CC BY
147
Copyright © 2020 by Academic Publishing House Researcher s.r.o.
* TT
I
Published in the Slovak Republic Media Education (Mediaobrazovanie) Has been issued since 2005 ISSN 1994-4160 E-ISSN 1994-4195 2020, 60(1): 154-165
DOI: 10.13187/me.2020.1.154 www.ejournal53.com
Television Advertising in the Multiscreen and Multitasking Age: Does it Work for Millennials?
Andrej Miklosik a , *, Peter Starchon a , Nina Evans b
a Comenius University in Bratislava, Slovak Republic b University of South Australia, Australia
Abstract
Advertisers are challenged to respond to changing behaviour of consumers who become more technologically literate and develop patterns to avoid commercials. Millennials, especially, multitask, use multiple screens to consume content and use technologies to search for information. In this researchfocus groups were conducted with 241 respondents, to study changes in information searches regarding future purchases. Masters' students were selected as participants for the research and their response to being exposed to television advertising was studied and their information search process was charted. Consequences of the changing behaviour for advertisers include the need to consider search visibility for general and campaign-related keywords as a cornerstone of their communication campaigns. This research contributes to theory by enhancing the existing perception of integrated marketing communication (IMC) with the central role of search visibility in connecting various communication channels. By reflecting on the charted process of active information research, advertisers can make their TV campaigns work for millennial consumers who use online search and various devices in the process following the TV spot consumption.
Keywords: integrated marketing communication (IMC), search engine marketing, search visibility, television (TV) advertising.
1. Introduction
With the abundance of quickly and easily accessible information resources, the information search behaviour of consumers regarding their future purchase changes dramatically. Consumers spend more time online, which is reflected in the way theyuse search engines, social media, review sites, and other resources as part of their purchasingdecision-making process. The consumption patterns of advertising have also developed over the past decade. Consumers have become resistant to most of the commonly used forms of both offline and online advertising. As W. Wilkie (Wilkie, 2005) suggests, the reason lies in the overload of conflicting advice regarding alternatives to select. Consumers tend to ignore advertisements, switch channels while watching television, use advertising-free televisionand use ad-blockers to block online advertisements. Fewer people watch regular television, as many of them are switching to online streaming services. Consumersalso tend to divide their attention between multiple devices and screens. While watching a movie, they are active on social media or browse websites. This attention shift towards the second screen
* Corresponding author
E-mail addresses: andrej.miklosik@fm.uniba.sk (A. Miklosik)
(e.g. mobile phone, laptop) duringthe advertisement breaks impacts the efficiency of the communicated message.
The situation described above prompts many questions: What is the implication of this changing consumer behaviour fortelevision advertisers? How does it affect the efficacy of their campaigns and what should be done to increase it? Can anything be done at all or does this mean that classical television advertising is dying slowly? These and other questions will be addressed in this article by providing insights into the consumption patterns of television advertising and the response of millennials to television advertisements. This will be done by examining the process of consumer response to televisionadvertising and identifying the factors that significantly contribute to ensuring that the advertised product or brand isconsidered and that the advertising hopefully leads towards the desired purchasing decision.
2. Materials and methods
Research gap. Literature on consumer behaviour, specifically focusing on the purchasing decision-making process and consumption of media by young people (millennials) confirms that millennials are reducing their consumption of traditional media, they avoid commercials due to the overload of information and theyoften multitask while watching television. The availability and use of mobile devices and other screens while watching television enable them to search for products or brands featured in the advertisements. The implications for businesses' approach to marketing are already intensely discussed by practitioners (as confirmed by the cited web studies and reports), with academic research falling behind. Previous empirical research aimed to analyse the connection between television advertising and online search by looking at the search spikes. There is a need and opportunity to compare/contrast/extend?? these findings with the opinions and perceptions of digital natives to understand their behaviour and the motives and triggers for their actions. Thus, the aim ofthe research was: to understand television advertising consumption patterns of millennial consumers, outline the triggers, process and resources used within the follow-up information research process and to explain the implications for both practitioners and theory concepts.
Research methods. This research offers insights and improved understanding of consumer behaviour and response to television advertising. Millennials, the digital natives who represent a significant group of consumers with increasing purchasing power in future, are especially targeted. The method of conducting focus groups was chosen to understand how millennials consume and respond to television advertisements.
Masters' students in their first and second year of study at the University of Economics in Bratislava, Slovakiawere selected as participants for the focus group session. The sample selection was purposive and included millennials that are soon to complete their higher education. More than 95 % of the students were born between 1993 and 1998. The focus groups took place at two locations, namely the educational centre in Virt, Slovakia and the Faculty of Commerce, located on the Bratislava campus. Five blocks of focus group sessions were organised, with a total number of 241 participants: 1) 23-25 February 2018, 55 participants; 2) 22-24 March 2018, 58 participants; 3) 24-26 April 2019, 54 participants; 4) 27 April 2019, 24 participants; 5) 28-30 April 2019, 50 participants.
Four to eight focus groups were organised within each block. Each focus group consisted of five to ten participants. The duration of eachsession was between 60 and 90 minutes. Participants were instructed by the facilitator regarding the process of the focus group.They were encouraged to speak openly andshare their true individual opinions. These opinions would later be discussed and further developed within the moderated discussion, without any judgment by other participants or the facilitator. Individual responses and opinions were shared within the group, with other participants had the opportunity to respond. In the first part, participants were asked to think of a recent purchase and elaborate on the steps preceding the purchase. The discussion covered topics such as information resources used, offline versus online search, reference groups, the role of word of mouth, etc. It continued with topics such as multitasking, parallel and sequential use of multiple screens, online search and its process, as well as questions about the cross-session and cross-device search.
