Historically, BCs were studied primarily in the macro-economic literature (see, for example, Christiano and Fitgerald 1998 or Zarnowitz 1985). However, the aggregate state of a country’s national economy is not always representative of what happens at the individual industry level (Stock and Watson 1999; Berman and Pfleeger 1997), let alone at the firm or brand level, entity aggregations often studied in marketing. Before 2000, academic marketing research on the topic was scarce. In a 2005 review by Srinivasan, Rangaswamy and Lilien (p. 110), only three studies (Coulson 1979; Cundiff 1975; Yang 1964) on economic contractions were published prior to 2000 in the Journal of Marketing, the Journal of Marketing Research, or Marketing Science, with the most recent in 1979. However, since 2000, the number of marketing studies on BCs has grown rapidly.
An overview of 31 post-2000 marketing studies that focus on the impact of the BC is presented in Table 1. In all studies (ordered chronologically), BCs were a key theme in the theorizing and/or empirical analysis (i.e., the state of the economy was not just included as one of the control variables).Footnote 1
We organize our discussion of the main insights from these studies along the following five dimensions: (1) the key focus of the study (output metric, marketing input, or marketing-mix effectiveness), (2) the type of industry (durables, non-durables, and services, in either a B2B or B2C setting), (3) the geographic coverage (single country, multi-country, or global), (4) the data characteristics (temporal aggregation and time span) and, finally, (5) the temporary versus permanent nature of the BC impact. Figure 1 visualizes and structures the core research themes studied in the marketing literature so far that will be covered in this review.
A first distinction is based on the focus of the study, where we distinguish three streams of research. A first stream focuses on how performance (=output) measures vary across the stages of the BC, a second research stream evaluates how marketing conduct (=input) changes over the BC, while a third one is concerned with the differential effectiveness of various marketing investments across alternative BC phases. In Table 2, we list the studies according to their main study focus, along with their primary research findings.
Focus on performance (output)
Several studies (see panel A of Table 2) have evaluated the impact of BCs on a variety of performance measures. These studies often consider not only the extent of cyclical sensitivity in a particular industry or category, but also whether the resulting cyclical fluctuations are symmetric.
Studies often compare the cyclical fluctuations in the variable of interest with those in the overall economy, and consider (1) whether they occur in the same (pro-cyclical) or opposite (counter-cyclical) direction and (2) whether they get amplified or attenuated relative to those in the economy as a whole. Deleersnyder et al. (2004), for example, show that consumer durables in the U.S. are very sensitive to BCs, with cyclical fluctuations that are, on average, more than four times larger than (but in the same direction as) those in GNP. Similarly, Dekimpe et al. (2016) find an excess sensitivity to economic cycles in the international tourism sector, while Cleeren et al. (2015) show that also expenditures on health care are affected by aggregate economic fluctuations, as people save on their private healthcare spending during adverse economic conditions. The latter adjustments are, on average, less pronounced than in other economic sectors, even though there is considerable heterogeneity across countries. Governmental or public spending on healthcare, on the other hand, is much less affected by cyclical ups and downs, in order to assure a continued healthcare service irrespective of the state of the economy. In terms of movie demand, Mukherjee and Xiao (2016) find that while overall demand for movies decreases, the demand for escapist movies increases, a conclusion similar to the findings of Dhar and Weinberg (2016).
Other studies focus more on how consumers re-allocate their budgets, rather than on the absolute size of the up- or downswings. Dutt and Padmanabhan (2011), Millet et al. (2012) and Kamakura and Du (2012) show in this respect how macro-economic conditions instigate consumers to shift their spending across product categories and time. Dutt and Padmanabhan (2011) describe how, in a monetary crisis, consumers smooth their consumption at various levels: they shift spending over time and between different types of durable goods, non-durable goods and services. Millet et al. (2012) illustrate how consumers shift their spending during economic contractions towards products or services associated with avoiding negative outcomes (such as insurances), while products associated with achieving positive outcomes (e.g., gambling) are more popular during good economic times. Kamakura and Du (2012), in turn, show a shift in consumption from positional (status-conveying) goods and services to non-positional ones during recessions, and from discretionary to more necessary products, even if the total consumer budget is unaffected.
