer engagement, the focus should be on fostering trust in advertising information. There is no hierarchy in the efficacy of different strategies; they should align with varying marketing contexts and advertising objectives.

The greatest role of social media influencers lies in attracting online traffic. information trust is the core element driving content consumption, and influencer factors mainly affect consumer engagement behaviors through information trust. Therefore, this study suggests that the primary role of influencers in social media advertising is to attract online traffic, i.e., increase consumer behavior regarding ad content consumption (reducing avoidance of ad content), and help brands achieve the initial goal of making consumers “see and complete ads.” However, their impact on further high-level consumer engagement behaviors is limited. This mechanism serves as a reminder to advertisers not to overestimate the effects of influencers in marketing. Currently, top influencers command a significant portion of the ad budget, which could squeeze the budget for other aspects of advertising, potentially affecting the overall effectiveness of the campaign. Businesses and brands should consider deeper strategic implications when planning their advertising campaigns.

Valuing Advertising Information Factors, Content Remains King. Our study posits that in the social media influencer marketing context, the key to enhancing consumer contribution and creation of advertising content lies primarily in the advertising information factors. In other words, while content consumption is important, advertisers should objectively assess the role influencers play in advertising. In the era of social media, content remains ‘king’ in advertising. This view indirectly echoes the points made in the previous paragraph: influencers effectively perform initial ‘online traffic generation’ tasks in social media, but this role should not be overly romanticized or exaggerated. Whether it’s companies, brands, or influencers, providing consumers with advertisements rich in informational value is crucial to achieving better advertising outcomes and potentially converting consumers into stakeholders.

Subjective norm is an unignorable social influence factor. Social media is characterized by its network structure of information dissemination, where a node’s information is visible to adjacent nodes. For instance, if user A likes a piece of content C from influencer I, A’s follower B, who may not follow influencer I, can still see content C via user A’s page. The aim of marketing in the social media era is to influence a node and then spread the information to adjacent nodes, either secondarily or multiple times (Kumar & Panda, 2020 ). According to the Theory of Planned Behavior, an individual’s actions are influenced by significant others in their lives, such as family and friends. Previous studies have proven the effectiveness of the Theory of Planned Behavior in influencing attitudes toward social media advertising (Ranjbarian et al., 2012 ). Current research further confirms that subjective norms also influence consumer engagement behaviors in influencer marketing on social media. Therefore, in advertising practice, brands should not only focus on individual consumers but also invest efforts in groups that can influence consumer decisions. Changing consumer behavior in the era of social media marketing doesn’t solely rely on the company’s efforts.

As communication technology advances, media platforms will further empower individual communicative capabilities, moving beyond the era of the “magic bullet” theory. The distinction between being a recipient and a transmitter of information is increasingly blurred. In an era where everyone is both an audience and an influencer, research confined to the role of the ‘recipient’ falls short of addressing the dynamics of ‘transmission’. Future research in marketing and advertising should thus focus more on the power of individual transmission. Furthermore, as Marshall McLuhan famously said, “the medium is the extension of man.” The evolution of media technology remains human-centric. Accordingly, future marketing research, while paying heed to media transformations, should emphasize the centrality of the ‘human’ element.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy issues. Making the full data set publicly available could potentially breach the privacy that was promised to participants when they agreed to take part, and may breach the ethics approval for the study. The data are available from the corresponding author on reasonable request.

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The authors thank all the participants of this study. The participants were all informed about the purpose and content of the study and voluntarily agreed to participate. The participants were able to stop participating at any time without penalty. Funding for this study was provided by Minjiang University Research Start-up Funds (No. 324-32404314).

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Conceptualization: CG; methodology: CG and QD; software: CG and QD; validation: CG; formal analysis: CG and QD; investigation: CG and QD; resources: CG; data curation: CG and QD; writing—original draft preparation: CG; writing—review and editing: CG; visualization: CG; project administration: CG. All authors have read and agreed to the published version of the manuscript.

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Gu, C., Duan, Q. Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective. Humanit Soc Sci Commun 11 , 587 (2024). https://doi.org/10.1057/s41599-024-03127-w

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