The second part of the session examined responses to television advertisement as part of the purchasing decision-making process. Participants were asked to consider their relationship towards advertising, specifically, television advertising and recall a recent situation where a television advertisement captured their attention. They were given time to think through the
sequence of actions that followed in response to the advertisement. The steps taken, their logical sequence, and timing were captured and then discussed within the group. Participants were able to reflect on their process, add missing parts if another participant's answer triggered the recollection of events. The focus was on the behaviour of participants, including the intensity (time, costs involved), thoroughness of information research, media and resources used, parties involved, offline and online points of sale visited etc.
The information was captured, coded, organised into logical groups and further analysed by the researchers. Following the data collection and processing, the research results and the implications for theory and practice were formulated.
3. Discussion
The information research process and sources of information
With almost unlimited interaction options in the pre-purchase, purchase, and post purchase phases, customer journeys become increasingly challenging to understand and influence (Van Bruggen et al., 2010). The pre-purchase phase, including information research, is perceived as the first step of the omnichannel customer journey (Barwitz, Maas, 2018). Searching for information is considered an important sub-process of decision making; in fact, it is one of the key research areas in the field of consumer behaviour (Utkarsh et al., 2019). In some sources, it is referred to as pre-decisional information search (Gigerenzer, 2003; Lindow, Betsch, 2019).
In relation to future purchases, information research represents a crucial step in the decision-making process. Consumers can undertake numerous activities before they make their final purchase decision, seeking content from different retailers and asking for social validation of their decision from their social networks both online and offline (Hall et al., 2017). Decision options (product alternatives) are characterised by multiple information dimensions such as price, location, features, reviews, delivery time, etc. To be able to weigh the alternatives and decide for a brand/product, values for these dimensions need to be gathered. A large amount of information, from a range of different offline online sources,is available to consumers.Brands and retailers need to understand the customer decision journey and their behaviours across all the devices and channels that are used (Hall et al., 2017).
Online information research saves time and often substitutes for time spent talking to people or visiting physical premises (Ratchford et al., 2007). Search engines, along with high street shops, shops where a similar item was purchased, shopping websitesor reference groups (partner, friends, family, colleagues) were identified as places where shoppers often look for purchasing ideas and inspiration (Hall et al., 2017). Search engines are used by consumers to locate and access various online sources of information, with daily search volumes increasing to 3.5 billion per day (Internet Live Stats, 2019). Researchers are looking at the response of users to paid search results, organic search results and their choices, trying to understand the whole information research process and decision within it (Agarwal et al., 2015; Kritzinger, Weideman, 2013; Kritzinger, Weideman, 2017; Park, Agarwal, 2018; Smith, 2016; Yang et al., 2018).
The term cross-sessional search is used to refer to search endeavours longer than one session (Kotov et al., 2011; Wang et al., 2013). Cross-device search refers to consumers using multiple devices in the information research process (Han et al., 2015; Wu et al., 2018). Trial and error behaviours that lead to accomplishment of the research task, or the intent shift directing the consumer towards an unexpected and unintended territory, are important for understanding the search patterns and behaviours (Chen et al., 2018). On top of functioning as a gate allowing access to information available on third-party websites, search engines can also provide information from their own database. Semantic web search engines such Google Knowledge Graph or Bing Satori are used to display instant answers to commonly asked questions, saving the consumer the need to leave the search to get the answer(Uyar, Aliyu, 2015). In connection with this, voice search that is increasingly used by users, changes the way search queries are entered and results presented (Guy, 2018). Search behaviour can be linked to purchases through the correlation between internet searches and product purchases (Jun, Park, 2016).
Consumers will pursue the information research differently. Their behaviour depends on their age, location, individual characteristics, preferences, previous experience, etc. Despite the individual character of this process, consumers can be categorised into certain groups. These consumer archetypes affect the purchasing decision-making process related outcomes such as number of cycles, duration, number of evaluated alternatives, and number of criteria considered
(Karimi et al., 2018). The commonly used criteria to form groups are social class or geographic location (Sessa et al., 2007), web experience characteristics (Frambach et al., 2003), motivational drives and personality traits (Morrison et al., 2013), subjective knowledge,and the style of decisionmaking (Brucks, 1985; Karimi et al., 2015). Utkarsh, S. Sangwan and P. Agarwal (Utkarsh et al., 2019) also studied subjective knowledge and its influence on information search behaviour. The level of stimulation and motivation represents another factor affecting the intensity of information search efforts (Utkarsh, 2017). On top of above-mentioned consumer categories, date of birth is often used to refer to consumers born after or between certain years. Millennials, people born between 1980 and 2000 (the 'from' years vary from late 1970s to early 1980s in the literature), are also referred to as "generation M", multi-taskers (Woempner, 2007) or iGeneration (Keengwe et al., 2014). Generation Y (people born after 1980) and generation Z (people born after 1990) represent subsegments of millennials. As M. Moore (Moor, 2012) suggests, differences can be spotted between Generation Y and Generation Z, with Generation Z being more individualistic, tech-savvy, always connected, are brand aware and more communicative than Generation Y. Millennials have lived during the era of rapid technological changes and a highly interconnected global world (Keengwe et al., 2014). They live in the moment and obtain information instantly, at their fingers, 24-7 (Rainer, Rainer, 2011). This generation is highly digitally literate and naturally uses technologies to perform daily tasks.
