Voice Commerce (or voice shopping) is rapidly becoming a focal point in academic, business, and industry research because of its swift adoption and disruptive potential in buying dynamics. As voice assistants become better at learning consumer preferences and habits, they will increasingly influence consumer behaviors. In doing so, voice assistants may assume a central relational role in the consumer market and progressively mediate market interactions. These fast-changing market dynamics within the context of voice shopping may have a severe impact on consumer brands and retailers. Loss of brand visibility, the increased relevance of retailers’ private labels, and the growth in advertising costs are just some of the consequences anticipated by marketing and technology experts. In light of these potential dynamics, researchers are called to study the interplay between consumers, brands, and retailers’ behaviors in response to “machine behaviors”. Providing structure and guidance to researchers and marketers in order to further explore this emerging stream of research is fundamental.
The Evolution of Marketing in the Context of Voice Commerce: A Managerial Pe...Alex Mari
This paper examines managers’ perceptions of the evolution of voice assistants and their potential effects on the marketing practice. Shopping-related voice assistants are likely to radically change the way consumers search and purchase products with severe impact on brands. However, the behavior of these AI-enabled machines represents a “black box” for brand owners. The study of the managers’ interpretation of a voice-enabled marketplace is critical as it may influence future marketing choices.
Voice Commerce, Voice Shopping, or V-CommerceAlex Mari
Understanding shopping-related voice assistants and their effect on brands. Why are voice assistants relevant for brands and retailers? How is voice assistants diffusion affecting market dynamics?
Presented at IMMAA Annual Conference on Media and Innovation, Northwestern University in Qatar, Doha (Qatar), October 4-6, 2019.
SMAC and Innovation Transformation covers the topics:
• Innovation
• Leadership Agility
• Leading Organizational Change
• Lean Startup Principles
• SMAC and the Transformation of Innovation
Impact of M-Commerce Technologies on Developing Countriesijtsrd
M commerce is defined as any transaction with monetary value that is conducted via a mobile telecommunication network. M commerce like E commerce can be B2B business to business , P2P person to person or B2C business to customer oriented. The framework divides into couple sub areas based on user's distribution criterion. Mobile E commerce addresses electronic commerce via mobile devices, where the consumer is not in physical or eye contact with the goods that are being purchased. On the contrary in M trade the consumer has eye contact with offered products and services. In both case the payment procedure is executed via the mobile network. Prof. Rekha D. M | Divya. L "Impact of M-Commerce Technologies on Developing Countries" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29410.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29410/impact-of-m-commerce-technologies-on-developing-countries/prof-rekha-d-m
Exploring Key Factors Affecting the Adoption of Mobile Commerce: A Case of Pu...paperpublications3
Abstract: The purpose of this research is how to apply TAM model proposed by Davis, F. D. (1989) and Marketing factor to improve the purchasing intention on luxury fashion products via Mobile commerce. Conceptual Framework is mainly based on 3 hypotheses, 7 sub variables. (Fashion Innovativeness, Fashion Involvement, Brand image, and the constructs Perceived usefulness, Perceived ease of use, Social influence, Security) Quantitative research is used to determine the relationship between independent variable and dependent variable. All hypotheses tested factor analysis, Reliability, Correlations, and Multiple regressions. Data were collected from 253 respondents through online questionnaires within the period of 1st of February to 25th of March 2016. Computer program Statistical Package for the social science (SPSS) software was used to analyze the data. Our results illustrated that all variables have positive related on purchase intention. There is no doubt Mobile technologies have the potential to bring changes to traditional shopping.
The Evolution of Marketing in the Context of Voice Commerce: A Managerial Pe...Alex Mari
This paper examines managers’ perceptions of the evolution of voice assistants and their potential effects on the marketing practice. Shopping-related voice assistants are likely to radically change the way consumers search and purchase products with severe impact on brands. However, the behavior of these AI-enabled machines represents a “black box” for brand owners. The study of the managers’ interpretation of a voice-enabled marketplace is critical as it may influence future marketing choices.
Voice Commerce, Voice Shopping, or V-CommerceAlex Mari
Understanding shopping-related voice assistants and their effect on brands. Why are voice assistants relevant for brands and retailers? How is voice assistants diffusion affecting market dynamics?
Presented at IMMAA Annual Conference on Media and Innovation, Northwestern University in Qatar, Doha (Qatar), October 4-6, 2019.
SMAC and Innovation Transformation covers the topics:
• Innovation
• Leadership Agility
• Leading Organizational Change
• Lean Startup Principles
• SMAC and the Transformation of Innovation
Impact of M-Commerce Technologies on Developing Countriesijtsrd
M commerce is defined as any transaction with monetary value that is conducted via a mobile telecommunication network. M commerce like E commerce can be B2B business to business , P2P person to person or B2C business to customer oriented. The framework divides into couple sub areas based on user's distribution criterion. Mobile E commerce addresses electronic commerce via mobile devices, where the consumer is not in physical or eye contact with the goods that are being purchased. On the contrary in M trade the consumer has eye contact with offered products and services. In both case the payment procedure is executed via the mobile network. Prof. Rekha D. M | Divya. L "Impact of M-Commerce Technologies on Developing Countries" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29410.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29410/impact-of-m-commerce-technologies-on-developing-countries/prof-rekha-d-m
Exploring Key Factors Affecting the Adoption of Mobile Commerce: A Case of Pu...paperpublications3
Abstract: The purpose of this research is how to apply TAM model proposed by Davis, F. D. (1989) and Marketing factor to improve the purchasing intention on luxury fashion products via Mobile commerce. Conceptual Framework is mainly based on 3 hypotheses, 7 sub variables. (Fashion Innovativeness, Fashion Involvement, Brand image, and the constructs Perceived usefulness, Perceived ease of use, Social influence, Security) Quantitative research is used to determine the relationship between independent variable and dependent variable. All hypotheses tested factor analysis, Reliability, Correlations, and Multiple regressions. Data were collected from 253 respondents through online questionnaires within the period of 1st of February to 25th of March 2016. Computer program Statistical Package for the social science (SPSS) software was used to analyze the data. Our results illustrated that all variables have positive related on purchase intention. There is no doubt Mobile technologies have the potential to bring changes to traditional shopping.
