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Marketing Intelligence: Boom or Bust of Service Marketing? (International Journal of Interactive Multimedia and Artificial Intelligence)
Abstract
Marketing intelligence fosters two major developments within digital service marketing. On the one hand, a boom of services seems to have evolved, accelerated by the opportunities of marketing intelligence. It has contributed to the optimization of customer experiences, e.g., supported by mobile, personalized, and customized marketing services. On the other hand, (digital) self-services are likely to pervert the term "service". Lifecycle marketing, including annoying marketing communication in real-time, automated price adjustment and programmatic advertising based on artificial intelligence, affects the vision of fully standardized marketing automation. Additionally, there are incentives to pollute the digital information in order to manufacture opinions. Fake news is one popular example. This leads to the (open) question if marketing intelligence means service boom or bust of marketing. This contribution aims to elaborate the boom-and-bust aspects of marketing intelligence and suggests a trade-off. The method applied in this paper will be a descriptive and conceptual literature review, through which the paradigmatic thoughts will be juxtaposed from the perspective of service.
... Generatywna sztuczna inteligencja znajduje zastosowanie w różnych dziedzinach, takich jak medycyna (Zhang, Vinothini, Muthu, 2023;Zhou i in., 2023), sztuka (Agüera y Arcas, 2017; Chatterjee, 2022), marketing (Lies, 2022), edukacja (Khosravi i in., 2022), muzyka (Álvareza, Quirós, Baldassarri, 2023), informatyka (Kulkarni, Padmanabham, 2017) i wiele innych. Korzystającym z GenAI służy m.in. ...
The implementation of artificial intelligence-based solutions not only contributes to increasing the level of innovation and the pace of development of companies operating in the digital space but can also be perceived as beneficial from the user’s perspective. Offering (seemingly) free tools designed to facilitate human work promotes, often unreflectively, their overuse not only for entertainment but also for creating reports, official documents, and even scientific texts.
Users interacting with artificial intelligence enter data that may be a company secret or of a private nature. Such actions can lead to a deepening imbalance between the benefits gained by companies offering services based on artificial intelligence and the benefits to users, ultimately posing a threat to the latter’s privacy.
This article is not only an attempt to answer questions about whether artificial intelligence can be controlled (from the perspectives of both organizations and users), how to protect privacy in the age of artificial intelligence, and whether it is possible to ensure the right to be forgotten, but it is also a contribution to a broader discussion regarding the often unacknowledged consequences of using generative artificial intelligence services. For this purpose, a multiple case study method was utilized. An analysis of the terms of service of selected AI-based services offered by companies, such as text generation, graphic generation, and immersive experiences, was conducted. Examples of solutions that allow for increased anonymity in the digital environment and the deletion of data on the internet will also be presented.
... Its ability to generate imaginative and realistic results, coupled with its accessibility, may be the reason for its rising popularity. These factors have far-reaching effects on a variety of fields, including software development [12], [13], marketing [10], [11], art [6], [7], music [8], [9], and medicine [1][2][3]. While artificial intelligence (AI) has gained popularity recently, the area of generative artificial intelligence, or GenAI, has grown explosively in the last few months of 2022 [14]. ...
The capacity of artificial intelligence (AI) to provide innovative and realistic outcomes has led to its rise in popularity. With programmes like ChatGPT1, Dall-E2, and Midjourney3 giving consumers access to Large Language Models (LLMs), generative artificial intelligence, or GenAI, has expanded quickly. On the other hand, a lack of a widely recognised definition for GenAI may cause misconceptions. Generative models are more complex algorithms that can generate high-quality, human-like material. Examples of these models include Variational Autoencoders (VAE) and Generative Adversarial Networks (GANs). Gen-AI is the term used to describe the combination of artificial intelligence (AI) and genetic algorithms in the electronics sector. This has resulted in a number of applications, including electronic circuit optimisation, improved hardware design and evolution, self-learning and adaptation facilitation, improved fault tolerance and self-repair, designing and controlling evolutionary robotics, and optimising co-design of hardware and software, evolutionary manufacturing, and electronic design automation. AI-driven chip design, genetic algorithms in circuit design, hardware optimised for AI, self-repairing circuits, autonomous electronics manufacturing, AI-enhanced Internet of Things devices, neuromorphic computing, and robotic evolution are some of the innovations in Gen-AI electronics. Four major obstacles stand in the way of Gen-AI, though: inaccurate outcomes, prejudice and fairness, infringement on intellectual property, and environmental issues. In the future, Gen AI will be utilised in the electronics industry to optimise the following: adaptive hardware-software co-design, real-time resource management, embedded systems and evolutionary robotics, energy-efficient hardware design, automated circuit design, self-healing electronics, real-time resource management, adaptive hardware-software co-design, and ethical and sustainable electronics design.
... Benchmark representative datasets are a standard in evaluating performance of a system [67], [68], [69], [70]. We select a well-known benchmark dataset of fake news, which contains fake and real news propagated during 2016 U.S. Presidential elections [36], [68], [69]. ...
