Market-level information and the diffusion of competing technologies:an exploratory analysis of the LAN industry

Market-level information diffused by print media may contribute to the legitimation of an emerging technology and thus influence the diffusion of competing technological standards. After analyzing more than 10,000 trade media abstracts from the Local Area Networks (LAN) industry published between 1981 and 2000, we found the presence of differential effects on the adoption of competing standards by two market-level information types: technology and product availability. The significance of these effects depends on the technology's order of entry and suggests that high-tech product managers should make strategic use of market-level information by appropriately focusing the content of their communications. © 2007 Elsevier B.V. All rights reserved

Alcohol marketing research: the need for a new agenda

Aims: This paper aims to contribute to a rethink of marketing research priorities to address policy makers' evidence needs in relation to alcohol marketing. Method: Discussion paper reviewing evidence gaps identified during an appraisal of policy options to restrict alcohol marketing. Findings: Evidence requirements can be categorized as follows: (i) the size of marketing effects for the whole population and for policy-relevant population subgroups, (ii) the balance between immediate and long-term effects and the time lag, duration and cumulative build-up of effects and (iii) comparative effects of partial versus comprehensive marketing restrictions on consumption and harm. These knowledge gaps impede the appraisal and evaluation of existing and new interventions, because without understanding the size and timing of expected effects, researchers may choose inadequate time-frames, samples or sample sizes. To date, research has tended to rely on simplified models of marketing and has focused disproportionately on youth populations. The effects of cumulative exposure across multiple marketing channels, targeting of messages at certain population groups and indirect effects of advertising on consumption remain unclear. Conclusion: It is essential that studies into marketing effect sizes are geared towards informing policy decision-makers, anchored strongly in theory, use measures of effect that are well-justified and recognize fully the complexities of alcohol marketing efforts

Large UK retailers' initiatives to reduce consumers' emissions: a systematic assessment

In the interest of climate change mitigation, policy makers, businesses and non-governmental organisations have devised initiatives designed to reduce in-use emissions whilst, at the same time, the number of energy-consuming products in homes, and household energy consumption, is increasing. Retailers are important because they are at the interface between manufacturers of products and consumers and they supply the vast majority of consumer goods in developed countries like the UK, including energy using products. Large retailers have a consistent history of corporate responsibility reporting and have included plans and actions to influence consumer emissions within them. This paper adapts two frameworks to use them for systematically assessing large retailers’ initiatives aimed at reducing consumers’ carbon emissions. The Framework for Strategic Sustainable Development (FSSD) is adapted and used to analyse the strategic scope and coherence of these initiatives in relation to the businesses’ sustainability strategies. The ISM ‘Individual Social Material’ framework is adapted and used to analyse how consumer behaviour change mechanisms are framed by retailers. These frameworks are used to analyse eighteen initiatives designed to reduce consumer emissions from eight of the largest UK retail businesses, identified from publicly available data. The results of the eighteen initiatives analysed show that the vast majority were not well planned nor were they strategically coherent. Secondly, most of these specific initiatives relied solely on providing information to consumers and thus deployed a rather narrow range of consumer behaviour change mechanisms. The research concludes that leaders of retail businesses and policy makers could use the FSSD to ensure processes, and measurements are comprehensive and integrated, in order to increase the materiality and impact of their initiatives to reduce consumer emissions in use. Furthermore, retailers could benefit from exploring different models of behaviour change from the ISM framework in order to access a wider set of tools for transformative system change

Are You What You Read? Predicting Implicit Attitudes to Immigration Based on Linguistic Distributional Cues From Newspaper Readership; A Pre-registered Study

The implicit association test (IAT) measures bias towards often controversial topics (e.g., race, religion), while newspapers typically take strong positive/negative stances on such issues. In a pre-registered study, we developed and administered an immigration IAT to readers of the Daily Mail (a typically anti-immigration publication) and the Guardian (a typically pro-immigration publication) newspapers. IAT materials were constructed based on co-occurrence frequencies from each newspapers' website for immigration-related terms (migrant/immigrant) and positive/negative attributes (skilled/unskilled). Target stimuli showed stronger negative associations with immigration concepts in the Daily Mail compared to the Guardian, and stronger positive associations in the Guardian corpus compared to the Daily Mail corpus. Consistent with these linguistic distributional differences, Daily Mail readers exhibited a larger IAT bias, revealing stronger negative associations to immigration concepts compared to Guardian readers. This difference in overall bias was not fully explained by other variables, and raises the possibility that exposure to biased language contributes to biased implicit attitudes