Response to television advertisements
In 2012, annual spending on television advertising was estimated at over $ 200 billion globally,more than twice the size of the entire market for online advertising (Lewis, Reiley, 2013). Since then, digital media, such as web, e-mail, and mobile have continuedto increase their share on marketing budgets at the expense of traditional media. The research study by eMarketer anticipates that in 2019, the advertising spending on digital media will outrank traditional media on the U.S. market, with the growth of digital media to continue in the following years (Ha, 2019). Nielsen (Nielsen, 2015) describes the situation as "screen wars", i.e. the battle for "eye space" in the TV-everywhere world. One of their findings is that 65 % of respondents prefer to watch video programming live, at its regularly scheduled time. The report also states that "watching TV in a linear fashion is changing for man, as we are now in more control of what we watch, when we watch and where we watch" (Nielsen, 2015). Despite these tendencies, television advertising continues to represent an enormous market and television is widely included in the communication mix of (especially) bigger brands. In Slovakia, for example, TV advertising spending has increased between 2016 and 2019 and is expected to reach €162 million in 2019 (Kienast, 2019).
To effectively reach their target audience during the decision-making process, advertisers need to understand the expectations of consumers regarding communications by brands. Consumers expect more real-time communications, instant responses, more open, and less formal interactions between them and brands (Tuten, Solomon, 2014). The more consumers connect to multiple devices, the more their level of consumption of traditional media is reduced (Kemp, 2016). Therefore, it is crucial to understand the behaviour of people consuming the television content, not to waste the opportunities and marketing budgets for television advertising. Millennial consumers are known to have a high propensity to use multiple online devices as digital natives connectedin the retail environment (Kirk et al., 2015).The frequent multitasking of consumers, especially while viewing television (Du et al., 2017) facilitated the existence of deep connections between the online and offline media worlds. The study of Google (2012) investigated how consumer use multiple devices to search for information and accomplish tasks. While they consume media, their attention is often shared between multiple devices, referred to as parallel media consumption or parallel use of screens (Google, 2012). The implications for the consumption of television advertising are apparent: consumers often use mobile phones, laptops or tablets while watching television (Lewis, Reiley, 2013).
A positive correlation between the intensity of searching for certain topics measured by search volumes and television advertising has been observed. In an early study, which evaluated data suggesting that simultaneous use of TVs and PCs, D. Zigmond and H. Stipp (Zigmond, Stipp, 2010) revealed an important effect of television advertising, namelytaking an immediate step to obtain more product information. In the study of M. Joo, K.C. Wilbur, BCowgill and Y. Zhu (Joo et al., 2014), a significant effect of television advertisements for financial services on brand searches and a smaller effect on category searches was confirmed. The research of R.A. Lewis and
D.H. Reiley (Lewis, Reiley, 2013) also revealed positive impact of advertising for a range of consumer products on online search.With the use of high-frequency search data that is readily accessible for all major advertised brands, they proved the existence of causal impact of TV advertising on consumer searches for the advertised brands. R.Y. Du, L. Xu, and K.C. Wilbur (Du et al., 2017) looked deeper into this relationship and confirmed that search spikes vary with television ad content: they are larger after brand focused advertisements than after price-focused ads, and after less-informative ads than after TV ads. They also found out that television advertisements generate post-ad searches for competitor brands. J. Liaukonyte, T. Teixeira, and K.C. Wilbur (Liaukonyte et al., 2015) looked at the response of online shopping to television advertising. They investigated how those effects depend on the characteristics of the advertisement, such as its content and media placement.
Integrated marketing communication
The extensive deployment and application of digital technologies resulted in the explosion of data regarding customers' buying behaviour (Candelo, 2019). Thanks to analytical tools driven by machine learning, marketers can now, also extract meaningful information from large data sets, leading to more precise analysis and understanding of consumer (Miklosik et al., 2019). One of the challenges marketers face is measuring the impact of television advertising. Refinement of the measurements can be achieved through TV attribution, assisting in accurately reporting on the impact TV ads have on their audience's behaviour online. This is done through incremental, minute-by-minute measurements and machine-learning driven analysis of search activity mapped to television advertisements (Tantot, 2017). Because of the proven impact of TV spots on the search volumes of relevant keywords, companies are realising that search visibility for these terms should be ensured to reflect the changing consumer behaviour. The report of Forrester Research (2017) claims that, because of this relationship, search is a key amplifier of other marketing channels. Companies are advised to prioritise search to maximise their marketing return on investment (Forrester Research, 2017). A. Miklosik (Miklosik, 2014) argues that search visibility represents the core of modern communication campaigns. He uses the term 'search-centric marketing' to refer to marketing that reflects the role of search visibility in achieving the efficacy of communication campaigns and the flow of the communication message through all the channels. Pay per click campaigns can be used to quickly appear at the top of search results to support TV advertising and its efficacy (Howard, 2017). Search engine optimisation represents another tool for achieving search visibility, focusing on understanding search engine algorithms in order to improve organic search rankings (Miklosik et al., 2019).