Misinformation is a pervasive problem on social media platforms, with the potential to cause harm to individuals and society as a whole. Many computational techniques have been employed to tackle the misinformation in the online ecosystem. Curbing misinformation on online social networks is an important sub-field of this research. In this paper, we explore the use of deep learning to detect misinformation on Reddit, which is one of the popular online forums. The proposed approach combines natural language processing and deep learning techniques to identify posts that contain misinformation and flag them for further review. We evaluate our model on a benchmark dataset of Reddit posts and show that it is able to identify misinformation with a high degree of precision. We use transfer learning by incorporating transformer-generated embeddings in our system. The Transformer based model is fine-tuned on Stanford natural language Inference dataset. We subsequently use a simple deep neural network to classify the posts as true or fake. Our model outperforms the models reported in the literature on the unimodal text data, on the benchmark dataset Fakeddit.
... Most businesses prioritize making profits and engage in comprehensive strategies to maximize their revenue and minimize costs, including campaigns based on fake news [62]. For example, companies may create and spread fake news to advertise their products or services, increase sales, or gain an advantage over competitors [63,64]. In addition, they sometimes exaggerate or falsify product information to make their products appear more appealing to consumers and manipulate them into buying, thus boosting sales. ...
Fake news was conventionally assumed to mean fabricated information published in newspapers and other mass media. It was mostly done to increase the paper's sales and was unlikely to have long-term impacts on society. Fake news in marketing concerns intentionally delivering false or misleading information regarding a product or service to influence consumer behavior. Despite the severity of these impacts, we noted that there is very little scientific research on fake news in marketing and consumer behavior. This systematic literature review (LRSB) aims to bridge this knowledge gap by searching and analysing relevant publications on "fake news in marketing" and synthesising data from 117 relevant studies, providing a framework for marketers and business leaders to reduce the spread and impact of online misinformation. The main highlights identify that the implementation and use of social media, shaping a presence on the internet, diffusion, and consumption of fake news have become a serious challenge and a dominant problem in the marketing sector, especially with the growth of digital marketing. Strategically, fake news spread can be categorized into various types, and the use of these tactics can produce short-term benefits such as increased engagement and instant sales, but they can also have severe long-term costs such as reputational damage, loss of revenues and market share, and discouraged investors and shareholders.
... Benchmark representative datasets are a standard in evaluating performance of a system [67], [68], [69], [70]. We select a well-known benchmark dataset of fake news, which contains fake and real news propagated during 2016 U.S. Presidential elections [36], [68], [69]. ...
Fake news is detrimental for society and individuals. Since the information dissipation through online media is too quick, an efficient system is needed to detect and counter the propagation of fake news on social media. Many studies have been performed in last few years to detect fake news on social media. This study focuses on efficient detection of fake news on social media, through a Natural Language Processing based approach, using deep learning. For the detection of fake news, textual data have been analyzed in unidirectional way using sequential neural networks, or in bi-directional way using transformer architectures like Bidirectional Encoder Representations from Transformers (BERT). This paper proposes Contextualized Fake News Detection System (ConFaDe)-a deep learning based fake news detection system that utilizes contextual embeddings generated from a transformer-based model. The model uses Masked Language Modelling and Replaced Token Detection in its pre-training to capture contextual and semantic information in the text. The proposed system outperforms the previously set benchmarks for fake news detection; including state-of-the-art approaches on a real-world fake news dataset, when evaluated using a set of standard performance metrics with an accuracy of 99.9 % and F1 macro of 99.9%. In contrast to the existing state-of-the-art model, the proposed system uses 90 percent less network parameters and is 75 percent lesser in size. Consequently, ConFaDe requires fewer hardware resources and less training time, and yet outperforms the existing fake news detection techniques, a step forward in the direction of Green Artificial Intelligence.
Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence' lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI".
Purpose
Drawing on theoretical insights from the persuasion knowledge model (PKM), this study aims to propose and test a model that maps out the antecedents, process and consequences to explain how consumers process and respond to fake news about brands on Facebook.
Design/methodology/approach
Contextualizing the fake news about Coca-Cola’s recall of Dasani water, an online survey was conducted via Qualtrics with consumers in the USA ( N = 468). Data were analyzed using covariance-based structural equation modeling.
Findings
Results showed that self-efficacy and media trust significantly predicted consumers’ persuasion knowledge of the fake news. Persuasion knowledge of the fake news significantly influenced consumers’ perceived diagnosticity of the fake news and subsequent brand trust. Furthermore, persuasion knowledge of the fake news mediated the effects from self-efficacy on perceived diagnosticity of the fake news and brand trust, respectively.
Originality/value
This study contributes to the literature of brand management by examining how consumers process and respond to fake news about a brand. It also extends the persuasion knowledge model by applying it to the context of fake news about brands on social media, and incorporating antecedents (self-efficacy and media trust) and consequences (perceived diagnosticity and brand trust) of persuasion knowledge in this particular context. Practically, this study provides insights to key stakeholders of brands to better understand consumers’ information processing of fake news about brands on social media.
Purpose - This conceptual paper delves into the implications of blockchain technology adoption for brands and consumers. Drawing on existing branding literature and real-life applications of blockchain, the challenges, risks and opportunities from blockchain adoption for four important areas of the branding literature are canvassed (i.e. brand positioning and corporate brand image, consumer-brand relationships, online brand communication and consumers’ trust to the brand). Also, a future-oriented discussion is provided that highlights some important avenues for researchers in the field.
Design/methodology/approach - This conceptual discussion sheds light in the potential implications of blockchain technology for strategic brand management. To do so, an analytical review of the nature of blockchain transactions is provided, and their unique features and functions for businesses and consumers are discussed.