Finally, apart from economizing on total spending or instead of shifting spending across product categories, research has also shown how, especially for necessary goods, consumers reduce their spending during contractions by switching to less expensive brands within the category. Lamey et al. (2007), for instance, were the first to show (across four different countries) that many consumers switch to cheaper store brands during their grocery shopping in bad economic times, while they switch (albeit not fully) back to national brands in the subsequent recovery. The same conclusion is echoed in Dubé et al. (2017), even though they posit that the switch to private labels due to income and/or wealth shifts is less extreme than reported in Lamey et al. (2007) and Lamey et al. (2012). Dekimpe et al. (2011) and Lamey (2014) argue that observed increases in store brand sales are partly due to consumers shopping more at discounters during bad economic times where only few national brands are offered, “forcing” consumers to choose among the less expensive store brand alternatives. Switching to cheaper brands allows consumers to reduce total spending without having to give in on the amount consumed. Ma et al. (2011), in turn, focus on the price of gasoline, a macro-economic factor that is changing more rapidly than BCs, but which also causes consumers to make adjustments in their buying patterns. When gasoline prices rise sharply, consumers have less disposable income, and must find ways to reduce spending in other areas. This study examines and finds various potential avenues for savings in consumer grocery spending: shopping frequency is reduced and shifts towards supercenter retail formats, from national brands to private labels, and from regular-priced to promotional products. Relatedly, Cha et al. (2015) show that household coping strategies for food purchases are not restricted to switching to less expensive brand alternatives or cheaper store outlets, but also that more items will be sold on deal when the economy turns sour.
While previous research has documented the possibility of multiple coping strategies, little is known about their relative occurrence. What are the categories where consumers opt to consume less, under what circumstances do they switch to cheaper alternatives, and for what products do they intensify their search to still buy the same brand either in a cheaper retail outlet or on deal? And how (and why) does this choice of coping strategy vary across consumer segments. Most prior studies have taken a fairly aggregate point of view, and focused, for example, on country-level durable sales, total category sales, or overall private-label shares. More research is needed to determine which consumers are more reluctant to adjust their consumption behavior when the economic conditions deteriorate, and opt instead to incur additional debt to maintain as long as possible their pre-crisis consumption standards. Similarly, future research should identify what firms (smaller/larger, publicly-listed versus family-owned, etc.) are more/less likely to suffer (or prosper) during difficult economic times, and/or what brands (e.g., global versus local, with more or less sub-brands) are more resistant to cyclical fluctuations.
Apart from the size and direction of the BC fluctuations in performance, a number of studies in this research stream have documented asymmetries between up- and downward movements in category or industry performance. This is observed in durable sales by Deleersnyder et al. (2004), but also private-label performance (Lamey et al. 2007) exhibits cyclical up- and downward movements that are not mirror images. In tourism, Dekimpe et al. (2016) examined, but could not find, such asymmetries across alternative BC phases. Asymmetries can occur in both the speed and depth of the cyclical fluctuations.
In Deleersnyder et al. (2004), consumers are found to cut back more (= depth) and faster (= speed) on their durable purchases during contractions than they increase spending in subsequent expansion periods. Asymmetries in the speed of downward versus upward adjustments, or steepness asymmetry, may arise from the way consumers gain (slow) or lose (fast) trust in the economic climate (Nooteboom et al., 1997). Moreover, people find themselves at the lowest level of their income right after a recession, so any initial rise in income will be used first to pay off debts and/or rebuild a precautionary stock of assets or capital (Gale 1996; Carroll 1992). Consequently, consumers are quick in cutting back on their durable expenditures in a contraction, while upward adjustments after the contraction are more slowly. As such, it takes considerably more time to restore the initial consumer spending. Dekimpe et al. (2016) could not find any systematic asymmetry in the speed of adjustment in tourism demand across contractions and expansions, suggesting a quicker recovery than many other (nonservice) sectors.
Asymmetries also arise in the size of the peaks and troughs of durable sales, causing the troughs to be deeper (relative to the mean level) than the peaks are tall, a phenomenon sometimes referred to as deepness asymmetry. A rationale for this can be found in prospect theory (Tversky and Kahneman 1991), which posits that people react more extensively to unfavorable changes than to comparable gains. If households experience or expect a deterioration in their wages or income, they considerably reduce their spending levels, especially for more discretionary products, while upward adjustments during expansions trigger more moderate reactions (Deleersnyder et al. 2004). Evidence of comparable asymmetries with CPG products is given in Lamey et al. (2007), where consumers are found to switch quickly and extensively towards private labels in contractions, while their switching back to national brands in the subsequent expansion period occurs more slowly and less extensively.
First, more studies have documented on the extent of cyclical sensitivity than on the cyclical asymmetry in performance series. As such, little is known on possible contingency factors for, respectively, level and speed asymmetries. It would be useful to consistently report on both asymmetry dimensions. Second, it would be interesting to further explore the temporal dimension in the reported asymmetries. For example, does a higher speed of adjustment take place primarily during the initial months of the contraction, after which some habituation takes place? And how about the frugal fatigue discussed in ter Braak et al. (2014)? Do customers re-evaluate and adjust their coping behavior when the contraction lingers on for too long? Importantly, more research is also needed into the underlying psychological motivations of both consumers and managers to better understand why these asymmetric patterns are observed (or not).
Focus on marketing conduct (input)
The extent of BC fluctuations in various marketing input series has been evaluated in several studies (see Panel B of Table 2) that assess whether and how managers adjust their marketing actions in response to, respectively, adverse and prosperous economic times.