Mobile Payments: How U.S. Banks Can Deal with Disruptive ChangeCognizant
Banks are well-positioned to benefit from mobile payments, if they invest in the technology infrastructure and forge strong partnerships to out-maneuver emerging non-traditional players.
When Device Recognitio an Programmatic Buying IntersectAdTruth
Mobile presents a major challenge to marketers: how to recognize and reach audiences programmatically, at scale, with support for sophisticated targeting and measruemtn models- whie still adhereding to consumer privacy best practices. This paper describes how mobile RTB - enabled by a new approach to device identification - meet this challenge.
The Growth Imperative: How Communications Service Providers Can Get their Moj...Cognizant
To thrive in new market realities, communications service providers must embrace human-centric customer service, enter adjacent verticals and monetize their data.
Understanding Online Consumer Purchase Behaviour for Varied Consumer Clusters...inventionjournals
: We are living in a digital age. The wave of digitalisation is in full swing to make its presence felt
in every sphere of life. It has not just challenged the geographical limitations narrowing the gaps between the
places that are situated faraway from each other and made the life easier with huge number of facilities but has
also influenced our attitudes and values. In such a situation, online shopping has started emerging as a popular
shopping option among urban and modernised consumers. Not all the consumers show similar trends while
using ecommerce. People of different clusters have different comfort zones as far as online shopping behaviour
is concerned. Product preferences vary with a change in demographics. Level of satisfaction is also different for
different set of consumers. Though window shopping is no longer an alien concept even for the internet
immigrants, purpose of use is certainly different for every age group. Literatures reveal that the attractive
features of the medium have tempted many researchers from time to time to throw light on lesser known areas
but there is still much to be explored. This paper is an endeavour to study about the potential of the medium to
market consumer electronics analysing in details the electronic shopping behaviour of different age groups. A
random survey has been conducted among the people of the age group of 19 – 35 (n – 100) and the samples
have been divided into four groups 19 – 22, 23 -26, 27 – 30 and 30 – 35. Reactions have been studied through
a questionnaire containing close ended questions. Analysis of data has been done through data graphs
Deloitte and Facebook team up to dissect reams of data, and then tell us that they have found out that "....young mums upload more pics after having their kids..."
IBM Retail | Meeting the demands of the smarter consumerIBM Retail
New technologies and socioeconomic trends are changing the retail marketplace. Discover the survey results of over 32,000 consumers to find out how their buying behaviors are evolving and what smarter consumers will demand from retailers in the future.
Digital Engineering: Combining Computer Science with Social Science to Transl...Cognizant
By digging deep to understand consumer behaviors, needs and wants, organizations can build systems that not only meet essential user needs but also uncover new business opportunities and anticipate future requirements.
The Sharing Economy: Implications for Property & Casualty InsurersCognizant
Collaborative consumption, also known as the "Peer-to-Peer" or "P2P" economy, poses significant risks for insurers. At the same time, consumers' willingness to share and utilize assets and services like Uber and Airbnb offers significant revenue opportunities for P&C carriers at a time when most have experienced flat-line growth.
An Analysis of Customer Awareness Level of Mobile Commerce Applications for B...inventionjournals
As more and more mobile devices, such as smart phones and tablets have become more pervasive and affordable, The demand for mobile commerce have been increased and it has become the hottest issue of present scenario in the developing countries like India. The demands for business to consumer’s applications of mobile commerce have been increasing with the number of increasing customer base for mobile companies. This study is conducted to find out the customers awareness about M-commerce applications for B2C operations and the reason for opting these Applications, thus this study is carried out in Semi Urban/Towns of Uttar Pradesh. These objectives are achieved by circulating a set of schedule to examine the awareness and usage of M-commerce B2C application by the customers of Semi urban areas of Western Uttar Pradesh.
Conversational interfaces; Speaking with Irresponsible black-boxesRaúl Tabarés Gutiérrez
Conference paper presented at #4s2017 in Boston, Massachusetts.
Introduction;
During the last years we have witnessed how conversational interfaces have popped up in the digital landscape due to the great advances in Artificial Intelligence (AI) and Speech Recognition (SR). That has made possible that chatbots and virtual assistants became common in different platforms and devices. This emergence has been also coined as “conversation-as-a-platform” stressing the radical change that means to communicate with machines throughout the human voice in terms of user experience (UX). This emphasis in outlining a new version of the Web is not new as it was also something previously stressed in past techno-market paradigms like “Web 2.0” but it also reflects the need of political reflection about the introduction of emergent and pervasive technologies in our society. The development of these chatting agents mirrors the concentration of AI resources around a bunch of companies that lead the so-called “platform economy”.