Findings - This work ignites an exploratory discussion around how blockchain applications and platforms can affect consumer-brand relationships, drawing on a number of real-life examples of blockchain implementation. This discussion sheds light on how blockchain features can impact upon various areas of interest for strategic brand management such as the adoption of digital currencies, brand storytelling, the use of blockchain-enabled loyalty programmes, the role of intermediaries in online advertising, counterfeit consumption, brand transparency and trust for brands in online marketplaces, among others.
Originality/value - This is one of the first conceptual efforts in the branding literature that draws on scarce existing knowledge around blockchain adoption and discusses the potential implications of blockchain technology for brands and consumers while also providing directions for future research.
This contribution reviews the vast scope of digital application areas, which shape the digital marketing landscape and coin the present term “marketing intelligence” from a marketing technique point of view. Additionally, marketing intelligence as social engineering techniques are described. The review ranges from digital IT- and big data marketing until marketing 5.0 as digitalized trust marketing. The multiplicity of applications and interdependencies of the digital and social techniques reviewed should show that big data and marketing intelligence have already become a marketing reality. It becomes clear that marketing is witnessing a methodological, technical and cultural paradigm shift that augments and amplifies traditional outbound marketing with inbound marketing.
The aim of this article is to draw the attention of comparative scholars, researchers and
policy-makers to the inferior position of consumer-creditors in bankruptcy proceedings, a
topic that escaped attention during the development of financial protection of consumers in Europe. Consumers may become creditors if they prepay certain goods or services that remain undelivered following bankruptcy of a retailer or service-provider. The problem that results is that consumer-creditors are treated as unsecured creditors in bankruptcy law, who rank very low on the priority ladder and are doomed to recover only a small fraction of their claims, if anything at all.
In order to fill the vacuum, the article attempts to map the real dimensions of the consumer-creditor problem first by outlining the spectrum of bankruptcy cases involving
consumer-creditors and the threats to consumers inherent to abandoned and defunct companies that are usually left without assets creditors could collect upon. This includes case studies of major recent bankruptcies caused by appearance of new technologies (e.g., the collapse of UK Farepak due to appearance of Internet-based competitors) and linked abuses (web-fraudulent schemes).
The second part of the article provides an overview of the regulatory responses, ranging from the prescriptive approach of US law implementing limited high priority to consumer-creditors in bankruptcy proceedings in the 1970s, the 2016 multi-pronged proposals of the UK Law Commission, to the specific regulatory responses of selected post-socialist systems, like the blocked accounts introduced by Croatia and Serbia, the forced deletions of Hungary and the special tax imposed in Slovakia.
SOCMINT (SOCial Media INTelligence) is increasingly considered relevant and cost efficient information, and the exploitation of social media information in the name of security and public safety is generally regarded as unproblematic. We will critically scrutinize this claim and argue that the exploitation of such information by Intelligence and Security Services raises new ethical concerns. Drawing on recent moral discussions about privacy, we will argue that individuals have an interest in privacy in public spaces, including online spaces. We will discuss the role of such public privacy interests and argue that the systematic surveillance of social media platforms by security authorities potentially entail a negative chilling effect.
Successful conservation in the United States relies on collective stewardship by millions of private landowners, challenging those agencies and nongovernment organizations tasked with engagement and outreach. Perennially limited resources compound this challenge, highlighting a deep need for efficient social marketing. In the following research, we test the efficacy of two social marketing strategies— microtargeting and normative appeals—through a randomized controlled trial of an integrated social marketing campaign targeting riparian landowners in the Pennsylvania portion of the Chesapeake Bay Watershed. We used a microtargeting algorithm to predict landowners’ likelihood of responding to a conservation outreach campaign to create treatment groups of high-likelihood prospects versus random prospects (i.e., no microtargeting). A normative appeal was also included as an experimental factor in the campaign communicating that forested riparian buffer investments were common among similar landowners. Among microtargeted landowners, we observed a 66% increase in response to a riparian restoration survey compared to the control group. Additionally, we found a significant influence of a normative message among random (nonmicrotargeted) prospects, increasing response by 23% over the control group.We conclude conservation outcomes may be more efficiently achieved by deploying these marketing techniques on a wider scale to a variety of conservation challenges.
The emergence of big data brings a new wave of Customer Relationship Management (CRM)’s strategies in supporting personalization and customization of sales, services and customer services. CRM needs big data for better customers experiences especially personalization and customization of services. Big data is a popular term used to describe data that is volume, velocity, variety, veracity, and value of data both structured and unstructured. Big data requires new tools and techniques to capture, store and analyse it and is used to improve decision making for enhancing customer management. The aim of the research is to examine big data for CRM’s scenario. The method of collection of data for this study was literature review and thematic analysis from recent studies. The study reveals that CRM with big data has enabled business to become more aggressive in term of marketing strategy like push notification through smartphone to their potential target audiences.
Brands can interact both directly and indirectly with fake news. In some instances, brands are the victims of fake news and, other times, the purveyors. Brands can either finance fake news or be the targets of it. Indirectly, they can be linked via image transfer, where either fake news contaminates brands, or brands validate fake news.
To control the risk of negative image transfer, the authors propose technical actions to address false news and systemic steps to rethink the management of brands in order to inoculate against various forms of “fakery” and to reestablish stakeholder trust. Systemic solutions involve a rethinking of brands and branding. Too often, brands have become uncoupled from the reality of the offerings they adorn. But brands are not ends in themselves, they are the result of outstanding offerings. They can act as interpretive frames, but they don’t unilaterally create reality, as many seem to believe. Brands should not be seen and managed as objects but as perceptual processes.