Enhanced understanding of the consumer decision journey requires integrated marketing communication (IMC) programs reflecting the way in which traditional and new media (e.g., search, display, mobile, TV, and social media) interact to affect consumer decision making (Batra, Keller, 2016). However, most researchers regard IMC as a concept where the brand identity is preserved through all the channels used in the communication mix (Laurie, Mortimer, 2019). Thus, IMC can be defined as an approach to brand communications where different channels and tools work together to create a seamless experience for the customer and present them with a similar tone and style that reinforces the brand's core message (Csikosova et al., 2014). O. Duralia (Duralia, 2018) emphasises the need to recognise the variety of channels their perception by consumers and also to utilise their efficient combination and integration. X. Dong and H. Li (Dong, Li, 2018) argue that it is necessary to understand the optimal media sequence for different types of products to achieve the desired effect of persuasion. L. Porcu, S. del Barrio-García, J.M. Alcántara-Pilar and E. Crespo-Almendros (Porcu et al., 2019) analysed the influence of integrated marketing and IMC on sales and financial results, indicating that they lead to greater brand advantage and desired customer-related outcomes. The work of O.O.E. Mihaela (Mihaela, 2015) also stresses the consistency of the message transmitted through a mix of communication tools such as advertising, sales promotion, direct marketing, public relations, online communication, etc; however, it also elaborates on its impact on consumer buying behaviour.
4. Results
Responses and discussions with participants confirmed that millennial consumers regard mobile devices and technology as an integral part of their purchasing decision making. A significant part of the purchasing process takes place online. It typically consists of more than one step andincludes online search through a search engine.
Most of the participants, when initially asked to recall a recent purchasing situation, would select one that resulted in an online purchase. The process of information search includedmostly electronic channels. The details of the process varied from case to case, although they had a few characteristics in common: 1) Consumers would start their purchasing journey on a website of a brand they are already familiar with and access it either directly or through a search engine;
2) They would consider multiple options before choosing a product that best matches their requirements; 3) To find more options, fulltext search was the most commonly used tool to navigate the internet; 4) Online reviews were readto determinewhether the product is of a certain quality and should be considered; 5) Depending on the product, participants frequently visit a physical point of sale to touch and feel the product; 6) In a majority of cases, the product was finally purchased online, despite the visit to the bricks and mortar store; 7) To select the retailer to purchase from, prices were compared online, mostly by searching in Google and accessing specialised price comparison sites; 8) Reviews of the particular retailer were also considered before finalising the purchase.
The discussion regarding the consumption and perception of television advertising revealed that many consumers do not watch classical television anymore. Those participants prefer using streaming services to access video content they desire when choosing items. Four respondents (1.66 %) said they avoid watching video content in the form of movies or series. However, they would consume other ad-hoc, usually shorter, video content online, usually on social media. Respondents who watch traditional television responded that they intentionally avoid advertisements. If they do not watch an advertisement-free television program, they either switch channels, leave the room, or switch the focus to another activity, usually a second device (screen), a conversation with their partner etc. If possible, they also like to record TV programs and watch them later by using the feature of fast-forwarding through the advertisements.
It took the focus group participants some time and effort to recall a television advertisement they have recently seen thatstimulated them to actively search for information. In some cases, respondents could only recall an advertisement that did not result in any consecutive action. In the end, focus group facilitators were able to discuss this topic with 227 out of 241 participants (94.19%). The discussion was then directed towards the identification of the steps taken by participantsafter seeing the advertisement. In some cases, this process resulted in a product purchase (this could be physical goods, subscription, service such as a student bank account etc.), while in others, it was stopped before this final stage. In 215 out of 227 cases (94.71 %), consumers used online search to locate a website that contained the desired information. This could be the website of the advertisers (corporate website), product landing page, price and feature comparison site, review site, blog site etc. The research did not specifically focus on the type of search engine used, but based on the answersit was mostly Google, quite often mentioned as a synonym for the term search engine. Phrases like 'I googled ...' or 'By entering the keyword into google...' were used to refer to a search engine. In the majority of cases (195 out of 215 or 90.70 %), searching for a term related to the TV spot was the first step of the process. Also, online search was not a one-off action that would return expected results after the first try. Consumers were searchingrepetitively, by adjusting the keywords to improve the results until they were satisfied with them. They used many different combinations and sequences of keywords which would often include specific words used in the TV spot. They would use multiple search sessions, with other steps in-between. The process can also last several hours or even days.
Two different types of information searchesthat follow television advertising were identified: 1) A search that ended before the actual purchase (189 out of 227 cases - 83.26 %); 2) A process that was completely finalised by purchasing a product (38 out of 227 cases - 16.74 %). The main factors identified as decisive for the final decision regarding the purchase were: a) Whether the need was intense enough to proceed with the purchase; 2) Product value/cost;
3) Results/information found. In the first scenario, the information research was usually much shorter. The research was either abandoned at a very early stage after finding that the product does not meet the requirements, it is not interesting at all, is too pricey etc. It was also not uncommon for the lack of drive and other inputs to cause ceasing of the search process. In a few cases, however, it was also noted that the reason for not continuing was that the product or brand could not be found online when searching for related keywords.