Phygital: A new dimension in customer experienceeveris
Over the last few years, companies have asked themselves about the future of physical environments. With the growth of digital channels, customer behavior has changed and several have chosen the convenience offered by technology
Mobile Payments: How U.S. Banks Can Deal with Disruptive ChangeCognizant
Banks are well-positioned to benefit from mobile payments, if they invest in the technology infrastructure and forge strong partnerships to out-maneuver emerging non-traditional players.
When Device Recognitio an Programmatic Buying IntersectAdTruth
Mobile presents a major challenge to marketers: how to recognize and reach audiences programmatically, at scale, with support for sophisticated targeting and measruemtn models- whie still adhereding to consumer privacy best practices. This paper describes how mobile RTB - enabled by a new approach to device identification - meet this challenge.
The Growth Imperative: How Communications Service Providers Can Get their Moj...Cognizant
To thrive in new market realities, communications service providers must embrace human-centric customer service, enter adjacent verticals and monetize their data.
Understanding Online Consumer Purchase Behaviour for Varied Consumer Clusters...inventionjournals
: We are living in a digital age. The wave of digitalisation is in full swing to make its presence felt
in every sphere of life. It has not just challenged the geographical limitations narrowing the gaps between the
places that are situated faraway from each other and made the life easier with huge number of facilities but has
also influenced our attitudes and values. In such a situation, online shopping has started emerging as a popular
shopping option among urban and modernised consumers. Not all the consumers show similar trends while
using ecommerce. People of different clusters have different comfort zones as far as online shopping behaviour
is concerned. Product preferences vary with a change in demographics. Level of satisfaction is also different for
different set of consumers. Though window shopping is no longer an alien concept even for the internet
immigrants, purpose of use is certainly different for every age group. Literatures reveal that the attractive
features of the medium have tempted many researchers from time to time to throw light on lesser known areas
but there is still much to be explored. This paper is an endeavour to study about the potential of the medium to
market consumer electronics analysing in details the electronic shopping behaviour of different age groups. A
random survey has been conducted among the people of the age group of 19 – 35 (n – 100) and the samples
have been divided into four groups 19 – 22, 23 -26, 27 – 30 and 30 – 35. Reactions have been studied through
a questionnaire containing close ended questions. Analysis of data has been done through data graphs
Deloitte and Facebook team up to dissect reams of data, and then tell us that they have found out that "....young mums upload more pics after having their kids..."
IBM Retail | Meeting the demands of the smarter consumerIBM Retail
New technologies and socioeconomic trends are changing the retail marketplace. Discover the survey results of over 32,000 consumers to find out how their buying behaviors are evolving and what smarter consumers will demand from retailers in the future.
Digital Engineering: Combining Computer Science with Social Science to Transl...Cognizant
By digging deep to understand consumer behaviors, needs and wants, organizations can build systems that not only meet essential user needs but also uncover new business opportunities and anticipate future requirements.
The Sharing Economy: Implications for Property & Casualty InsurersCognizant
Collaborative consumption, also known as the "Peer-to-Peer" or "P2P" economy, poses significant risks for insurers. At the same time, consumers' willingness to share and utilize assets and services like Uber and Airbnb offers significant revenue opportunities for P&C carriers at a time when most have experienced flat-line growth.
An Analysis of Customer Awareness Level of Mobile Commerce Applications for B...inventionjournals
As more and more mobile devices, such as smart phones and tablets have become more pervasive and affordable, The demand for mobile commerce have been increased and it has become the hottest issue of present scenario in the developing countries like India. The demands for business to consumer’s applications of mobile commerce have been increasing with the number of increasing customer base for mobile companies. This study is conducted to find out the customers awareness about M-commerce applications for B2C operations and the reason for opting these Applications, thus this study is carried out in Semi Urban/Towns of Uttar Pradesh. These objectives are achieved by circulating a set of schedule to examine the awareness and usage of M-commerce B2C application by the customers of Semi urban areas of Western Uttar Pradesh.
Conversational interfaces; Speaking with Irresponsible black-boxesRaúl Tabarés Gutiérrez
Conference paper presented at #4s2017 in Boston, Massachusetts.
Introduction;
During the last years we have witnessed how conversational interfaces have popped up in the digital landscape due to the great advances in Artificial Intelligence (AI) and Speech Recognition (SR). That has made possible that chatbots and virtual assistants became common in different platforms and devices. This emergence has been also coined as “conversation-as-a-platform” stressing the radical change that means to communicate with machines throughout the human voice in terms of user experience (UX). This emphasis in outlining a new version of the Web is not new as it was also something previously stressed in past techno-market paradigms like “Web 2.0” but it also reflects the need of political reflection about the introduction of emergent and pervasive technologies in our society. The development of these chatting agents mirrors the concentration of AI resources around a bunch of companies that lead the so-called “platform economy”.
Phygital: A new dimension in customer experienceeveris
Over the last few years, companies have asked themselves about the future of physical environments. With the growth of digital channels, customer behavior has changed and several have chosen the convenience offered by technology
Reinventing VUI_Joy Of Learning (Master Thesis Dossier)Tarka Patil
Personal Research Project - IED Barcelona
This is the thesis research dossier for the Master Jury at IED Barcelona. It includes the detailed journey of my research in understanding voice user interface, exploration of the problem area with users, uncovering the user insights and innovative solution to tackle the user problems discovered.
‘Joy of Learning’ was the outcome of the project, which is an ideal solution for families to help them reduce screen time by using a virtual assistant that provides playful content and a healthy learning experience.