A new era of Information Warfare has arrived. Various actors, including state-sponsored ones, are weaponizing information on Online Social Networks to run false-information campaigns with targeted manipulation of public opinion on specific topics. These false-information campaigns can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections. Evidently, the problem of false information on the Web is a crucial one and needs increased public awareness as well as immediate attention from law enforcement agencies, public institutions, and in particular, the research community.
In this article, we make a step in this direction by providing a typology of the Web’s false-information ecosystem, composed of various types of false-information, actors, and their motives. We report a comprehensive overview of existing research on the false-information ecosystem by identifying several lines of work: (1) how the public perceives false information; (2) understanding the propagation of false information; (3) detecting and containing false information on the Web; and (4) false information on the political stage. In this work, we pay particular attention to political false information as: (1) it can have dire consequences to the community (e.g., when election results are mutated) and (2) previous work shows that this type of false information propagates faster and further when compared to other types of false information. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false-information dissemination on the Web.
Data mining have made customer relationship management (CRM) a new area where firms can gain a competitive advantage, and play a key role in the firms' management decision. In this paper, we first analyze the value and application fields of data mining techniques for CRM, and further explore how data mining applied to Customer churn analysis. A new business culture is developing today. The conventional production centered and sales purposed market strategy is gradually shifting to customer centered and service purposed. Customers' value orientation is increasingly affecting the firms'. And customer resource has become one of the most important strategic resources. Therefore, understanding customers' needs and discriminating the most contributed customers has become the driving force of most modern business.
Purpose
The purpose of this paper is to demonstrate how geomarketing statical tools (notably, gravitational models) can support healthcare organisations to improve the quality of their services.
Design/methodology/approach
Geomarketing tools were applied to the analysis of data (91,478 observations) concerning the performance of nuclear magnetic resonance.
Findings
Geomarketing models can support and enhance the planning of service provisions of healthcare organisations. Drawing the planning actions on the patient needs and actual behaviours allow the healthcare organisations to obtain better market performance.
Practical implications
The results support the health service planning activities related to covering the so-called “offer gaps”.
Originality/value
This is the first study to apply geomarketing to improve the quality of healthcare services.
The unabated flurry of research activities dedicated to gaining business insights from a flood of data generated by heterogeneous mobile sources, such as the Internet of Vehicles, sensors, and smartphones, has instigated a new research domain called MBD. At the core of this mobile environment, scalability, cost effectiveness, reliability, analytics, and security are important concerns. Coping with these issues in handling MBD requires understanding the challenges associated with it. Mobile computing and big data have been widely studied separately; however, very few studies have explored the convergence of these two domains. In this article, we critically review recent research efforts directed at MBD. We also classify the MBD by devising a thematic taxonomy that is based on source, analytics, applications, characteristics, security, and data type. Furthermore, we discuss the opportunities offered by MBD in terms of analytics. Some potential uses of MBD in healthcare, telecommunication, digital advertising, and transportation are also presented. Several open research challenges are discussed as future research directions.
This paper introduces an overview and positioning of the contemporary brand experience in the digital context. With technological advances in games, gamification and emerging technologies, such as Virtual Reality (VR) and Artificial Intelligence (AI), it is possible that brand experiences are getting more pervasive and seamless. In this paper, we review the current theories around multi-sensory brand experience and the role of new technologies in the whole consumer journey, including pre-purchase, purchase and post-purchase stages. After this analysis, we introduce a conceptual framework that promotes a continuous loop of consumer experience and engagement from different and new touch points, which could be augmented by games, gamification and emerging technologies. Based on the framework, we conclude this paper with propositions and recommendations for future research in contemporary brand management, which could help brand managers and designers to deal with technological challenges posed by the contemporary society.
Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors’ affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena.
In this paper, the author argues that technologies will transform marketing organization and reshape market space, but companies should acknowledge that their mere possession will not be enough to ensure a sustainable market leadership position. The aim of this paper is to summarize the main challenges of digital disruption, as well as, to identify the opportunities for marketing to anticipate IT, in order to capitalize on its benefits. The research topics driving this paper are: 1) to analyze social media usage by the Millennial Generation in Bulgaria 2) to evaluate the attitudes of Bulgarian SMEs toward ICT implementation in their current marketing management practices and 3) to identify the possibilities to apply Marketing 4.0 by fostering agility employment in marketing organizations. Following the literature review a conceptual model of an agile marketing organization is proposed. D3I2C framework integrates contemporary marketing practice and digital transformation. It can be used by academia and other organizations as an outline for the transformation of marketing organizations.
Demonstrating the monetary outcomes of marketing is no longer considered a
virtue but a necessity by the top management. Marketers are increasingly held
accountable for their actions, yet most marketers struggle in their attempts to
measure marketing performance. The emergence of digital analytics has raised
optimism of improved measurability due to its ability to track customer
behavior in the digital environment. However, research lacks a clear
understanding of the advances and limitations of digital analytics, and what it
takes from an organization to make the most of its usage. The dissertation
advances the knowledge in this area by investigating how industrial companies
can use digital analytics for measuring and optimizing digital marketing
performance.