One of the first studies in this area was Srinivasan et al. (2005), who show empirically that a recession presents a unique opportunity for firms to strengthen their market position by going against the tide with a “proactive marketing strategy.” Based on management survey data, they show that firms with a strategic emphasis on marketing during the recession achieve superior business performance. The study is concerned with general marketing spending, without distinguishing between different marketing investments. In later studies, researchers also examined individual marketing instruments.
Studies on the extent of advertising spending over the BC cycle have repeatedly shown that a majority of firms cuts back significantly on advertising in a contraction, while advertising spending rebounds in the subsequent expansion period (Deleersnyder et al., 2009; Kashmiri and Mahajan 2014; Lamey et al. 2012; Özturan et al., 2014). Deleersnyder et al. (2009), for example, document pro-cyclical advertising adjustments across 37 countries worldwide in four traditional media (TV, radio, newspapers, and magazines). Various reasons have been advanced to explain why general BC swings become amplified in advertising expenditures. These include the widely held view of advertising as a cost rather than an investment, the low commitment to and flexibility in media contracts, and the fact that fewer competitors engage in advertising in recessionary times, which warrants a lower spending level to achieve the same share-of-voice (Deleersnyder et al. 2009). In addition, herding behavior can lead to further reductions once some firms start to cut their spending (Steenkamp and Fang 2011).
Despite the dominant practice of cutting back on advertising, research has repeatedly shown that maintained, or even increased, advertising spending during economic contractions often results in long-term managerial and social benefits, which can be in the form of better firm performance (Deleersnyder et al. 2009; Özturan et al. 2014; Kashmiri and Mahajan, 2014), lower long-term private-label growth (Deleersnyder et al. 2009; Lamey et al. 2012), and higher long-term growth of the advertising industry itself (Deleersnyder et al. 2009).
Research in both economics and marketing shows that innovation development and new-product launches exhibit pro-cyclical adjustment patterns, i.e., they move in the same direction as the general economy (see, e.g., Devinney 1990; Axarloglou 2003; Barlevy 2007; Lamey et al. 2012; Kashmiri and Mahajan 2014). According to Lamey et al. (2012), BC fluctuations in this instrument get amplified, both for major and more incremental innovations. The arguments for the more severe reductions in spending on innovations and R&D are similar to those for reduced advertising, and relate to difficult spending justification, its common treatment as a suspendable cost, and a reduction in the number of competing innovations. In this context, Kashmiri and Mahajan (2014) show that the reduction in the rate of new-product introductions is less dramatic in family-owned firms, given the longer investment horizon of family executives. Both Lamey et al. (2012) and Kashmiri and Mahajan (2014) show that if managers maintain or increase new product introductions in a recession, they will achieve higher growth and better (long-term) performance than when they systematically cut such activities, albeit temporarily, in response to adverse economic shocks.
In economics, opposing arguments on the direction of the recommended price changes during economic contractions have been made. On the one hand, it has been argued that prices should decrease when demand is unexpectedly low. Firms then switch from collusive higher prices to lower competitive prices, because they attribute their lower demand to cheating on the part of their rivals (see, e.g., Green and Porter 1984). On the other hand, it has also been argued that especially during high-demand periods (or booms), it is more beneficial to undercut the higher collusive prices (see, e.g., Rotemberg and Saloner 1986). Others have studied the implications of demand trends on competition. For example, Haltiwanger and Harrington (1991) argue that the threat of future punishments is a stronger deterrent if demand is increasing versus decreasing. Thus, firms are more likely to sustain higher (collusive) prices when the demand trend is positive. An in-depth discussion on the differences between these models is provided in Sudhir et al. (2005), who introduce the notion of time-varying competition (with the extent of competition a function of aggregate demand). They discuss how demand can have both a direct effect on prices, and an indirect effect through changing competition. Marn et al. (2003), in turn, point out that managers have a tendency to increase prices (p) during a contraction to offset the revenue (p*q) losses caused by reduced sales (q) levels.
Deleersnyder et al. (2004), studying 24 consumer-durable categories, find evidence of counter-cyclical pricing: prices tend to increase during an economic contraction, and to decrease during an expansion. This, in turn, contributed significantly to the resulting amplified cyclical sensitivity in category sales. Sudhir et al. (2005) allow for firm-specific adjustments, and show how, in the U.S. photographic film market, Kodak priced more competitively in periods of high demand (reflected in higher levels of consumer confidence), while Fuji did not respond to changes in consumer confidence. Also Gilchrist et al. (2015) observe differences in firms’ price-setting behavior in response to adverse demand, which they attribute to differences in the firms’ liquidity position.
Lamey et al. (2012) document clear cyclical patterns in various promotional activities in the CPG industry. The relative intensity of national-brands’ promotions compared to private labels was found to decrease during economic downturns for three main promotion instruments (displays, features, and temporary price cuts), while the reverse pattern is observed in expansions. Importantly, unlike advertising and innovations, the promotional instrument is a shared manufacturer-retailer decision, with the retailer having the final say. So the observed decline in relative promotion activity may be caused by manufacturers cutting back on promotions in a recession, by retailers reducing the pass-through during that time, or a combination of both. The regular price premium of branded offerings over private-label variants is the only marketing instrument in Lamey et al. (2012) with no clear adjustment pattern following changes in the state of the economy. Also Coibion et al. (2015) find little cyclical sensitivity in the inflation rate of prices posted by grocery retailers. However, they find that there is more cyclical sensitivity in the effective prices paid by consumers, consistent with consumers reallocating their expenditures to lower-priced brands and stores when local economic conditions deteriorate.