It was conceptualised after Interviewing 15+ families to understand the usage of smart home assistants and day-to-day pain points of parents with their kids’ well being and development.
It also demonstrates a B2B2C business model created by following design thinking, lean canvas model, minimum viable product design, customer value proposition.
For more information please visit:
https://www.behance.net/gallery/76209333/JOY-OF-LEARNING
An Implementation of Voice Assistant for Hospitalitysipij
Voice user interface has gained popularity in the recent years. A chatbot is a machine with the ability to answer automatically through a conversational interface. Instead of using mouse and keyboards as input and screen as output, a chatbot with extra voice user interface feature improve the system and enhance the user experience. A chatbot is considered as one of the most exceptional and promising expressions of human computer interaction. Voice-based chatbots or artificial intelligence (AI) devices transform humancomputer bidirectional interactions that allow users to navigate an interactive voice response (IVR) system with their voice generally using natural language. In this paper, we focus on voice based chatbots for mediating interactions between hotels and guests from both the hospitality technology providers’ and guests’ perspectives. A hotel web application with voice user interface was implemented which provides voice input/output interface to enhance the user experience. Speech recognition component was used to dictate the user voice input to text. Speech synthesis API was used for text to voice conversion. A closed domain question answering (cdQA) Natural Language Processing (NLP) solution was used for processingof query and return the best answer possible.
An Implementation of Voice Assistant for Hospitalitysipij
Voice user interface has gained popularity in the recent years. A chatbot is a machine with the ability to
answer automatically through a conversational interface. Instead of using mouse and keyboards as input
and screen as output, a chatbot with extra voice user interface feature improve the system and enhance the
user experience. A chatbot is considered as one of the most exceptional and promising expressions of
human computer interaction. Voice-based chatbots or artificial intelligence (AI) devices transform humancomputer bidirectional interactions that allow users to navigate an interactive voice response (IVR) system
with their voice generally using natural language. In this paper, we focus on voice based chatbots for
mediating interactions between hotels and guests from both the hospitality technology providers’ and
guests’ perspectives. A hotel web application with voice user interface was implemented which provides
voice input/output interface to enhance the user experience. Speech recognition component was used to
dictate the user voice input to text. Speech synthesis API was used for text to voice conversion. A closed
domain question answering (cdQA) Natural Language Processing (NLP) solution was used for processingof
query and return the best answer possible.
According to the eMarketer analysis, millennials engage with voice assistants about twice as much as Generation Xers do on a monthly basis. and the difference is expected to increase in the next three years as this ever-evolving digital world will look for optimisation in every possible way. IOT App Development Company have contributed to the rising popularity of Voice technology by integrating it to unexpected sectors.
The rapid developments in Artificial Intelligence (AI) and the intensification in the adoption of AI in domains such as autonomous vehicles, lethal weapon systems, robotics and alike pose serious challenges to governments as they must manage the scale and speed of socio-technical transitions occurring. While there is considerable literature emerging on various aspects of AI, governance of AI is a significantly underdeveloped area. The new applications of AI offer opportunities for increasing economic efficiency and quality of life, but they also generate unexpected and unintended consequences and pose new forms of risks that need to be addressed. To enhance the benefits from AI while minimising the adverse risks, governments worldwide need to understand better the scope and depth of the risks posed and develop regulatory and governance processes and structures to address these challenges. This introductory article unpacks AI and describes why the Governance of AI should be gaining far more attention given the myriad of challenges it presents. It then summarises the special issue articles and highlights their key contributions. This special issue introduces the multifaceted challenges of governance of AI, including emerging governance approaches to AI, policy capacity building, exploring legal and regulatory challenges of AI and Robotics, and outstanding issues and gaps that need attention. The special issue showcases the state-of-the-art in the governance of AI, aiming to enable researchers and practitioners to appreciate the challenges and complexities of AI governance and highlight future avenues for exploration.
Consumer Intention to Adopt Smartphone Apps: An Empirical Study of PakistanIOSRJBM
The paper investigates the relationship between the perceived usefulness, perceived ease of use, perceived enjoyment, social needs, subjective norms, self-efficacy and personal attitude with a mediating role of Intention to adopt smart phone apps and impact on the customer behavior. The current recognition of smart phones is a result of rapid development in smart phone apps that offer much kind of mobile persistent services. We used theory of planned behavior for consumer intention to adopt these apps. Our findings suggest that consumer intention is always based on these factors like perceived usefulness, perceived ease of use, perceived enjoyment, social needs, subjective norms, self-efficacy and personal attitude now a days and that intention to adopt apps will impact the consumer behavior
RESEARCH ARTICLEUSER SERVICE INNOVATION ON MOBILE PHONEP.docxaudeleypearl
RESEARCH ARTICLE
USER SERVICE INNOVATION ON MOBILE PHONE
PLATFORMS: INVESTIGATING IMPACTS OF LEAD
USERNESS, TOOLKIT SUPPORT,
AND DESIGN AUTONOMY1
Hua (Jonathan) Ye
Department of Information Systems and Operations Management, The University of Auckland Business School,
12 Grafton Road, Auckland 1142, NEW ZEALAND {[email protected]}
Atreyi Kankanhalli
Department of Information Systems and Analytics, National University of Singapore, 13 Computing Drive,
SINGAPORE 117417 {[email protected]}
User participation is increasingly being seen as a way to mitigate the challenges that firms face in innovation,
such as high costs and uncertainty of customer acceptance of their innovations. Thus, firms are establishing
online platforms to support users in innovating services, such as iOS and Android platforms for mobile data
service (MDS) innovation. Mobile phone platforms are characterized by technology (toolkits) and policy
(rules) components that could influence user’s innovation. Additionally, attributes of user innovators (lead
userness) are expected to drive their innovation behavior. Yet it is unclear how these characteristics jointly
impact users’ service innovation outcomes. To address this knowledge gap, we propose a model that builds
on user innovation theory and the work design literature to explain the influences of lead userness, design
autonomy, toolkit support, and their interactions on user’s innovation outcomes (innovation quantity) on these
platforms. We conceptualize toolkit support in terms of two constructs (i.e., ease of effort and exploration), and
design autonomy in terms of three constructs (i.e., decision-making autonomy, scheduling autonomy, and work-
method autonomy). The model was tested using survey and archival data from two dominant mobile phone
platforms (i.e., iOS and Android). As hypothesized, lead userness, exploration through toolkits, and ease of
effort through toolkits positively affect users’ innovation quantity. Additionally, decision-making autonomy
and work-method autonomy influence innovation quantity, but scheduling autonomy does not. Further, the pro-
posed three-way interactions between lead userness, toolkit support, and design autonomy constructs on users’
quantity of MDS innovation are largely supported. The findings enhance our understanding of user innovation
on mobile phone platforms.