The primary data of this dissertation come from three case studies that ex-
amine the use of digital analytics from different angles. The first case study ex-
plores the use of digital analytics for overcoming universal marketing perfor-
mance measurement challenges; the second study investigates the organiza-
tional processes for measuring digital marketing performance through the use
of digital analytics; the third study takes a step further and studies how digital
analytics data can be harnessed for optimizing digital marketing performance.
The findings confirm that digital analytics produces data that can be used
for measuring and optimizing digital marketing performance but its real value
is determined by an organization’s ability to process the data into meaningful
insights that inform decision-making. Overall, the findings suggest that the
greater use of digital analytics can be seen as a movement toward data-driven
marketing where marketing decisions are based on information rather than ex-
perience and intuition. While demonstrating the benefits that companies may
gain from the use of digital analytics, the dissertation also discusses the dangers
of relying on digital analytics data that may lead organizations to maximize
short-term revenue generation at the expense of long-term marketing perfor-
mance.
Marketing departments are under increased pressure to demonstrate their economic value to the firm. This challenge is exacerbated by the fact that marketing uses attitudinal (e.g., brand awareness), behavioral (e.g., brand loyalty), and financial (e.g., sales revenue) performancemetrics, which do not correlate highly with each other. Thus, onemetric could view marketing initiatives as successful, whereas another could interpret them as a waste of resources. The resulting ambiguity has several consequences for marketing practice. Among these are that the scope and objectives of marketing differ widely across organizations. There is confusion about the difference between marketing effectiveness and efficiency.Hard and softmetrics and offline and onlinemetrics are typically not integrated. The two dominant tools for marketing impact assessment, response models and experiments, are rarely combined. Risk inmarketing planning and execution receives little consideration, and analytic insights are not communicated effectively to drive decisions. The authors first examine how these factors affect both research and practice. They then discuss how the use of marketing analytics can improve marketing decision making at different levels of the organization. The authors identify gaps in marketing's knowledge base that set the stage for further research and enhanced practice in demonstratingmarketing's value.
The proliferation of advertising in all communication media causes consumers to perceive a significant amount of competitiveness between advertised products, as well as to feel overwhelmed by the intrusiveness of their advertisements. When taken together, these dimensions form the concept “advertising clutter.” A review of the literature shows that perceived intrusiveness is the main component of the perception of clutter. Advertising clutter can prompt undesired behaviors (e.g., advertising avoidance) as well as attitudes contrary to those that companies’ advertising campaigns hope to achieve. It also leads to diminished advertising efficacy in terms of consumer memory, a decrease in positive attitudes towards the message and brand, as well as declined purchasing intention and, therefore, sales. In this article, the main consequences of advertising clutter for consumers in online media are reviewed and discussed. To that end, a theoretical review of this concept and its main dimensions is performed; special attention is paid to the online context. Finally, some practical recommendations and research opportunities are pointed out.
Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
Improvements in information and communication technologies introduce important marketing opportunities for companies. One of the last technological improvements affecting the marketing activities of the companies is mobile phones being used as a channel of marketing communication. In the process of the promotion of the goods, services and ideas, this situation has increased the importance of ‘Mobile Marketing’ which can be identified as establishing the marketing communication with the target clients through mobile phones and sending promotion oriented messages. This study will firstly touch upon the improvements in mobile communication technologies briefly. Thereafter, the increasing use of mobile phone and the growing importance of mobile marketing due to its advancing features will be revealed along with its benefits, and solutions will be offered by means of discussing the problems confronted. Finally, future oriented expectations will also be included.
Purpose
– The purpose of this paper is to find the role of trust as a mediating variable between e-service quality and customer satisfaction in internet banking.
Design/methodology/approach
– The paper opted for an exploratory study using closed ended questionnaire. The empirical data are drawn from 367 customers of internet banking. Factor structure of e-SQ, customer satisfaction and trust has been tested using EFA and CFA by gap values and perception values. Structure equation modeling has been used to analyze the effects of independent variable e-service quality on customer satisfaction and the role of mediating variable trust. Stepwise analysis has been done to examine the effect of trust on customer satisfaction. Sobel test has been used to measure the indirect effect.
Findings
– e-SQ was found to be strongly correlated with customer satisfaction. The results confirm trust as a mediating variable between e-service quality while analyzing the same model with gap value and perception value.
Research limitations/implications
– The research implies that banking service providers should focus on improvement of trust parameters as well as those e-SQ dimensions which affects trust, to retain the customers and to get more customers for internet banking.
Originality/value
– The reliable and valid instrument confirmed in this research can be used by further studies detecting the relationships among these constructs in an extended context. The fundamental premise of the proposed research work model was to make banking service providers understand comprehensively the factors necessary to achieve high service quality that will significantly impact on customers’ trust, satisfaction.
This study proposes that the benefits gained from marketing performance measurement are determined by how an organization exploits the metrics system under specific circumstances. For this purpose, the authors review performance measurement literature and apply it to the use of Web analytics, which offers companies a metrics system to measure digital marketing performance. By performing an in-depth investigation of the use of Web analytics in industrial companies, the study shows that an organization's efforts to use marketing metrics systems and the resulting outcomes cannot be understood without considering the reasoning behind the chosen metrics, the processing of metrics data, and the organizational context surrounding the use of the system. Given the continuously growing importance of digital marketing in the industrial sector, this study illustrates how industrial companies characterized by complex selling processes can harness Web analytics to demonstrate how digital marketing activities benefit their businesses.
Purpose
The purpose of this paper is to present the multi‐sensory brand‐experience concept in relation to the human mind and senses. It also seeks to propose a sensory marketing (SM) model of the multi‐sensory brand‐experience hypothesis.