First, existing studies have almost exclusively focused on the cyclical sensitivity in one or two traditional marketing instruments. It would be good to move beyond the often-studied price and advertising variables, and to also consider variables such as assortment composition, distribution intensity, or online marketing activities that have not yet been studied (as extensively) in a BC context. For example, to what extent do (or should) national-brand manufacturers offer cheaper versions (e.g., Tide Basic) of their premium brand during recessions (similar to retailers offering multiple private-label tiers), or offer different (e.g., smaller) package sizes, and how can they do so without undermining the post-recession equity of their original brand? Similarly, should national-brand manufacturers try to get their product listed with hard discounters during economic downturns to maintain their overall sales levels, or will this undermine their relationships with their traditional channel partners (see also Dekimpe et al. 2011 for a more detailed discussion on these issues), which could hurt their performance in the subsequent expansion? And how about retailers? To what extent is the optimal proportion of private-label SKUs in their assortment (see, for example, Ailawadi et al., 2008) dependent on the state of the economy? And how should this number be divided across the different private-label tiers (budget, regular, premium)?
Second, it would be useful to consider marketing instruments at a lower level of aggregation. For example, it is customary to talk about the reduction in aggregate advertising spending in recessionary times. However, does this also apply to the many new online instruments? Due to its increased flexibility, cost effectiveness, better targeting opportunities, and improved measurability, one could argue that internet advertising is ideally suited for times where budgets are constrained, and where each marketing initiative needs to be justified extensively (Quelch and Jocz 2009). More research is needed to see whether the cyclical swings in online spending will be even more pronounced, or whether online advertising (and sales) is more resilient. Future research should examine this empirically for the growing set of online marketing instruments and channels.
Finally, little is known to what extent the content of the ads is (should be) adjusted. Similarly, should the type of innovations be tailored to worsening (improving) economic conditions?
Focus on differential marketing effectiveness
Finally, various studies (see Panel C of Table 2) have evaluated how the effectiveness of different marketing actions changes when the economy deteriorates/improves. If this is the case, managers are often recommended (see, e.g., Steenkamp and Fang 2011; van Heerde et al., 2013) to shift their spending from periods with lower marketing effectiveness to periods characterized by a higher effectiveness. Thus far, studies have mainly looked at this issue in the context of advertising, R&D, and prices, even though evidence also exists for a differential importance over the BC of customer satisfaction (Hunneman et al., 2015; Kumar et al., 2014; Ou et al., 2014) and of critics’ ratings of movies (Dhar and Weinberg 2016).
R&D investments and pricing
Overall, research findings are consistent with respect to R&D investments and pricing: for the majority of products and brands, both instruments are more effective in economic downturns, and hence, it is recommended to increase the spending on R&D and to focus more on price reductions during an economic downturn. These conclusions are based on studies by, among others, Srinivasan et al. (2011) and Steenkamp and Fang (2011) for R&D spending, and Estelami et al. (2001), Gordon et al. (2013) and van Heerde et al. (2013) for prices. In addition, Schöler et al. (2014) find that the riskiness and radicalness of financial innovations tends to increase the introducing banks’ abnormal returns, even though radicalness has lower cumulative abnormal stock returns in recessions than in expansions. No such interaction was found for riskiness.
Research findings are less equivocal for advertising. Srinivasan et al. (2011) show, across many industries, that firms, from a profit point of view, tend to overspend on advertising in a recession, while van Heerde et al. (2013) find that long-term advertising elasticities are lower in a recession, suggesting that advertising should be reduced during that time. In contrast, a higher advertising effectiveness is found in Steenkamp and Fang (2011) and Graham and Frankenberger (2011), leading them to recommend higher advertising spending in a recession. The same recommendation is provided by studies that linked the cyclical fluctuations in advertising to long-term firm performance, such as Deleersnyder et al. (2009) or Lamey et al. (2012). In a recent meta-analysis, Edeling and Fischer (2016) look at the stock-market impact of both current advertising expenditures (a flow variable) and market assets (stock variables, which can be brand related, like brand equity, or customer related, like customer equity). They find the marketing-asset elasticities to be higher during recession times (while no such effect was found for advertising expenditure elasticities). Strong assets help firms to retain customers and thus attenuate the negative financial consequences of recessions. Given that marketing assets are quite sticky, however, one could make a case to try to increase the asset already in better times, when more financial resources may be available.