1
Keywords: User innovation, mobile phone platform, design autonomy, toolkit support, lead userness, three-
way interaction
1Arun Rai was the accepting senior editor for this paper. Yulin Fang served as the associate editor.
The appendices for this paper are located in the “Online Supplements” section of the MIS Quarterly’s website (http://www.misq.org).
DOI: 10.25300/MISQ/2018/12361 MIS Quarterly Vol. 42 No. 1, pp. 165-187/March 2018 165
Ye & Kankanhalli/User Service Innovation on Mobile Phone Platforms
Introduction
Engaging customers or users in the process of service
innovation is incre ...
Conversational Commerce: Why Consumers Are Embracing Voice AssistantsCapgemini
The Digital Transformation Institute has launched its latest research report titled “Conversational Commerce: Why Consumers Are Embracing Voice Assistants in Their Lives”. The report helps answer why voice assistants provide a significant platform to brands and retailers to engage with their consumers through new and innovative mediums like Google Assistant, Amazon’s Alexa, and Apple’s Siri.
(Crestani et al., 2004) The proliferation of mobile devices and thMargaritoWhitt221
(Crestani et al., 2004) The proliferation of mobile devices and the ubiquity of computing and networking technologies have revolutionized how we access information. Mobile and ubiquitous information access is now an essential issue in human-computer interaction, information retrieval, and computer-supported cooperative work.
The International Workshop on Mobile and Ubiquitous Information Access (MobileHCI) was held in Udine, Italy, on September 8, 2003. It included user interface design issues, novel interaction techniques, context-aware applications, collaborative systems, and social implications of mobile computing. They provide a snapshot of the state-of-the-art in this rapidly evolving field. They will interest researchers and practitioners in human-computer interaction, information retrieval, and computer-supported cooperative work.
The workshop was organized by Fabio Crestani, Mark Dunlop, and Stefano Mizzaro. It was in conjunction with the Ninth International Conference on Human-Computer Interaction (HCI International 2003).
(Bace et al.,2020) Part of this is that it's challenging to quantify visual attention in mobile HCI. In a recent paper, Bace et al. tried to address this challenge by quantifying how often and for how long users look at their mobile devices.
The researchers found that, on average, users look at their devices around 46 times per day. They also found that users spend more time looking at their instruments when using them for communication purposes, such as text messaging or phone calls. This suggests a need for further research into how mobile devices can be designed to capture better and hold users' attention.
The researchers also found that users look at their devices more often in a social setting, such as a meeting or a party. This suggests that mobile devices may be distracting us from our social interactions.
In conclusion, the study provides valuable insights into how we can better understand and quantify visual attention in mobile HCI.
(Oulasvirta et al.,2005) Mobile HCI 2004 was a seminal conference on human-computer interaction with mobile technology. The meeting was highly successful, and its impact is still felt today.
The theme of the conference was "Experience and Reflection." This theme was reflected in the papers presented at the meeting, which covered a wide range of topics related to mobile HCI. The papers addressed user experience, design principles, interaction techniques, and evaluation methods in addition to these technical papers, keynote speeches, and panel discussions on various aspects of mobile HCI.
The Mobile HCI 2004 conference was an important event in the history of mobile HCI. It helped establish the field as a central research area, and its papers have significantly impacted how mobile HCI is conducted today.
(Jia, 2014) The limited display space of mobile devices is inadequate for simultaneously displaying all the information needed in context. This paper proposes a novel mobile ...
Similar to Voice Commerce: Understanding shopping-related voice assistants and their effect on brands (20)
SEO as the Backbone of Digital MarketingFelipe Bazon
In this talk Felipe Bazon will share how him and his team at Hedgehog Digital share our journey of making C-Levels alike, specially CMOS realize that SEO is the backbone of digital marketing by showing how SEO can contribute to brand awareness, reputation and authority and above all how to use SEO to create more robust global marketing strategies.
First Things First: Building and Effective Marketing Strategy
Too many companies (and marketers) jump straight into activation planning without formalizing a marketing strategy. It may seem tedious, but analyzing the mindset of your targeted audiences and identifying the messaging points most likely to resonate with them is time well spent. That process is also a great opportunity for marketers to collaborate with sales leaders and account managers on a galvanized go-to-market approach. I’ll walk you through the methods and tools we use with our clients to ensure campaign success.