Design/methodology/approach
This paper applies exploratory and explanatory approaches to investigating the multi‐sensory brand‐experience concept within the context of discovery. The qualitative study is built on primary and secondary data sources, including personal interviews with experts and managers.
Findings
The multi‐sensory brand‐experience hypothesis suggests that firms should apply sensorial strategies and three explanatory levels within an SM model. It allows firms through means as sensors, sensations, and sensory expressions to differentiate and position a brand in the human mind as image.
Research limitations/implications
A theoretical implication is that the multi‐sensory brand‐experience hypothesis emphasizes the significance of the human mind and senses in value‐generating processes. Another theoretical implication is that the hypothesis illustrates the shortcomings of the transaction and relationship marketing models in considering the multi‐sensory brand‐experience concept. It is worth conducting additional research on the multi‐sensory interplay between the human senses in value‐generating processes.
Practical implications
The findings offer additional insights to managers on the multi‐sensory brand‐experience concept. This research opens up opportunities for managers to identify emotional/psychological linkages in differentiating, distinguishing and positioning a brand as an image in the human mind.
Originality/value
The main contribution of this research lies in developing the multi‐sensory brand‐experience hypothesis within a SM model. It fills a major gap in the marketing literature and research in stressing the need to rethink conventional marketing models.
Within digital marketing, marketing intelligence fosters two major developments. First-and even in the absence of key performance indicators-it improves marketing efficiency, as life-cycle marketing, automated price adjustment and programmatic advertising based on artificial intelligence (AI) all support the vision of fully standardised marketing automation, with real-time, optimised customer centricity. Secondly, new marketing areas such as viral marketing, social media marketing and content marketing emphasise the increasing significance of creative marketing. Looking for paradigmatic principles reveals that performance marketing is shaped by analytic, processual and lean thinking. In contrast, creative marketing depends on agile outside-in thinking as a key element in the debate regarding marketing 3.0. Thus, digitisation seems to foster paradigmatic incompatibilities within marketing 4.0. This paper argues that until Big Data and AI can judge creativity, the digitisation of marketing will continue to suffer teething problems.
We are living in a world of data abundance and rapid technological advances in the digital realm. The consequences for marketing practices have been transformative. The Marketing Edge and Journal of Business Research sponsored this special issue to address the need for research in this domain. We draw upon past literature to trace how data-driven marketing practices and adoption of digital technologies have helped transform and expand the scope of marketing from a function that was primarily related to analyzing advertisements, to crafting analytics-driven customer-centric marketing, to a function that is fiscally responsible and increasingly technology enabled. The collection of nine studies in this special issue richly describes the challenges that marketing practitioners face and highlights research issues that need to be addressed.
The widespread dissemination of misinformation in social media has recently received a lot of attention in academia. While the problem of misinformation in social media has been intensively studied, there are seemingly different definitions for the same problem, and inconsistent results in different studies. In this survey, we aim to consolidate the observations, and investigate how an optimal method can be selected given specific conditions and contexts. To this end, we first introduce a definition for misinformation in social media and we examine the difference between misinformation detection and classic supervised learning. Second, we describe the diffusion of misinformation and introduce how spreaders propagate misinformation in social networks. Third, we explain characteristics of individual methods of misinformation detection, and provide commentary on their advantages and pitfalls. By reflecting applicability of different methods, we hope to enable the intensive research in this area to be conveniently reused in real-world applications and open up potential directions for future studies.
Service marketing, to be effective and successful, requires a mirror-opposite view of conventional “product” practices.
Purpose
The purpose of this paper is to explore the extent to which organisations engage with stakeholders about social and environmental issues. The authors establish where the designers of these reports source the information reported.
Design/methodology/approach
This was an exploratory study that employed a qualitative research design. Interviews with 24 individuals from 15 organisations in New Zealand that are involved with the writing of sustainability reports were conducted. In addition documents were analysed from a number of organisations to allow for triangulation.
Findings
Findings indicate that engagement with stakeholders was important and one of the main purposes was to have a licence to stay in business. It was also found that managers do prioritise the saliency of issues. Generally, stakeholders do not get involved in the decision making pertaining to environmental issues in organisations.
Research limitations/implications
The sample investigated in this study was relatively small so generalisation of the results would be difficult. However, these in depth interviews did provide insights that can be used in further study using large samples and in different countries.
Originality/value
This is the first time that the designers of annual reports were interviewed. They have knowledge of the extent of stakeholder engagement with firms.
Wie gelingt profitables Online-Marketing? Erwin Lammenett, Gründer einer renommierten Internetagentur, erläutert kompakt und praxisnah die gesamte Palette der Online-Marketing-Instrumente. Sie erfahren, wie Sie diese Instrumente erfolgreich nutzen, welche Sie sinnvoll miteinander kombinieren können und wie Sie ein effizientes Controlling aufbauen. Angesprochen werden Führungskräfte und Mitarbeiter aus Marketing und PR, Produktmanager, Unternehmensberater, aber auch Dozierende und Studierende mit dem Schwerpunkt Marketing.
Extras: Im Blog zum Buch finden Sie weitere Beispiele, aktuelle Links, ergänzende Videos und Screencasts.
Die 5. Auflage wurde überarbeitet und aktualisiert. Dabei wurde besonderer Wert auf die Bewertung von aktuellen Entwicklungen gelegt.