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

[EN] The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.This work has been supported by the Heineken Endowed Chair in Neuromarketing at the Polytechnic University of Valencia in order to research and apply new technologies and neuroscience in communication, distribution and consumption fields.Guixeres Provinciale, J.; Bigné-Alcañiz, E.; Ausin-Azofra, JM.; Alcañiz Raya, ML.; Colomer, A.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V. (2017). Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising. Frontiers in Psychology. 8:1-14. https://doi.org/10.3389/fpsyg.2017.01808S1148Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., & Suri, J. S. (2006). Heart rate variability: a review. Medical & Biological Engineering & Computing, 44(12), 1031-1051. doi:10.1007/s11517-006-0119-0Aftanas, L. I., Reva, N. V., Varlamov, A. A., Pavlov, S. V., & Makhnev, V. P. (2004). Analysis of Evoked EEG Synchronization and Desynchronization in Conditions of Emotional Activation in Humans: Temporal and Topographic Characteristics. Neuroscience and Behavioral Physiology, 34(8), 859-867. doi:10.1023/b:neab.0000038139.39812.ebAstolfi, L., De Vico Fallani, F., Cincotti, F., Mattia, D., Bianchi, L., Marciani, M. G., … Babiloni, F. (2008). Neural Basis for Brain Responses to TV Commercials: A High-Resolution EEG Study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(6), 522-531. doi:10.1109/tnsre.2008.2009784Astolfi, L., Fallani, F. D. V., Cincotti, F., Mattia, D., Bianchi, L., Marciani, M. G., … Babiloni, F. (2009). Brain activity during the memorization of visual scenes from TV commercials: An application of high resolution EEG and steady state somatosensory evoked potentials technologies. Journal of Physiology-Paris, 103(6), 333-341. doi:10.1016/j.jphysparis.2009.07.002Baack, D. W., Wilson, R. T., & Till, B. D. (2008). Creativity and Memory Effects: Recall, Recognition, and an Exploration of Nontraditional Media. Journal of Advertising, 37(4), 85-94. doi:10.2753/joa0091-3367370407Bellman, S., Murphy, J., Treleaven-Hassard, S., O’Farrell, J., Qiu, L., & Varan, D. (2013). Using Internet Behavior to Deliver Relevant Television Commercials. Journal of Interactive Marketing, 27(2), 130-140. doi:10.1016/j.intmar.2012.12.001Bigné, E. (2016). Frontiers in research in business: Will you be in? European Journal of Management and Business Economics, 25(3), 89-90. doi:10.1016/j.redeen.2016.09.001Bigné, E., Llinares, C., & Torrecilla, C. (2016). Elapsed time on first buying triggers brand choices within a category: A virtual reality-based study. Journal of Business Research, 69(4), 1423-1427. doi:10.1016/j.jbusres.2015.10.119Blanco-Velasco, M., Weng, B., & Barner, K. E. (2008). ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in Biology and Medicine, 38(1), 1-13. doi:10.1016/j.compbiomed.2007.06.003Boksem, M. A. S., & Smidts, A. (2015). Brain Responses to Movie Trailers Predict Individual Preferences for Movies and Their Population-Wide Commercial Success. Journal of Marketing Research, 52(4), 482-492. doi:10.1509/jmr.13.0572Bradley, M. M., Houbova, P., Miccoli, L., Costa, V. D., & Lang, P. J. (2011). Scan patterns when viewing natural scenes: Emotion, complexity, and repetition. Psychophysiology, 48(11), 1544-1553. doi:10.1111/j.1469-8986.2011.01223.xCastiglioni, P., & Di Rienzo, M. (s. f.). On the evaluation of heart rate spectra: the Lomb periodogram. Computers in Cardiology 1996. doi:10.1109/cic.1996.542584Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.-Y., & Moon, S. (2007). I tube, you tube, everybody tubes. Proceedings of the 7th ACM SIGCOMM conference on Internet measurement - IMC ’07. doi:10.1145/1298306.1298309Chen, L., Zhou, Y., & Chiu, D. M. (2014). A lifetime model of online video popularity. 