Key Takeaways:
-Recognize the critical role of strategy in marketing
-Learn our approach for building an actionable, effective marketing strategy
-Receive templates and guides for developing a marketing strategy
Mastering Local SEO for Service Businesses in the AI Era is tailored specifically for local service providers like plumbers, dentists, and others seeking to dominate their local search landscape. This session delves into leveraging AI advancements to enhance your online visibility and search rankings through the Content Factory model, designed for creating high-impact, SEO-driven content. Discover the Dollar-a-Day advertising strategy, a cost-effective approach to boost your local SEO efforts and attract more customers with minimal investment. Gain practical insights on optimizing your online presence to meet the specific needs of local service seekers, ensuring your business not only appears but stands out in local searches. This concise, action-oriented workshop is your roadmap to navigating the complexities of digital marketing in the AI age, driving more leads, conversions, and ultimately, success for your local service business.
Key Takeaways:
Embrace AI for Local SEO: Learn to harness the power of AI technologies to optimize your website and content for local search. Understand the pivotal role AI plays in analyzing search trends and consumer behavior, enabling you to tailor your SEO strategies to meet the specific demands of your target local audience. Leverage the Content Factory Model: Discover the step-by-step process of creating SEO-optimized content at scale. This approach ensures a steady stream of high-quality content that engages local customers and boosts your search rankings. Get an action guide on implementing this model, complete with templates and scheduling strategies to maintain a consistent online presence. Maximize ROI with Dollar-a-Day Advertising: Dive into the cost-effective Dollar-a-Day advertising strategy that amplifies your visibility in local searches without breaking the bank. Learn how to strategically allocate your budget across platforms to target potential local customers effectively. The session includes an action guide on setting up, monitoring, and optimizing your ad campaigns to ensure maximum impact with minimal investment.
Gokila digital marketing| consultant| Coimbatoredmgokila
Myself Gokila digital marketing consultant located in Coimbatore other various types of digital marketing services such as SEM
SEO SMO SMM CAMPAIGNS content writing web design for all your business needs with affordable cost
Digital Marketing Services | Techvolt Software :
Digital Marketing is a latest method of Marketing techniques widely used across the Globe. Digital Marketing is an online marketing technique and methods used for all products and services through Search Engine and Social media advertisements. Previously the marketing techniques were used without using the internet via direct and indirect marketing strategies such as advertising through Telemarketing,Newspapers,Televisions,Posters etc.
List of Services offered in Digital Marketing |Techvolt Software :
Techvolt Software offers best Digital Marketing services for promoting your products and services through online platform on the below methods of Digital marketing
1. Search Engine Optimization (SEO)
2. Search Engine Marketing (SEM)
3. Social Media Optimization (SMO)
4. Social Media Marketing (SMM)
5. Campaigns
Importance | Need of Digital Marketing (Online Promotions) :
1. Quick Promotions through Online
2. Generation of More leads and Business Enquiries via Search Engine and Social Media Platform
3. Latest Technology development vs Business promotions
4. Creation of Social Branding
5. Promotion with less investment
Benefits Digital Marketing Services at Techvolt software :
1. Services offered with Affordable cost
2. Free Content writing
3. Free Dynamic Website design*
4. Best combo offers on website Hosting,design along with digital marketing services
5. Assured Lead Generation through Search Engine and Social Media
6. Online Maintenance Support
Free Website + Digital Marketing Services
Techvolt Software offers Free website design for all customer and clients who is availing the digital marketing services for a minimum period of 6 months.
With Regards
Gokila digital marketer
Coimbatore
Checkout Abandonment - CRO School by Mailmodosaba771143
Fear of abandonment’ means a whole different thing in eCommerce.
Because the loss is tangible. And felt right in your pocket.
But that also means there are real things you could fix.
One of the final stages of shopping abandonment occurs is the checkout page.
Which means it impacts your bottom line directly.
So here’s a rundown of:
→ Reasons shoppers abandon the checkout process
→ How other brands cope with these issues
→ Actionables to fix your checkout flow
Do it right, and you’ll feel the change in your revenue.
This is a part of our CRO School series - to help you fix the revenue leaks in your eCommerce store.
Sign up for CRO School and get these insights right in your inbox
(Visit the link to enroll ->https://www.mailmodo.com/cro-school/?utm_source=cro-school&utm_medium=slideshare )
#ecommerce
#cro
#cart
#abandonement
#checkout
#email
#course
#conversion
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
Elevate your trade show game with our comprehensive guide on creating an interactive booth that captures attention and drives engagement! In this presentation, Blue Atlas Marketing shares practical tips and creative strategies to transform your trade show presence. Learn how to use digital displays, interactive demos, and engaging activities to attract visitors and make lasting impressions. Whether you're a trade show veteran or a newcomer, these insights will help you stand out from the crowd and maximize your event success. Dive into our slides to discover how to turn your booth into a dynamic and interactive experience!
Exploring the Top Digital Marketing Company in CanadaSolomo Media
Choosing Solomo Media as your digital marketing company in Canada can propel your business to new heights. With their expertise, innovative solutions, and client-centric approach, they are well-equipped to help you achieve your digital marketing goals. By focusing on strategic planning, leveraging cutting-edge tools, and delivering measurable results, Solomo Media proves to be a valuable partner in navigating the complex world of digital marketing.