Customer satisfaction and movie critics
Also other marketing activities have been shown to have a differential effectiveness across alternative BC stages. Several papers have shown that the impact of/on customer satisfaction changes when the economy deteriorates. Hunneman et al. (2015), for instance, examine the relationship between customer satisfaction with the retailer and consumers’ share of wallet during grocery shopping. While this relationship is not directly moderated by consumer confidence, the impact of service attributes on customer satisfaction is stronger in periods of low consumer confidence, making consumers spend more at higher service firms when the economy is down. Similarly, Ou et al. (2014) examine the moderating role of consumer confidence on the relationship between various customer equity drivers (value equity, brand equity and relationship equity) and consumer loyalty intentions. The differential effectiveness across high and low consumer confidence varies across industries. Also, according to Kumar et al. (2014), the returns on marketing investments in customer satisfaction in the airline service industry differ between expansions and contractions. Unlike earlier expectations, investments in service satisfaction are found to be more effective in expansion periods. Finally, Dhar and Weinberg (2016) find that movie critics have a higher impact on movie demand in contractions.
However, not all marketing relationships have been found to differ between expansion and contraction periods. For example, Tuli et al. (2012) did not find an asymmetric stock-market reaction to unexpected changes in advertising spending and growth in same-store sales, and Fornell et al. (2010) found the relationship between customer satisfaction and consumer spending growth to not change structurally in the recent great financial crisis. A similar conclusion was obtained in Yeung et al. (2013), who found no significant interaction between customer satisfaction and a continuous (rather than the discrete recession dummy used in Fornell et al. 2010) income per capita metric. Van Heerde et al. (2013), in turn, found that short-run price and advertising elasticities do not change with the BC, while their long-run counterparts do in an asymmetric way.
While previous results hold for the majority of firms and brands, several studies have pointed out that there can be considerable heterogeneity depending on the industry type (Srinivasan et al. 2011; Steenkamp and Fang 2011), product category (Gordon et al. 2013; van Heerde et al. 2013), and even across different brands or firms within the category (Mukherjee and Bonfrer 2015; van Heerde et al. 2013). More research is needed to develop adequate contingency frameworks to better understand this heterogeneity in the cyclical sensitivity of marketing’s effectiveness. Relatedly, more attention on qualitative issues is warranted, such as the quality of the advertising campaigns, to complement the more quantity-oriented metrics studied thus far. Will only the best creative talent be retained by advertising agencies in recessionary times, resulting in a higher average quality (and hence, more effective) campaigns running during such times?
Importantly, there is agreement across multiple studies that while individual firms or managers may not be able to prevent economic downturns from happening, they can, to some extent, limit the impact of such contractions on their performance by spending more (or refrain from cutting back, which will often improve already their relative position) on marketing during difficult times. Such a practice is sometimes referred to as proactive marketing (Srinivasan et al. 2005). Interestingly, this ability to moderate the impact of BC fluctuations allows one to partially endogenize the BC concept (see in this respect also Bharadwaj et al., 2005). More research is needed to better advise managers how to do this depending on their specific setting.
Clearly, many marketers do not have extra money available when times turn sour, and may therefore find this advice to invest more in marketing impractical. However, research on this issue not just argues that managers should spend more on marketing, they also make a case for spending existing budgets more smartly by shifting some of the marketing expenses on e.g., advertising, innovations, and promotions over time towards contraction periods to be able to weather tough economic times. Alternatively, one could reallocate marketing budgets across instruments (Lamey et al. 2012) or across countries (Dekimpe et al. 2016) to better ride the economic tides without increasing the total marketing budget. More research is required to make these normative recommendations, which are thus far mostly directional in nature, more actionable/concrete.
Type of industry
The impact of BCs has been found to differ between durable and non-durable industries, between B2B and B2C markets, and between purchases of goods and services.
Durable vs. non-durable consumer goods
Consumer spending on durable goods is hit particularly hard by contractions, resulting in cyclical fluctuations that are much more pronounced than those in aggregate GDP (Deleersnyder et al. 2004). These outlays are often a consumer choice for which there is no pressing need to make the purchase at a particular time. Consumers who want to restrict their purchases during an economic contraction tend to first reconsider these more discretionary expenditures. When faced with adverse economic conditions, consumers can postpone the acquisition (Cook 1999), and current owners of consumer durables can extend the lives of their products by repairing, rather than replacing, them (Clark et al., 1984).
In contrast, it is more difficult to cut back on non-durable consumer goods. Many frequently-purchased CPGs are seen as necessities, and their purchases have become more habitual. Because of that, the quantity bought of these products is more difficult to adjust (Lamey et al. 2007). During a contraction, consumers do not necessarily buy less of these products, but are more likely to use other strategies to economize on their spending, such as switching to cheaper alternatives (Lamey et al. 2007, 2012; Dubé et al. 2017), switching to cheaper stores like discounters (Lamey 2014) or supercenters (Ma et al. 2011), or looking for products on deal (Cha et al. 2015; Ma et al. 2011).