Der Inhalt
• Affiliate-Marketing und E-Mail-Marketing
• Suchmaschinenmarketing: SEA, SEO, Universal Search
• Online-Werbung: Vermarkter, Formate
• Social-Media-Marketing: Blogs, Wikis, Facebook, Twitter
• Online-PR: Pressearbeit, Presseportale
• Erfolgsdeterminanten: Abhängigkeiten und Beziehungen untereinander
Der Autor
Dr. Erwin Lammenett ist Gründer der Internetagentur team in medias GmbH, die seit 1995 am Markt ist. 2014 verkaufte er seine Agentur und arbeitet seither als freier Berater für Online-Marketing. Seit Mai 2015 ist er Vorsitzender des Aufsichtsrates der +Pluswerk AG. Ferner ist er Mitglied im Praxisbeirat der Fachhochschule Düsseldorf, FB Medien.
This paper shows the existence of extreme types of zombie firm, i.e. companies with negative equity that continue to do business despite having lost their entire equity. We explain how these firms are measured and how the riskier ones are defined with different determinants. Using a Spanish sample from 2010 to 2014 an index called the EZIndex is developed that includes four dimensions of the extreme zombie problem: extension, contagion, recovery signs and immediacy. The paper contributes to zombie theory on the one hand by developing a method for ranking zombie firms based on risks and changes over time, and on the other hand by using a log-linear model to detect the riskiest corporate profiles out of all these risky firms. It demonstrates significant implications that need to be considered by the competent authorities not only in terms of their impact as a whole but also in regard to the particular profile of extreme zombie firms: they are less regulated, large and located in regions with large business fabrics.
Das essential thematisiert vier zentrale Entwicklungen, die die Praxis und Theorie von Marketing und Unternehmenskommunikation prägen: Marketing 4.0, Mittelstandskommunikation, Marketing-Mix und die Annäherung von Marketing und PR, wie sie beispielsweise bei Pokémon Go zu finden ist. Dabei geht es unter anderem um die Notwendigkeit des menschen-zentrierten Marketings und um die Relevanz der Unternehmenskommunikation für den Mittelstand. Der Autor Jan Lies gibt Impulse für kleine und mittelständische Unternehmen, sich mit den Potenzialen der Digitalisierung im Marketing auseinander zu setzen.
Der Inhalt
Die digitale Transformation des Mittelstands
Menschenzentrierung: Kunde statt Markt
Marketing und PR 4.0
Die Zielgruppen
Dozierende und Studierende der Wirtschafts- und Betriebswissenschaften
Führungskräfte aus mittelständischen Unternehmen
Der Autor
Dr. Jan Lies ist Professor für Allgemeine Betriebswirtschaftslehre mit den Schwerpunkten Unternehmenskommunikation und Marketing an der FOM Hochschule für Oekonomie & Management Dortmund.
The most common thing we are listening about these days is e commerce this paper is all about taking e commerce to the next level. In a survey (of Bi intelligence) it was found that the most used social networking app is Facebook messenger and what's app, which is basically online chatting applications. It's all about giving customers a reliable and easy platform of internet shopping to save their time and to make them feel better. From a study research it has been found that there are more than 2 billion Smartphone users in the world and they increasingly day by day are getting addicted to Smartphone and can't spend much time without them. And no wonder there is a growing interest in conversational commerce. Conversational commerce is an approach that puts conversations between brands and their customers at the heart of online customer's journey.
We develop and describe a framework for research in digital marketing that highlights the touchpoints in the marketing process as well as in the marketing strategy process where digital technologies are having and will have a significant impact. Using the framework we organize the developments and extant research around the elements and touchpoints comprising the framework and review the research literature in the broadly defined digital marketing space. We outline the evolving issues in and around the touchpoints and associated questions for future research. Finally, we integrate these identified questions and set a research agenda for future research in digital marketing to examine the issues from the perspective of the firm.
To facilitate efficient and effective service delivery, firms are introducing self-service technologies (SSTs) at an increasing pace. This article presents a meta-analysis of the factors influencing customer acceptance of SSTs. The authors develop a comprehensive causal framework that integrates constructs and relationships from different technology acceptance theories, and they use the framework to guide their meta-analysis of findings consolidated from 96 previous empirical articles (representing 117 independent customer samples with a cumulative sample size of 103,729 respondents). The meta-analysis reveals the following key insights: (1) SST usage is influenced in a complex fashion by numerous predictors that should be examined jointly; (2) ease of use and usefulness are key mediators, and studies ignoring them may underestimate the importance of some predictors; (3) several determinants of usefulness impact ease of use, and vice versa, thereby revealing crossover effects not previously revealed; and (4) the links leading up to SST acceptance in the proposed framework are moderated by SST type (transaction/self-help, kiosk/Internet, public/private, hedonic/utilitarian) and country culture (power distance, individualism, masculinity, uncertainty avoidance). Results from the meta-analysis offer managerial guidance for effective implementation of SSTs and provide directions for further research to augment current knowledge of SST acceptance.
Understanding customer experience and the customer journey over time is critical for firms. Customers now interact with firms through myriad touch points in multiple channels and media, and customer experiences are more social in nature. These changes require firms to integrate multiple business functions, and even external partners, in creating and delivering positive customer experiences. In this article, the authors aim to develop a stronger understanding of customer experience and the customer journey in this era of increasingly complex customer behavior. To achieve this goal, they examine existing definitions and conceptualizations of customer experience as a construct and provide a historical perspective of the roots of customer experience within marketing. Next, they attempt to bring together what is currently known about customer experience, customer journeys, and customer experience management. Finally, they identify critical areas for future research on this important topic.