Most small businesses struggle to see marketing results. In this session, we will eliminate any confusion about what to do next, solving your marketing problems so your business can thrive. You’ll learn how to create a foundational marketing OS (operating system) based on neuroscience and backed by real-world results. You’ll be taught how to develop deep customer connections, and how to have your CRM dynamically segment and sell at any stage in the customer’s journey. By the end of the session, you’ll remove confusion and chaos and replace it with clarity and confidence for long-term marketing success.
Key Takeaways:
• Uncover the power of a foundational marketing system that dynamically communicates with prospects and customers on autopilot.
• Harness neuroscience and Tribal Alignment to transform your communication strategies, turning potential clients into fans and those fans into loyal customers.
• Discover the art of automated segmentation, pinpointing your most lucrative customers and identifying the optimal moments for successful conversions.
• Streamline your business with a content production plan that eliminates guesswork, wasted time, and money.
Mastering Dynamic Web Designing A Comprehensive Guide.pdfIbrandizer
Dynamic Web Designing involves creating interactive and adaptable web pages that respond to user input and change dynamically, enhancing user experience with real-time data, animations, and personalized content tailored to individual preferences.
Trust Element Assessment: How Your Online Presence Affects Outbound Lead Gene...Martal Group
Learn how your business's online presence affects outbound lead generation and what you can do to improve it with a complimentary 13-Point Trust Element Assessment.
Come learn how YOU can Animate and Illuminate the World with Generative AI's Explosive Power. Come sit in the driver's seat and learn to harness this great technology.
[Google March 2024 Update] How To Thrive: Content, Link Building & SEOSearch Engine Journal
March 2024 disrupted the SEO industry. Websites were deindexed, and manual penalties were delivered—all to produce more helpful, more trustworthy search results.
How did your website fare?
Watch us as we delve into the seismic shifts brought about by Google's March 2024 updates and explore strategies to not just survive, but thrive in this dynamic digital landscape.
You’ll learn:
- How to create content that is valuable to users (not just search engines) using E-E-A-T.
- How to build links that can boost rankings and withstand algorithm updates.
- Best practices for content creation and link building so you can thrive during algorithm updates.
With Vince Ramos, we'll examine the implications of the latest algorithm changes on content creation, link building, and SEO practices, and offer actionable insights from businesses like yours that have remained steadfast amidst the volatility.
Using real-life case studies, we’ll also show you the effectiveness of manual link building techniques and person-first content strategies.
Whether you're a seasoned SEO professional, a budding content creator, or anyone in between, this webinar will help you weather the changes in Google's algorithms and capitalize on them for sustained success.
Check out this webinar and unlock the secrets to thriving in the new Google era.
Google Ads Vs Social Media Ads-A comparative analysisakashrawdot
Explore the differences, advantages, and strategies of using Google Ads vs Social Media Ads for online advertising. This presentation will provide insights into how each platform operates, their unique features, and how they can be leveraged to achieve marketing goals.
In the digital age, businesses are inundated with tools promising to streamline operations, enhance creativity, and boost productivity. Yet, the true key to digital transformation lies not in the accumulation of tools but in strategically integrating the right AI solutions to revolutionize workflows. Join Jordache, an experienced entrepreneur, tech strategist and AI consultant, as he explores essential AI tools across three critical categories—Ideation, Creation, and Operations—that can reshape the way your business creates, operates, and scales.This talk will guide you through the practicalities of selecting and effectively using AI tools that go beyond the basics of today’s popular tools like ChatGPT, Claude, Gemini, Midjourney, or Dall-E. For each category of tools, Jordache will address three crucial questions: What is each tool? Why is each one valuable to you as a business leader? How can you start using it in your workflow? This approach will not only clarify the role of these tools but also highlight their strategic value, making it perfect for business leaders ready to make informed decisions about integrating AI into their workflows.
Key Takeaways:
>> Strategic Selection and Integration: Understand how to select AI tools that align with your business goals and how to conceptually integrate them into your workflows to enhance efficiency and innovation.
>> Understanding AI Tool Categories: Gain a deeper understanding of how AI tools can be leveraged in the areas of ideation, creation, and operation—transforming each aspect of your business.
>> Practical Starting Points: Learn how you can start using these tools in your business with practical tips on initial steps and integration ideas.
>> Future-Proofing Your Business: Discover how staying informed about and utilizing the latest AI tools and strategies can keep your business competitive in a rapidly evolving digital landscape.
Smart Tools, Smarter Business -15 AI Tools to Optimize Your Workflows from Id...
Voice Commerce: Understanding shopping-related voice assistants and their effect on brands
1. Voice Commerce
Understanding shopping-related voice assistants and their
effect on brands
Alex Mari
University of Zurich, Switzerland
Alex Mari (alex.mari@business.uzh.ch) is a Research Associate and Ph.D. candidate at the Chair for Marketing and
Market Research, University of Zurich.
Introduction
Artificial intelligence (AI) technologies have left the server room to enter the lives of billions of consumers. AI enables
objects to perform activities that resemble cognitive functions associated with the human mind, such as learning and
problem solving (Russell & Norvig, 2009). AI-powered smartphones, smart homes, and smart speakers connect the
various nodes of consumers’ lives into one ubiquitous experience that seamlessly accompanies them in every routine.