The cases that resulted in the purchase of a product usually included a more thorough research. On top of using online sources, offline resources were sometimes used. This was,
however, usually the case for more expensive products. Mobile phones or smart watches can be mentioned as examples. In these cases, respondents would also consult with their peers (often) or family members (rare) for advice and experiences either with the brand, operating system or the specific product. Another finding was particularly interesting: in more than half of these cases (22 out of 38 - 57.89 %), the product purchased was different from the one advertised.Adifferent brand or manufacturer was selected.
The underlying process of responding to television advertising differed from one case to another. However, based on the discussions, a general process chart could be constructed reflecting the steps, decisions and triggers that most frequentlyoccur within the information search process that was initiated as a direct response to a television advertisement. The processis visualised in Fig. 1.
Fig. 1. Process of active information research as a response to television advertising
Consumers arebecoming more technically literate, with younger consumers being digital natives. This brings changes to their purchasing decision-making process. Young people use multiple screens to access information, they multitask, their information research is predominantly online, and they use search engines to access the information they look for.
For advertisers engaged in traditional television advertising it is therefore necessary to be aware of the current trends in television consumption, response to advertisements, and consumer behaviourto reflect this shift in their communication strategies. Multiple trajectories are used by consumers to navigate to the final decision. To retain the prospective customers once their
attention has been captured by an advertisement, it is necessary to ensure that the brand and product are present and sufficiently visible in the variety of consumer touch points.
Literature and the focus group sessions confirmed that, for a brand to be further considered, it is of paramount importance to be visible in search engines. Searching online is the preferred way of locating and accessing new information resources. Most of the consumers would use search even to locate a website that they know they want to access. The implications are already being widely discussed and partially implemented by practitioners who realised that search visibility is the cornerstone of an integrated communication (and marketing) strategy. The research results presented in this paper further confirm previous findings and extend them by providing more insights into the information research phase that follows the consumption of television advertising.
It is crucial for television advertisers to include search engine marketing strategies into their communication campaigns. Advertised brands and products need to be visible for a large scale of keywords, both general ones and those directly related to messages contained in the television spot. Some companies already use the bridge connecting the offline and online world by advising the consumer to 'search for ...' in response to the advertisement. However, advertisers need to be aware that the variety of keywords entered by prospective customers can be much broader than they initially expect. Thus, keywords that closely relate to the advertised message need to be included in the strategy to ensure that the landing page of the advertiser is displayed at the top of search results. The combination of pay per click and search engine optimisation is the best way to ensure required search visibility.
Adopting this approach and putting search visibility first in any type of communication campaign can positively affectitsefficiency. As a result, the chance that the consumer does not purchase the product from the advertiser andselect a competitor brand, willbe minimised. By not being visible and present in search results when consumers actively search for a brand and more information on products advertised, companies are 'actively steering' these users towards their competitors. There is another implication of the awareness of these processes and underlying consumer behaviour;Apart from optimising their own campaigns, advertisers and their agencies can actively target keywords used in communication campaigns (including television) of their competitors. By optimising the search visibility for these, within ethical guidelines, they can create suggestions and inputs in search results offered to active searchers who will eventually stumble upon them. This can redirect the attention of the searching usersaway from the competitor's product towards a brand theywould not previouslyconsider buying.
This approach can be incorporated to extend the meaning and understanding of the already frequently used concept of IMC. The existing understanding was mostly (as indicated in the literature review) building on the coherence of the communication message across multiple channels to ensure that an integrated, unified message is pushed through all the different communication channels and consumer touch points with the brand. However, because of the documented importance and role of search visibility in this process, it can be argued that search and search visibility represent the cornerstone of IMC. Decisions of the users depend on the search results they see while collecting information from online resources. Search visibility thus stands in the middle of the information research process (Fig. 1). Through ensuring sufficient search visibility with the brand and product occupying top positions in search results for a whole variety of campaign-related keywords, brands ensure that they remain visible and considered through the whole purchasing decision-making process. Search results and search visibility thus also connect the offline and online components or channels of the communication campaign, making them work together and synchronised towards a common goal. With this approach, television advertising can still be effective as the initial trigger of the purchasing process.
5. Conclusion
Opportunities exist for future research in connection to the presented results. Firstly, the identified issues can be further confirmed and expanded by performing a quantitative research study with standardised set of questions addressed to millennial consumers. This can provide more insights on the information search process following the consumption of television advertisements. The questions could, for example, include details of the cross-session and cross-device search, depth of search results they consider while searchingfor the factors affecting the commitment and intensity of information search. Secondly, understanding other consumer groups is also vital to building up the whole concept of integrated marketing communication. Other segments of
consumers (e.g. different age or typology) can be questioned to provide insights regarding their preferences and consumption of television advertising and the follow-up actions. Thirdly, more respondents from other countries can be included in the research, using the same or extended methodology, to enable a comparison between various nations or geographical markets. Lastly, a follow-up empirical study could show how advertisers embrace this new situation. This study could investigate whether their communication campaigns are already encompassing the required search visibility and to what extent their campaigns, including television advertising, can be described as fully integrated.
6. Acknowledgements
This paper originated as the result of working on the grant scheme KEGA 016EU-4/2019 Innovative learning texts from marketing for secondary schools.