2014 23rd International Conference on Computer Communication and Networks (ICCCN). doi:10.1109/icccn.2014.6911774Christoforou, C., Christou-Champi, S., Constantinidou, F., & Theodorou, M. (2015). From the eyes and the heart: a novel eye-gaze metric that predicts video preferences of a large audience. Frontiers in Psychology, 6. doi:10.3389/fpsyg.2015.00579Colomer Granero, A., Fuentes-Hurtado, F., Naranjo Ornedo, V., Guixeres Provinciale, J., Ausín, J. M., & Alcañiz Raya, M. (2016). A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Frontiers in Computational Neuroscience, 10. doi:10.3389/fncom.2016.00074Couwenberg, L. E., Boksem, M. A. S., Dietvorst, R. C., Worm, L., Verbeke, W. J. M. I., & Smidts, A. (2017). Neural responses to functional and experiential ad appeals: Explaining ad effectiveness. International Journal of Research in Marketing, 34(2), 355-366. doi:10.1016/j.ijresmar.2016.10.005Curry, B., & Moutinho, L. (1993). Neural Networks in Marketing: Modelling Consumer Responses to Advertising Stimuli. European Journal of Marketing, 27(7), 5-20. doi:10.1108/03090569310040325Daugherty, T., Hoffman, E., & Kennedy, K. (2016). Research in reverse: Ad testing using an inductive consumer neuroscience approach. Journal of Business Research, 69(8), 3168-3176. doi:10.1016/j.jbusres.2015.12.005Davidson, R. J. (2004). What does the prefrontal cortex «do» in affect: perspectives on frontal EEG asymmetry research. Biological Psychology, 67(1-2), 219-234. doi:10.1016/j.biopsycho.2004.03.008Deitz, G. D., Royne, M. B., Peasley, M. C., & Huang, J. «Coco». (2016). EEG-Based Measures versus Panel Ratings: Predicting Social-Media Based Behavioral Responses to Super Bowl Ads. Journal of Advertising Research, 56(2), 217. doi:10.2501/jar-2016-030Demarzo, M. M. P., Montero-Marin, J., Stein, P. K., Cebolla, A. s, Provinciale, J. G., & García-Campayo, J. (2014). Mindfulness may both moderate and mediate the effect of physical fitness on cardiovascular responses to stress: a speculative hypothesis. Frontiers in Physiology, 5. doi:10.3389/fphys.2014.00105Santos, R. D. O. J. dos, Oliveira, J. H. C. de, Rocha, J. B., & Giraldi, J. D. M. E. (2015). Eye Tracking in Neuromarketing: A Research Agenda for Marketing Studies. International Journal of Psychological Studies, 7(1). doi:10.5539/ijps.v7n1p32Elsen, M., Pieters, R., & Wedel, M. (2016). Thin Slice Impressions: How Advertising Evaluation Depends on Exposure Duration. Journal of Marketing Research, 53(4), 563-579. doi:10.1509/jmr.13.0398Feise, R. J. (2002). Do multiple outcome measures require p-value adjustment? BMC Medical Research Methodology, 2(1). doi:10.1186/1471-2288-2-8Fishman, M., Jacono, F. J., Park, S., Jamasebi, R., Thungtong, A., Loparo, K. A., & Dick, T. E. (2012). A method for analyzing temporal patterns of variability of a time series from Poincaré plots. Journal of Applied Physiology, 113(2), 297-306. doi:10.1152/japplphysiol.01377.2010Fjorback, L. O., Arendt, M., Ørnbøl, E., Fink, P., & Walach, H. (2011). Mindfulness-Based Stress Reduction and Mindfulness-Based Cognitive Therapy - a systematic review of randomized controlled trials. Acta Psychiatrica Scandinavica, 124(2), 102-119. doi:10.1111/j.1600-0447.2011.01704.xGao, J. F., Yang, Y., Lin, P., Wang, P., & Zheng, C. X. (2009). Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals. Brain Topography, 23(1), 105-114. doi:10.1007/s10548-009-0131-4Geisler, F. C. M., Vennewald, N., Kubiak, T., & Weber, H. (2010). The impact of heart rate variability on subjective well-being is mediated by emotion regulation. Personality and Individual Differences, 49(7), 723-728. doi:10.1016/j.paid.2010.06.015Goldberg, J. H., Stimson, M. J., Lewenstein, M., Scott, N., & Wichansky, A. M. (2002). Eye tracking in web search tasks. Proceedings of the symposium on Eye tracking research & applications - ETRA ’02. doi:10.1145/507072.507082Grandjean, D., Sander, D., & Scherer, K. R. (2008). Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization. Consciousness and Cognition, 17(2), 484-495. doi:10.1016/j.concog.2008.03.019Guerreiro, J., Rita, P., & Trigueiros, D. (2015). Attention, emotions and cause-related marketing effectiveness. European Journal of Marketing, 49(11/12), 1728-1750. doi:10.1108/ejm-09-2014-0543Ha, L. (2008). Online Advertising Research in Advertising Journals: A Review. Journal of Current Issues & Research in Advertising, 30(1), 31-48. doi:10.1080/10641734.2008.10505236Harmon-Jones, E., Gable, P. A., & Peterson, C. K. (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biological Psychology, 84(3), 451-462. doi:10.1016/j.biopsycho.2009.08.010Holmqvist, K., Andrà, C., Lindström, P., Arzarello, F., Ferrara, F., Robutti, O., & Sabena, C. (2011). A method for quantifying focused versus overview behavior in AOI sequences. Behavior Research Methods, 43(4), 987-998. doi:10.3758/s13428-011-0104-xKent, R. J., & Allen, C. T. (1994). Competitive Interference Effects in Consumer Memory for Advertising: The Role of Brand Familiarity. Journal of Marketing, 58(3), 97. doi:10.2307/1252313Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., & Townsend, C. (2013). Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications, 40(9), 3803-3812. doi:10.1016/j.eswa.2012.12.095Kim, K., Hayes, J. L., Avant, J. A., & Reid, L. N. (2014). Trends in Advertising Research: A Longitudinal Analysis of Leading Advertising, Marketing, and Communication Journals, 1980 to 2010. Journal of Advertising, 43(3), 296-316. doi:10.1080/00913367.2013.857620Kopton, I. M., & Kenning, P. (2014). Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00549Kühn, S., Strelow, E., & Gallinat, J. (2016). Multiple «buy buttons» in the brain: Forecasting chocolate sales at point-of-sale based on functional brain activation using fMRI. NeuroImage, 136, 122-128. doi:10.1016/j.neuroimage.2016.05.021Lang, A., Bolls, P., Potter, R. F., & Kawahara, K. (1999). The effects of production pacing and arousing content on the information processing of television messages. Journal of Broadcasting & Electronic Media, 43(4), 451-475. doi:10.1080/08838159909364504Lee, J., & Ahn, J.-H. (2012). Attention to Banner Ads and Their Effectiveness: An Eye-Tracking Approach. International Journal of Electronic Commerce, 17(1), 119-137. doi:10.2753/jec1086-4415170105McAlister, L., Srinivasan, R., Jindal, N., & Cannella, A. A. (2016). Advertising Effectiveness: The Moderating Effect of Firm Strategy. Journal of Marketing Research, 53(2), 207-224. doi:10.1509/jmr.13.0285McDuff, D., Kaliouby, R. E., Cohn, J. F., & Picard, R. W. (2015). Predicting Ad Liking and Purchase Intent: Large-Scale Analysis of Facial Responses to Ads. IEEE Transactions on Affective Computing, 6(3), 223-235. doi:10.1109/taffc.2014.2384198Mognon, A., Jovicich, J., Bruzzone, L., & Buiatti, M. (2011). ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology, 48(2), 229-240. doi:10.1111/j.1469-8986.2010.01061.xBabiloni, F. (2012). Consumer Nueroscience: A New Area of Study for Biomedical Engineers. IEEE Pulse, 3(3), 21-23. doi:10.1109/mpul.2012.2189166Mould, D., Mandryk, R. L., & Li, H. (2012). Emotional response and visual attention to non-photorealistic images. Computers & Graphics, 36(6), 658-672. doi:10.1016/j.cag.2012.03.039Cartocci, G., Caratù, M., Modica, E., Maglione, A. G., Rossi, D., Cherubino, P., & Babiloni, F. (2017). Electroencephalographic, Heart Rate, and Galvanic Skin Response Assessment for an Advertising Perception Study: Application to Antismoking Public Service Announcements. Journal of Visualized Experiments, (126). doi:10.3791/55872Pan, J., & Tompkins, W. J. (1985). A Real-Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering, BME-32(3), 230-236. doi:10.1109/tbme.1985.325532Pieters, R., Warlop, L., & Wedel, M. (2002). Breaking Through the Clutter: Benefits of Advertisement Originality and Familiarity for Brand Attention and Memory. Management Science, 48(6), 765-781. doi:10.1287/mnsc.48.6.765.192Piskorski, J., & Guzik, P. (2007). Geometry of the Poincaré plot ofRRintervals and its asymmetry in healthy adults. Physiological Measurement, 28(3), 287-300. doi:10.1088/0967-3334/28/3/005Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039Ruanguttamanun, C. (2014). Neuromarketing: I Put Myself into a fMRI Scanner and Realized that I love Louis Vuitton Ads. Procedia - Social and Behavioral Sciences, 148, 211-218. doi:10.1016/j.sbspro.2014.07.036Shehu, E., Bijmolt, T. H. A., & Clement, M. (2016). Effects of Likeability Dynamics on