B2B vs. B2C industries
Most empirical studies have focused on (durable and non-durable) consumer goods (B2C), where consumers are the final buyers. Far less attention has been devoted to the business-to-business (B2B) market (not unlike the marketing literature at large; see Lilien 2016). Notable exceptions are Özturan et al. (2014), Srinivasan et al. (2005, 2011) and Frösén et al. (2016). Even though one could argue that clients in B2B industries may be more rational (Srinivasan et al. 2011), and therefore less affected by short-term economic-sentiment swings than end consumers, they may suffer from a “bullwhip” effect, in that small BC-induced changes in demand by the end consumer get amplified as one moves further up the supply chain (Hanssens 1998; Lee et al., 1997, 2004). Moreover, given that the resources controlled by one firm can, directly or indirectly, depend on the resources controlled by other firms in a B2B network (Andersson and Mattsson 2010), the herding effect may also get amplified. As such, the overall cyclical sensitivity could be more or less pronounced in B2B markets.
Srinivasan et al. (2005) surveyed 20 senior marketing executives from four primary industry groups (engineering, computers, telecommunications and light manufacturing). Firms that adopted a proactive marketing response during a recession are found to achieve superior performance, already during the recession. However, they did not formally examine the difference in BC impact between B2B and B2C firms. In the 2001 contraction, Özturan et al. (2014) find significantly higher cuts in advertising in Turkish B2B firms compared to B2C firms, even though firm performance in the contraction did not differ significantly between both groups. Finally, Srinivasan et al. (2011) looked into differences in spending on R&D and advertising between both industries. They find that B2B firms are more often at a right level of advertising and R&D spending compared to B2C firms, which often underspend on R&D and overspend on advertising during recessions.
Frösén et al. (2016) surveyed 140 Finish B2B firms during both an economic up- and downturn, and assessed the impact of different forms of market orientation (MO) across the two economic states. The impact of the firms’ MO changed during a downturn, with interfunctional coordination boosting performance, and competitor orientation becoming detrimental. Interestingly, the performance impact of customer orientation remained unaltered between the two times of measurement. Hence, different MO dimensions yield diverse performance effects depending on the state of the economy.
Goods vs. services
While manufacturers of goods can smooth production and employment through stock building and producing for inventory when demand falls in a downturn, this is not possible for services (Zeithaml et al., 1985). The inseparability of production and consumption, along with the inherent perishability of services, is likely to make them more vulnerable to BC swings than goods.
Kumar et al. (2014) and Dekimpe et al. (2016) both find that the state of the economy significantly influences the travel service industry. Kumar et al. (2014) show that consumers book flights less often, and spend less on travel, when the economy turns sour. Dekimpe et al. (2016) find that the New Zealand tourism industry exhibits BC fluctuations that exceed the swings in aggregate GDP. Spending on leisure and business trips are discretionary expenditures that are easy to postpone, and holidays are considered a luxury good that consumers scale back drastically when their income deteriorates. Apart from the more luxury spending on holiday and travel services, also spending on medical services has been subjected to a BC analysis. While medical needs should not fluctuate with the BC, Cleeren et al. (2015) show that especially private health-care spending changes systematically with cyclical ups or downs. Finally, services are also subject to significant influences from changes in consumer confidence through its impact on customer satisfaction with the service providers (Hunneman et al. 2015; Ou et al. 2014).
A formal comparison of the effect of changes in R&D and advertising in a recession between goods and services was conducted in Srinivasan et al. (2011). They find that, in a recession, most B2C goods firms underspend on R&D, while they are at approximately the right level of advertising. B2C service firms, in turn, overspend on advertising during such times. Finally, B2B service firms are at approximately the right levels of R&D and advertising in a recession. These conclusions are based on a marginal profit analysis. However, these findings are found to differ depending on the outcome metric (profits or stock returns) that is used.
In combination, these findings show that, compared to goods firms, service firms may be affected differently by BC fluctuations, and therefore deserve separate research attention, especially since the service industry contributes significantly to most countries’ GDP.
While previous research has established clear differences between the broad B2C/B2B/service typologies, it would be good to explore in more detail the heterogeneity within a given sector. For example, different services can be more or less discretionary, more or less difficult to postpone, characterized by a different income elasticity, or be more or less sensitive to social-visibility considerations (Dekimpe et al. 2016). Because of this, extrapolations from a single service sector (as tourism, which was studied in several papers), or comparisons across broad aggregates (as services vs. consumer durables vs. CPGs) may well be misleading. Similar differences within the B2C and B2B sector have remained largely unexplored in the current literature. Finally, since service evaluations are highly dependent on consumers’ prior expectations about the service quality, examining if and how such expectations evolve in relation to the aggregate economic activity could be worth exploring further. Similarly, the extent to which firms rely on closer and more personal relationships in a B2B industry could affect their resilience to economic adversity. Future research should explore in more detail underlying drivers of differences in BC sensitivity across firms and industries.