References
Agarwal et al., 2015 - Agarwal, A., Hosanagar, K., Smith, M.D. (2015). Do organic results help or hurt sponsored search performance? Information Systems Research, 26 (4): 695-713. DOI: https://doi.org/10.1287/isre.2015.0593
Barwitz, Maas, 2018 - Barwitz, N., Maas, P. (2018). Understanding the omnichannel customer journey: determinants of interaction choice. Journal of Interactive Marketing, 43: 116133. DOI: https://doi.org/10.1016/j.intmar.2018.02.001
Batra, Keller, 2016 - Batra, R., Keller, K.L. (2016). Integrating marketing communications: new findings, new lessons, and new ideas. Journal of Marketing, 80(6): 122-145. DOI: https://doi.org/10.1509/jm.15.0419
Brucks, 1985 - Brucks, M. (1985). The Effects of product class knowledge on information search behavior. Journal of Consumer Research, 12. DOI: https://doi.org/10.1086/209031
Candelo, 2019 - Candelo, E. (2019). Digital technologies are rewriting the old rules of marketing bt - marketing innovations in the automotive industry: meeting the challenges of the digital age. In E. Candelo (Ed.). Cham: Springer International Publishing: 141-154. DOI: https://doi.org/10.1007/978-3-030-15999-3_17
Chen et al., 2018 - Chen, J., Liu, Y., Luo, C., Mao, J., Zhang, M., Ma, S. (2018). Improving session search performance with a Multi-MDP Model. In Y.-H. Tseng et al. (Eds.). Information Retrieval Technology. Cham: Springer International Publishing: 45-59.
Csikosova et al., 2014 - Csikosova, A., Antosova, M, Culkova, K. (2014). Strategy in direct and interactive marketing and integrated marketing communications. Procedia - Social and Behavioral Sciences, 116: 1615-1619. DOI: https://doi.org/10.1016/j.sbspro. 2014.01.444
Dong, Li, 2018 - Dong, X., Li, H. (2018). Does online media sequence matter in product marketing? Electronic Commerce Research and Applications, 28: 44-53. DOI: https://doi.org/ 10.1016/j.elerap.2018.01.010
Du et al., 2017 - Du, R.Y., Xu, L., Wilbur, K.C. (2017). TV ads and search spikes: toward a deeper understanding. [Electronic resource]. URL: https://sics.haas.berkeley.edu/ pdf_2017/paper_dxw.pdf
Duralia, 2018 - Duralia, O. (2018). Integrated marketing communication and its impact on consumer behavior. Studies in Business and Economics, 13 (2): 92-102. DOI: https://doi.org/10.2478/sbe-2018-0022
Forrester Research, 2017 - Forrester Research. (2017). Prioritize search to maximize the ROI of marketing: search is a key amplifier of other marketing channels. [Electronic resource]. URL: https://www.catalystdigital.com/wp-content/uploads/Prioritize-Search-To-Maximize-The-ROI-Of-Marketing.pdf
Frambach et al., 2003 - Frambach, R.T., Prabhu, J., Verhallen, T.M.M. (2003). The influence of business strategy on new product activity: The role of market orientation. International Journal of Research in Marketing, 20 (4): 377-397. DOI: https://doi.org/10.1016/ j.ijresmar.2003.03.003
Gigerenzer, 2003 - Gigerenzer, G. (2003). The Adaptive toolbox and lifespan development: common questions? BT - Understanding human development: dialogues with lifespan psychology. In U.M. Staudinger and U. Lindenberger (Eds.). Boston: Springer US: 423-435. DOI: https://doi.org/10.1007/978-1-4615-0357-6_20
Google, 2012 - Google (2012). The New multi-screen world study. [Electronic resource]. URL:
https: //www.thinkwithgoogle.com/ advertising-channels/mobile/the-new-multi-screen-world-study/
Guy, 2018 - Guy, I. (2018). The Characteristics of voice search: comparing spoken with typed-in mobile web search queries. ACM Transactions on Information Systems, 36 (3). DOI: https://doi.org/10.1145/3182163
Ha, 2019 - Ha, A. (2019). eMarketer predicts digital ads will overtake traditional spending in 2019. [Electronic resource]. URL: https://techcrunch.com/2019/02/20/emarketer-digital-ad-forecast/ Hall et al., 2017 - Hall, A., Towers, N, Shaw, D.R. (2017). Understanding how millennial shoppers decide what to buy: digitally connected unseen journeys. International Journal of Retail and Distribution Management, 45 (5): 498-517. DOI: https://doi.org/10.1108/IJRDM-11-2016-0206
Han et al., 2015 - Han, S., He, D., Yue, Z., Brusilovsky, P. (2015). Supporting cross-device web search with social navigation-based mobile touch interactions, 9146. DOI: https://doi.org/ 10.1007/978-3-319-20267-9_12
Howard, 2017 - Howard, J. (2017). How to use PPC to support TV advertising. [Electronic resource]. URL: https://www.periscopix.co.uk/blog/how-to-use-ppc-to-support-tv-advertising/
Internet Live Stats, 2019 - Internet Live Stats (2019). Google Search Statistics. [Electronic resource]. URL: http://www.internetlivestats.com/google-search-statistics/
Joo et al., 2014 - Joo, M, Wilbur, K. C, Cowgill, B, Zhu, Y. (2014). Television advertising and online search. Management Science, 60(1): 56-73. DOI: https://doi.org/10.1287/ mnsc.2013.1741