Malicious crowdsourcing, also known as crowdturfing, has become an important security problem. However, detecting accounts performing crowdturfing tasks is challenging because human workers manage the crowdturfing accounts such that their characteristics are similar with the characteristics of normal accounts. In this paper, we propose a novel crowdturfing detection method, called CrowdTarget, that aims to detect target objects of crowdturfing tasks (e.g., post, page, and URL) not accounts performing the tasks. We identify that the manipulation patterns of target objects by crowdturfing workers are unique features to distinguish them from normal objects. We apply CrowdTarget to detect collusion-based crowdturfing services to manipulate account popularity on Twitter with artificial retweets. Evaluation results show that CrowdTarget can accurately distinguish tweets receiving crowdturfing retweets from normal tweets. When we fix the false-positive rate at 0.01, the best true-positive rate is up to 0.98.
Online reviews are a cornerstone of consumer decision making. However, their authenticity and quality has proven hard to control, especially as polluters target these reviews toward promoting products or in degrading competitors. In a troubling direction, the widespread growth of crowdsourcing platforms like Mechanical Turk has created a large-scale, potentially difficult-to-detect workforce of malicious review writers. Hence, this paper tackles the challenge of uncovering crowdsourced manipulation of online reviews through a three-part effort: (i) First, we propose a novel sampling method for identifying products that have been targeted for manipulation and a seed set of deceptive reviewers who have been enlisted through crowdsourcing platforms. (ii) Second, we augment this base set of deceptive reviewers through a reviewer-reviewer graph clustering approach based on a Markov Random Field where we define individual potentials (of single reviewers) and pair potentials (between two reviewers). (iii) Finally, we embed the results of this probabilistic model into a classification framework for detecting crowd-manipulated reviews. We find that the proposed approach achieves up to 0.96 AUC, outperforming both traditional detection methods and a SimRank-based alternative clustering approach.
This article looks at how brands can incorporate direct mail into automated marketing programmes and the benefits this channel can yield for businesses.
This book presents a comprehensive and practical discussion of the most important research tools and methods in today's sophisticated quantitative marketing professional's arsenal. The primary audience of this book are quantitative marketing professionals interested in the selection and implementation of marketing techniques relevant to their specific needs.
The book covers three main components of the quantitative marketing research profession. The first component refers to the importance of integrating marketing research, metrics, and data mining into the marketing investment process. The second is a detailed discussion of marketing research and data mining methods with a view to solve the practical needs of a marketing effort design and implementation. The third thrust of the book is the application of the various methodologies to illustrative case studies, representative of the common practice challenges marketing professionals confront.
We use data from a large-scale field experiment to explore what influences the effectiveness of online advertising. We find that matching an ad to website content and increasing an ad's obtrusiveness independently increase purchase intent. However, in combination, these two strategies are ineffective. Ads that match both website content and are obtrusive do worse at increasing purchase intent than ads that do only one or the other. This failure appears to be related to privacy concerns: the negative effect of combining targeting with obtrusiveness is strongest for people who refuse to give their income and for categories where privacy matters most. Our results suggest a possible explanation for the growing bifurcation in Internet advertising between highly targeted plain text ads and more visually striking but less targeted ads.
Marketing Automation - What it is and what it is notHistory and DefinitionThe term “Marketing automation” was first introduced by John D.C. Little in his presentation at the 5th Invitational Choice Symposium UC Berkeley 2001 (Little 2001) and refers to the automated marketing decision support on the Internet (Little 2001; Bucklin et al. 1998, 2002). Little (2001) formulated its key essence in the phrase “What do we tell retailer X to do when customer Y arrives on Monday morning?”. He suggested analyzing the digital footprints of customer Y and using appropriate models to come up with meaningful managerial implications for the whole purchase funnel. Such an automated marketing decision support promises enhanced productivity, better decision-making, higher returns on marketing investments, and increased customer satisfaction and loyalty through customization of marketing activities (Bucklin et al. 1998).The motivation for marketing automation in 2001 was just the same as today: the lack ...
Big data analytics have been embraced as a disruptive technology that will reshape business intelligence, which is a domain that relies on data analytics to gain business insights for better decision-making. Rooted in the recent literature, we investigate the landscape of big data analytics through the lens of a marketing mix framework in this paper. We identify the data sources, methods, and applications related to five important marketing perspectives, namely people, product, place, price, and promotion, that lay the foundation for marketing intelligence. We then discuss several challenging research issues and future directions of research in big data analytics and marketing related business intelligence in general.
The subject of automation in business has had extended coverage in the business press during recent months. Electronics has also captured the imagination of marketing men, but the areas for automation have not been clearly defined. This article answers some important questions and gives specific results that have been obtained.
This paper explores the reality of self-service today from the customer perspective and identifies what organisations need to do to maximise their contribution to improving customer service in the future. It is based on the findings of an extensive study, including original consumer research and analysis, undertaken by the Future Foundation that was sponsored by a consortium of companies consisting of Broadsystem, BT, mmO2, BUPA and GIMRA (General Insurance Market Research Association). It is structured around a series of questions that the research was designed to answer, with clear recommendations for businesses coming out of each section.