All but one study in Table 1 work at the country level when assessing the general state of the economy. However, as pointed out by Kumar et al. (2014), regional economic differences may exist within a country or market (see also Croux et al., 2001 for a similar argument), which could also have a profound effect on firm performance. In many instances, information on a less aggregate level than the country level is missing, however.
Many studies have relied on U.S. data.Footnote 2 This could be attributed in part to the fact that more extensive, and especially longer, data on marketing conduct and performance are available for U.S.-based firms, and/or to the clear, publicly available, delineation of contraction and expansion periods by the NBER Business Cycle Dating Committee.Footnote 3 Still, it is important to extend this literature beyond the impact of the U.S. BC, as (1) economic contractions are not equally severe in all countries and may not even hit certain countries at all (Ang et al., 2000), (2) the timing of the peaks and troughs does not always coincide (Baxter and Kouparitsas 2005), while (3) also the marketing implications have been found to differ between countries and cultures (see, for example, Deleersnyder et al., 2009).
Several studies have observed stark differences between countries in terms of the evolution of their BC. The 1997 Asian crisis, for instance, had a dramatic impact on the Asian markets, but its impact on Western-European countries was negligible (Ang et al. 2000; Grewal and Tansuhaj 2001). Even though important international interdependencies exist across economic markets worldwide (Baxter and Kouparitsas 2005), and even though certain shocks can hit the economic activity globally, there is increasing evidence that BCs are not always synchronized, neither with the U.S. economy, nor with the economy of neighboring countries (Cerqueira, 2013). Peers et al. (2017) observe in this respect how the “global” financial crisis in 2009–2010 caused a deep trough in countries such as the U.S., the U.K., and Japan, but hardly affected China and Australia. For these countries, downturns were much more pronounced in the eighties and nineties. Also the timing of the peaks and troughs is not entirely synced. For instance, Japan went through a deep downturn in 1993–1995, whereas Australia experienced a strong upturn that time. The correlations between the 30 BCs in Peers et al. (2017) range from 0.90 (Malaysia–Thailand) to −0.23 (U.S. –Indonesia), with an average correlation of 0.36, well below unity. When cyclical fluctuations across countries do not coincide, multinational firms can exploit these differences, and shift marketing funds across countries that are in a different economic state. Such diversification opportunities can help to smooth the overall cyclical fluctuations in performance, and reduce the firm’s cyclical sensitivity (Dekimpe et al. 2016; Peers et al. 2017).
Given differences in the strength and timing of BCs across countries, it is important to study BC phenomena beyond the often-used U.S. setting. Fortunately, a number of studies have already focused on other countries, such as Finland (Frösén et al. 2016), Turkey (Özturan et al. 2014), Thailand (Grewal and Tansuhaj 2001), the Netherlands (Hunneman et al. 2015; Ou et al. 2014), or the U.K. (van Heerde et al. 2013). Also, a few studies (see Table 1 for more details) have used data from multiple countries (e.g., Lamey et al. 2007), sometimes from different continents (e.g., Deleersnyder et al. 2009). The latter studied cross-country differences in the cyclical sensitivity of advertising spending. Data across 37 countries revealed significant differences in the extent that advertising is reduced during contractions, which could be partly attributed to cultural differences between the countries. Still, the number of truly cross-national studies is limited. It would be useful to expand the geographic scope of the studies to include more developing economies, and to explore more systematically how cultural, economic and political differences moderate the cyclical sensitivity of consumers and/or managers. For example, does a stronger presence of discount chains provide a buffer to excessive cyclical swings? Will the growth of a modern retailing infrastructure in many developing countries (Bronnenberg and Ellickson 2015) attenuate or amplify the cyclical fluctuations, and what is the role of a more stringent rule of law system (cf. Steenkamp and Geyskens 2014)?
Apart from more insights on systematic differences between countries, there is also a need to better understand within-country differences. For example, to what extent are firms/brands more affected by regional, as opposed to national (or even global) contractions? And are consumers in rural as opposed to metropolitan regions more or less sensitive to BCs?
Data characteristics: data aggregation and time span
We further characterize earlier research according to two (inter-related) data characteristics: (1) the total time span covered, and (2) the temporal aggregation level of the data. BC research typically requires consistent time series over multiple decades, which is harder to achieve at a lower level of temporal aggregation (e.g., days or weeks). On top of that, the BC tends to vary more meaningfully over months, quarters or years, rather than over days or weeks.
A clear majority of the studies evaluates/contrasts marketing behavior and performance across multiple recession and expansion periods. Since BCs typically last between 1.5 and 8 years (Christiano and Fitzgerald 1998), a time span of several decades ensures that multiple cycles are covered, which allows researchers to move beyond the idiosyncracies of any specific recession and/or subsequent recovery. While some studies covered more than 50 years (e.g., 53 years in some of the categories studied in Deleersnyder et al. 2004), the majority of the studies listed in Tables 1, 2 and 3 covered around 15–25 years of data. Even with several decades of annual data, the number of data points remains limited. To increase the power of the statistical inference, many studies have therefore used meta-analytical techniques across multiple categories (e.g., Deleersnyder et al. 2004; van Heerde et al. 2013) or countries (Deleersnyder et al. 2009; Lamey et al. 2007).