Jun et al., 2016 - Jun, S.-P., Park, D.-H. (2016). Consumer information search behavior and purchasing decisions: Empirical evidence from Korea. Technological Forecasting and Social Change, 107: 97-111. DOI: https://doi.org/https://doi.org/10.1016/j.techfore.2016.03.021
Karimi et al., 2018 - Karimi, S., Holland, C.P., Papamichail, K.N. (2018). The impact of consumer archetypes on online purchase decision-making processes and outcomes: A behavioural process perspective. Journal of Business Research, 91: 71-82. DOI: https://doi.org/10.1016/ j.jbusres.2018.05.038
Karimi et al., 2015 - Karimi, S., Papamichail, K.N., Holland, C.P. (2015). The effect of prior knowledge and decision-making style on the online purchase decision-making process: A typology of consumer shopping behaviour. Decision Support Systems, 77: 137-147. DOI: https://doi.org/ 10.1016/j.dss.2015.06.004
Keengwe et al., 2014 - Keengwe, J., Schnellert, G., Jonas, D. (2014). Mobile phones in education: Challenges and opportunities for learning. Education and Information Technologies, 19(2): 441-450. DOI: https://doi.org/10.1007/s10639-012-9235-7
Kemp, 2016 - Kemp, S. (2016). Digital in 2016 we are social's compendium of global digital, social, and mobile data, trends, and statistics. [Electronic resource]. URL: https://d1ri6y1vinkzt0. cloudfront.net/media/documents/WeAres Social Digital in 2016v02-160126235031.pdf
Kienast, 2019 - Kienast, K. (2019). Spending on advertising in the Slovak Republic from 2007 to 2019, by medium (in million euros). [Electronic resource]. URL: https://www.statista.com/statistics/491853/advertising-spend-by-medium-slovak-republic/
Kirk et al., 2015 - Kirk, C., Chiagouris, L, Lala Vishal, P., Thomas, J. (2015). How do digital natives and digital immigrants respond differently to interactivity online? a model for predicting consumer attitudes and intentions to use digital information products. Journal of Advertising Research, 55. DOI: https://doi.org/10.2501/JAR-55-1-000-000
Kotov et al., 2011 - Kotov, A., Bennett, P.N., White, R.W., Dumais, S.T., Teevan, J. (2011). Modeling and analysis of cross-session search tasks. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval.
Kritzinger, Weideman, 2013 - Kritzinger, W.T., Weideman, M. (2013). Search engine optimization and pay-per-click marketing strategies. Journal of Organizational Computing and Electronic Commerce, 23 (3): 273-286. DOI: https://doi.org/10.1080/10919392.2013.808124
Kritzinger, Weideman, 2017 - Kritzinger, W.T., Weideman, M. (2017). Parallel search engine optimisation and pay-per-click campaigns: A comparison of cost per acquisition. South African Journal of Information Management, 19 (1). DOI: https://doi.org/10.4102/sajim.v19i1.820
Laurie, Mortimer, 2019 - Laurie, S., Mortimer, K. (2019). How to achieve true integration: the impact of integrated marketing communication on the client/agency relationship. Journal of Marketing Management, 35 (3-4): 231-252. DOI: https://doi.org/10.1080/0267257X.2019.
1576755
Lewis, Reiley, 2013 - Lewis, RA.., Reiley, D.H. (2013). Down-to-the-minute effects of super bowl advertising on online search behavior. In Proceedings of the Fourteenth ACM Conference on Electronic Commerce. New York: ACM: 639-656. DOI: https://doi.org/10.1145/2492002.2482600 Liaukonyte et al., 2015 - Liaukonyte, J., Teixeira, T., Wilbur, K.C. (2015). Television advertising and online shopping. Marketing Science, 34 (3): 311-330. DOI: https://doi.org/ 10.1287/mksc.2014.0899
Lindow, Betsch,2019 - Lindow, S., Betsch, T. (2019). Children's adaptive decision making and the costs of information search. Journal of Applied Developmental Psychology, 60: 24-34. DOI: https://doi.org/10.1016/j.appdev.2018.09.006
Mihaela, 2015 - Mihaela, O.O.E. (2015). The Influence of the Integrated marketing communication on the consumer buying behaviour. Procedia Economics and Finance, 23: 14461450. DOI: https://doi.org/10.1016/S2212-5671(15)00446-3
Miklosik, 2014 - Miklosik, A. (2014). Improving positions in search engines as the centre of consumer-oriented marketing campaings. In Marketing identity. Explosion of innovations. Trnava: Faculty of Mass Media Communication, University of Ss. Cyril and Methodius in Trnava:
323-333.
Miklosik et al., 2019 - Miklosik, A., Evans, N., Zak, S., Lipianska, J. (2019). A framework for constructing optimisation models to increase the visibility of organizations' information in search engines. Information Research, 24 (1), paper 808. [Electronic resource]. URL: http://informationr.net/ir/24-1/paper808.html
Miklosik et al., 2019 - Miklosik, A., Kuchta, M., Evans, N., Zak, S. (2019). Towards the Adoption of Machine Learning-Based Analytical Tools in Digital Marketing. IEEE Access, 7 (1): 85705-85718.