Consumers’ Intention to Share Online Video Advertisements. Journal of Interactive Marketing, 35, 27-43. doi:10.1016/j.intmar.2016.01.001Smith, A. N., Fischer, E., & Yongjian, C. (2012). How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102-113. doi:10.1016/j.intmar.2012.01.002Strach, P., Zuber, K., Fowler, E. F., Ridout, T. N., & Searles, K. (2015). In a Different Voice? Explaining the Use of Men and Women as Voice-Over Announcers in Political Advertising. Political Communication, 32(2), 183-205. doi:10.1080/10584609.2014.914614Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal, 17(3), 354-381. doi:10.1093/oxfordjournals.eurheartj.a014868Tomkovick, C., Yelkur, R., & Christians, L. (2001). The USA’s biggest marketing event keeps getting bigger: an in-depth look at Super Bowl advertising in the 1990s. Journal of Marketing Communications, 7(2), 89-108. doi:10.1080/13527260121725Vakratsas, D., & Ambler, T. (1999). How Advertising Works: What Do We Really Know? Journal of Marketing, 63(1), 26. doi:10.2307/1251999Valenza, G., Allegrini, P., Lanatà, A., & Scilingo, E. P. (2012). Dominant Lyapunov exponent and approximate entropy in heart rate variability during emotional visual elicitation. Frontiers in Neuroengineering, 5. doi:10.3389/fneng.2012.00003Valenza, G., Citi, L., Lanatá, A., Scilingo, E. P., & Barbieri, R. (2014). Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics. Scientific Reports, 4(1). doi:10.1038/srep04998Varan, D., Lang, A., Barwise, P., Weber, R., & Bellman, S. (2015). How Reliable Are Neuromarketers’ Measures of Advertising Effectiveness? Journal of Advertising Research, 55(2), 176-191. doi:10.2501/jar-55-2-176-191Vecchiato, G., Astolfi, L., Tabarrini, A., Salinari, S., Mattia, D., Cincotti, F., … Babiloni, F. (2010). EEG Analysis of the Brain Activity during the Observation of Commercial, Political, or Public Service Announcements. Computational Intelligence and Neuroscience, 2010, 1-7. doi:10.1155/2010/985867Vecchiato, G., Maglione, A. G., Cherubino, P., Wasikowska, B., Wawrzyniak, A., Latuszynska, A., … Babiloni, F. (2014). Neurophysiological Tools to Investigate Consumer’s Gender Differences during the Observation of TV Commercials. Computational and Mathematical Methods in Medicine, 2014, 1-12. doi:10.1155/2014/912981Vecchiato, G., Susac, A., Margeti, S., De Vico Fallani, F., Maglione, A. G., Supek, S., … Babiloni, F. (2012). High-Resolution EEG Analysis of Power Spectral Density Maps and Coherence Networks in a Proportional Reasoning Task. Brain Topography, 26(2), 303-314. doi:10.1007/s10548-012-0259-5Vecchiato, G., Toppi, J., Astolfi, L., De Vico Fallani, F., Cincotti, F., Mattia, D., … Babiloni, F. (2011). Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements. Medical & Biological Engineering & Computing, 49(5), 579-583. doi:10.1007/s11517-011-0747-xVenkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., … Winer, R. S. (2015). Predicting Advertising success beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling. Journal of Marketing Research, 52(4), 436-452. doi:10.1509/jmr.13.0593Wackermann, J., Lehmann, D., Michel, C. M., & Strik, W. K. (1993). Adaptive segmentation of spontaneous EEG map series into spatially defined microstates. International Journal of Psychophysiology, 14(3), 269-283. doi:10.1016/0167-8760(93)90041-mWedel, M., & Kannan, P. K. (2016). Marketing Analytics for Data-Rich Environments. Journal of Marketing, 80(6), 97-121. doi:10.1509/jm.15.0413Werkle-Bergner, M., Müller, V., Li, S.-C., & Lindenberger, U. (2006). Cortical EEG correlates of successful memory encoding: Implications for lifespan comparisons. Neuroscience & Biobehavioral Reviews, 30(6), 839-854. doi:10.1016/j.neubiorev.2006.06.009West, P. M., Brockett, P. L., & Golden, L. L. (1997). A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice. Marketing Science, 16(4), 370-391. doi:10.1287/mksc.16.4.370Zhou, R., Khemmarat, S., Gao, L., Wan, J., & Zhang, J. (2016). How YouTube videos are discovered and its impact on video views. Multimedia Tools and Applications, 75(10), 6035-6058. doi:10.1007/s11042-015-3206-