Occasionally, studies have relied on cross-sectional data, and focused on a single recession period. This was the case in Grewal and Tansuhaj (2001), Ou et al. (2014), Srinivasan et al. (2005), and Özturan et al. (2014). Three of these studies use management surveys that were collected right after a severe economic recession that hit Asia in 1997 (Grewal and Tansuhaj 2001), or that hit the U.S. (Srinivasan et al. 2005) and Turkish economy (Özturan et al. 2014) in 2001. Ou et al. (2014) collected consumer survey data in 2010 right after a recession hit the Dutch economy. With surveys, it is hard to systematically collect data for the same entity over multiple time periods. Kumar et al. (2014) and Frösén et al. (2016) nevertheless used a longitudinal survey with multiple waves. Kumar et al. (2014) traced customers’ flight purchases and other service information for passengers who completed their surveys at least three times during the data period. These data were subsequently matched with monthly state-level survey data on the general health of the U.S. economy. Frösén et al. (2016), in turn, administered two waves (covering both an economic up- and downturn) of a web-based questionnaire among Finish B2B firms measuring various market-orientation dimensions, which were subsequently linked to objective firm performance.
Temporal aggregation of the data
Given the multi-decade time span in many studies, it is not surprising that only few studies have relied on data at the quarterly or monthly level. Exceptions are Gordon et al. (2013), who analyzed BCs at the quarterly level, and Hunneman et al. (2015), Ma et al. (2011), and van Heerde et al. (2013) who relied on monthly data. However, these studies cover a shorter time span with 3 years in Hunneman et al. (2015), 5 years (2001–2006) in Gordon et al. (2013), 2 years (2006–2008) in Ma et al. (2011), and 17 years (1993–2010) in van Heerde et al. (2013), suggesting a trade-off between both temporal characteristics.
The monthly BC turning points published by the NBER and other official institutions clearly show that recessions should ideally be tracked at a lower level of aggregation than the yearly level. Moreover, 7 out of the 10 contraction periods identified since 1950 lasted less than one full year. Accordingly, with yearly data, some cyclical fluctuations within a given year may remain unnoticed, and those years that are only partly in a recession period should ideally be treated differently than years where all 12 months are part of the recession. Also, how should statistical accuracy (given that more data points become available when working with multi-decade time series) be reconciled with managerial relevance (given that very distant recessions may be less informative/relevant)? In general, data at a lower aggregation level are preferable. At the same time, the data period should ideally cover multiple full BCs to improve the odds that the results are generalizable, and to avoid that the substantive findings are driven by idiosyncrasies of a single recession or expansion period. Clearly, both objectives may conflict, and further research is needed on the trade-off between them.
Temporary vs. permanent impact of BCs on marketing variables
Asymmetries in the cyclical patterns suggest that changes in the contraction are not always mirrored by opposite changes in the subsequent expansion. For example, in case of steepness asymmetry, it may take more time for performance to rebound than it took to drop in the contraction. Inspired by this idea, a number of studies have questioned whether all performance changes will eventually be reversed, or whether some of these changes will persist. Lamey et al. (2007), for instance, were the first to show empirically how expansions and contractions affect private-label shares to a different degree, and that the changes in a contraction are not just a temporary glitch. Once consumers switch to private labels to economize on their grocery expenditures, they learn about private-label quality. The increased quality of store brands over the years may positively surprise them, so that some consumers keep buying the cheaper private labels even when bad economic times are long over. Consequently, contractions tend to have a positive impact on private-label growth that is not fully offset in the subsequent expansion. This leaves permanent “scars” on national brands’ performance. Lamey (2014) extends these results, and shows that part of the permanent switch to store brands is driven by consumers moving from traditional retailers to hard discounters during contractions. In those stores, consumers are forced to choose from a narrow assortment dominated by store brands.
Asymmetric growth induced by the BC is also found in consumer expenditures on insurances and gambling in Millet et al. (2012), and in the context of tourism and healthcare spending in, respectively, Dekimpe et al. (2016) and Cleeren et al. (2015). All three studies provide evidence that cyclical adjustments in spending are not just a temporary phenomenon, but also influence the underlying long-term growth pattern in the performance series at hand.
Given that only few studies have considered the differential long-run implications of recession-induced cut-backs, numerous research questions remain, such as: How long will such cut-backs in R&D spending affect the future innovativeness in different categories? Once advertising budgets have been switched to more flexible online media, can more traditional media win the lost contracts back? Once cheaper alternatives (whether budget private labels or cheaper versions of well-known national brands) have gained acceptance among certain consumer segments, should they keep a similar shelf presence after the crisis, or can (should) this be gradually reduced? And if so, how fast? Also, is the size of the permanent effects related primarily to the length of the preceding recession, or more to its depth?