Moore, 2012 - Moore, M. (2012). Interactive media usage among millennial consumers. Journal of Consumer Marketing, 29 (6): 436-444. DOI: https://doi.org/10.1108/ 07363761211259241
Morrison et al., 2013 - Morrison, M.A., Cheong, H.J., McMillan, S.J. (2013). Posting, lurking, and networking: behaviors and characteristics of consumers in the context of usergenerated content. Journal of Interactive Advertising, 13 (2): 97-108. DOI: https://doi.org/ 10.1080/15252019.2013.826552
Nielsen, 2015 - Nielsen, (2015). Sceen Wars: The battle for eye spance in a TV-everywhere world. [Electronic resource]. URL: https://www.nielsen.com/content/dam/corporate/us/en/ reports-downloads/2015-reports/nielsen-global-digital-landscape-report-march-2015.pdf
Park, Agarwal, 2018 - Park, C.H., Agarwal, M.K. (2018). The order effect of advertisers on consumer search behavior in sponsored search markets. Journal of Business Research, 84: 24-33. DOI: https://doi.org/10.1016/jjbusres.2017.11.003
Porcu et al., 2019 - Porcu, L., del Barrio-García, S., Alcántara-Pilar, J.M., Crespo-Almendros, E. (2019). Analyzing the influence of firm-wide integrated marketing communication on market performance in the hospitality industry. International Journal of Hospitality Management, 80: 13-24. DOI: https://doi.org/10.1016Zj.ijhm.2019.01.008
Rainer, Rainer, 2011 - Rainer, T.S., Rainer, J.W. (2011). The millennials: connecting to America's largest generation. B & H Publishing Group.
Ratchford et al., 2007 - Ratchford, B.T., Talukdar, D, Lee, M.-S. (2007). The impact of the Internet on consumers' use of information sources for automobiles: A re-inquiry. Journal of Consumer Research, 34 (1): 111-119. DOI: https://doi.org/10.1086/513052
Sessa et al., 2007 - Sessa, V.I., Kabacoff, R.I., Deal, J., Brown, H. (2007). Generational differences in leader values and leadership behaviors. The Psychologist-Manager Journal. Sessa, Valerie I.: Department of Psychology, Montclair State University, 237 Dixon Hall, Montclair, NJ, US: Taylor & Francis. DOI: https://doi.org/10.1080/10887150709336612
Smith, 2016 - Smith, A.D. (2016). Search engine marketing and customer relationship management within a social media environment. International Journal of Business Information Systems, 22 (2): 181-210. DOI: https://doi.org/10.1504/IJBIS.2016.076246
Tantot,2017 - Tantot, E. (2017). Tuning in to TV Attribution - How does it really work? [Electronic resource]. URL: https://www.periscopix.co.uk/blog/tuning-in-to-tv-attribution-how-does-it-really-work/
Tuten, Solomon, 2014 - Tuten, T.L., Solomon, M.R. (2014). Social Media Marketing, Pearson Education Inc., NJ. (Second). SAGE Publications Ltd.
Utkarsh, 2017 - Utkarsh (2017). Individual differences in consumer information search for services: A multiple mediation study. Journal of Retailing and Consumer Services, 37: 33-42. DOI: https://doi.org/10.1016/jjretconser.2017.02.015
Utkarsh et al., 2019 - Utkarsh, Sangwan, S., Agarwal, P. (2019). Effect of consumer self-confidence on information search and dissemination: Mediating role of subjective knowledge. International Journal of Consumer Studies, 43 (1): 46-57. https://doi.org/10.1111/ijcs.12482
Uyar, Aliyu, 2015 - Uyar, A., Aliyu, F.M. (2015). Evaluating search features of Google Knowledge Graph and Bing Satori Entity types, list searches and query interfaces. Online Information Review, 39 (2): 197-213. DOI: https://doi.org/10.1108/OIR-10-2014-0257
Van Bruggen et al., 2010 - Van Bruggen, G.H., Antia, K.D., Jap, S.D., Reinartz, W.J., Pallas, F. (2010). Managing marketing channel multiplicity. Journal of Service Research, 13 (3): 331-340. https://doi.org/10.1177/1094670510375601
Wang et al., 2013 - Wang, H., Song, Y., Chang, M.-W, He, X., White, R.W., Chu, W. (2013). Learning to extract cross-session search tasks. In Proceedings of the 22Nd International Conference on World Wide Web. New York: ACM: 1353-1364. DOI: https://doi.org/10.1145/2488388.2488507
Wilkie, 2005 - Wilkie, W. (2005). Needed: A Larger sense of marketing and scholarship. Journal of Marketing, 69:8-10.
Woempner, 2007 - Woempner, C. (2007). Teaching the next generation. Mid-Continent Research for Education and Learning: 1-5.
Wu et al., 2018 - Wu, D., Dong, J., Tang, Y. (2018). Identifying and modeling information resumption behaviors in cross-device search. In ACM/SIGIR Proceedings 2018. NY: Assoc Computing Machinery: 1189-1192. DOI: https://doi.org/10.1145/3209978.3210126
Yang et al., 2018 - Yang, S., Li, D., Tao, Z., Li, X. (2018). Search engine advertising for organic food: The effectiveness of information concreteness on advertising performance. Journal of Consumer Behaviour, 17 (1): 47-56. DOI: https://doi.org/10.1002/cb.1685
Zigmond, Stipp, 2010 - Zigmond, D., Stipp, H. (2010). Assessing a new advertising effect. Journal of Advertising Research, 50 (2): 162-168. DOI: https://doi.org/10.2501/S00218 49910091324