The antecedents of cross-functional coordination and their implications for marketing adaptiveness

As the gap between accelerating rate of change and organizational capability in responding to it widens, managers face increasing challenges to coordinate and align diverse intra-firm functions. Although coordination across functions in an organization is necessary for integrating complex resources such as responding to uncertainty in business environments, little is known about the internal conditions of a firm in which cross-functional coordination influences marketing adaptiveness. Marketing adaptiveness recognizes the potential conflicting goals of intra-firm functions, and the need to identify disparate but interdependent organizational resources to fit the external environment. In order to account for the potential interactions of multiple conditions in cross-functional coordination, we use fuzzy-set qualitative comparative analysis to analyze survey data of 274 managers in Egyptian firms operating in uncertain environments based on the motivation-ability-opportunity framework and configuration theory. The findings show that the causal pathways leading to cross-functional coordination and marketing adaptiveness can be enhanced by resource dependency, cross-functional teams, multifunctional training, and management support. In particular, management support is a crucial condition for coordination in support of cross-functional teams and multifunctional training. While resource dependency is an important internal factor for coordination, a high resource dependency can result in a negative effect on marketing adaptiveness

B2B brands on Twitter: Engaging users with a varying combination of social media content objectives, strategies, and tactics

YesThe objective of this research is to increase understanding about B2B company-led user engagement on social media content. Building on hierarchy-of-effects (HoE) theory, we explore how the world’s leading B2B companies use content objectives (why), strategies (how), and tactics (what) on Twitter. We first integrate B2B advertising and social media research on companies’ content objectives, strategies, and tactics. Then, using qualitative analyses, we examine the existence of objectives, strategies, and tactics in the most engaging tweets (N=365) of the worlds’ ten leading B2B brands, covering five industries, in 2017. Finally, we quantitatively examine how the use of diverse objectives and strategies differs between the most engaging tweets (N=318) and least engaging tweets (N=229) of the companies in 2018. The companies use objectives, strategies and tactics that relate to creating awareness, knowledge and trust, interest, and liking in the majority of their most and least engaging tweets, and express preference, conviction and purchase aspects much less. Differences exist in general, industry-wise, and company-wise. The study is a rare attempt to integrate the extant B2B advertising and social media research, and compare the most and least engaging B2B social media content

The effects of customer equity drivers on loyalty across services industries and firms

Customer equity drivers (CEDs)—value equity, brand equity, and relationship equity—positively affect loyalty intentions, but this effect varies across industries and firms. We empirically examine potential industry and firm characteristics that explain why the CEDs–loyalty link varies across services industries and firms in the Netherlands. The results show that (1) some previously assumed industry and firm characteristics have moderating effects while others do not and (2) firm-level advertising expenditures constitute the most crucial moderator because they influence all three loyalty strategies (significant for value equity and brand equity; marginally significant for relationship equity), while three industry contexts (i.e., innovative markets, visibility to others, and complexity of purchase decisions) each influence two of the three loyalty strategies. Our results clearly show that specific industry and firm characteristics affect the effectiveness of specific loyalty strategies