Every intelligent object, from cars to toothbrushes, is expected to collect relevant information that helps to identify
consumption patterns and predict future individual behaviors (Hoffman & Novak, 2017). Within the Internet of Things
(IoT) market, the fast adoption and rising performance of voice platforms like Amazon Echo, Apple HomePod, Google
Home, Alibaba Tmall Genie, Xiaomi Xiao AI, and Baidu Xiaodu suggest that in-home voice assistants will be central
to the development of smart homes. The voice touchpoint is rapidly becoming a focal point in academic, business,
and industry research because of its swift adoption and disruptive potential in buying dynamics (Dawar & Bendle,
2018). Given its interdisciplinary nature, the research on voice assistants is highly fragmented with contributions from
a variety of disciplines (Knote et al., 2018). Recent studies have produced insights on the functional characteristics of
voice assistants (Sciuto et al., 2018; Gollnhofer & Schüller, 2018, Hoy, 2018), their adoption and social roles (Han &
Yang, 2018; Purington et al., 2017; Schweitzer et al., 2019), attitudes towards the technology (Moriuchi, 2019;
Ahmadian & Lee, 2017; Brill, 2018), and applications for marketing (Kumar et al., 2016). However, these
investigations have not led to a deeper understanding of consumer judgment and behavior towards brands. At the same
time, studies on consumer technologies for shopping, such as—personal computers, smartphones, tablets—seem
insufficient to understand the unique nature of this new channel and shopping method. Although exemplary research
on consumer behavior and media possess insights that are likely transferable to voice assistants, the peculiarities of
this technology require new theories that are not yet fully developed (Kumar et al., 2016). This study sheds light on
the potential impact that the diffusion of shopping-related voice assistants has on consumer brands. The main
contribution is to reconcile existing interdisciplinary literature and review how voice assistants may alter market
dynamics as emerged during in-depth interviews with 31 AI-aware executives. Key conceptual nodes related to the
dual agency/mediator role of voice assistants and their anticipated effects on consumer brands are explored.
Keywords: Voice assistants (VA), voice commerce, artificial intelligence, machine behavior, brand management.
Citation: Mari, A. (2019). Voice Commerce: Understanding shopping-related voice assistants and their effect on
brands. In: IMMAA Annual Conference. Northwestern University in Qatar, Doha (Qatar). October 4-6, 2019.
2. The rise of voice assistants for shopping
The term voice assistant (VA) refers to conversational agents that perform tasks with or for an individual,
whether of functional or social nature and own the ability to self-improve their understanding of the interlocutor and
context. This software, embedded in smart objects, leverages a combination of AI techniques, such as automatic
speech recognition (ASR), text-to-speech synthesis (TTS), and natural language understanding (NLU), to engage in
natural conversational interactions with humans (Gaikwad, Gawali & Yannawar, 2010). Such category of IoT goes
under various names that include but are not limited to smart speaker (Bentley et al., 2018), AI assistant (Dawar &
Bendle, 2018), intelligent personal assistant (Han & Yang, 2018), personal digital assistant (Milhorat et al., 2014),
voice-controlled smart assistant (Schweitzer et al., 2019), voice-activated intelligent assistant (Jiang et al., 2015), and
conversational agent (Lee & Choi, 2017).
Voice assistants can take various forms of in-place and mobile devices such as Bluetooth speakers (e.g.,
Amazon Echo) or built-in software agents for smartphones and computers (e.g., Apple Siri). With over 70 million
U.S. owners, in-home voice assistants currently see a faster adoption rate than smartphones and tablets (Newman,
2018). Their most popular functions are playing music, controlling smart home appliances, providing weather
information, answering general knowledge questions, and setting alarms (Sciuto et al., 2018). However, from a
commercial standpoint, digital assistants represent a novel touchpoint that allows for new forms of interaction between
consumers and brands (Sterne, 2017).
Voice commerce (or voice shopping) identifies the act of placing orders online using voice assistants. This
topic captures mainstream media headlines (e.g., Creswell, 2018; Chaudhuri & Terlep, 2018) and is often used to
speculate about the dominance of U.S. tech giants—Google, Amazon, Apple (see Galloway, 2017). Although the
number of consumers who have completed at least one purchase through a smart speaker is rising fast, the percentage
of buyers using VAs varies widely among product categories. A report suggests that 21% of U.S. smart speaker owners
have purchased entertainment such as music or movies, 8% household items, and 7% electronic devices (eMarketer,
2019). Meanwhile, Alexa’s users can order items like household products and fresh produce from a local Whole Foods
and receive delivery within two hours.
Functional characteristics of voice assistants
Unique from other consumer applications, VAs can converse with users naturally, interpret and handle
requests contextually, expand their knowledge, and learn from mistakes.
Natural conversation represents the main difference in this new communication channel. Voice assistants are
built to mimic human-to-human interactions. Similar to interpersonal relationships, VAs react to the interlocutor when
their name is called (Sacks & Schegloff, 1979). VAs can “memorize” relevant facts from previous conversations,
giving a sense of continuity from past interactions. Also, they assume a persona and refer to themselves as “I.” For
instance, when asking Google Home, “Okay Google, what do you think about Alexa?” the answer is, “I like her blue
light.” VAs’ ability to naturally dialog with users as well as the sense of “spontaneity” that originates from unexpected
answers can facilitate the emergence of closeness feelings (Han & Yang, 2018).
Context-awareness is a constituting factor of VAs (Knote et al., 2018). The context of a device is represented
by any information that can be used to characterize a situation relevant to its users (Abowd et al., 1999). Context-
aware computing collects and processes information about the context of a device in order to customize services to
the particular contextual clues such as the identity of the user, location of the device, time and date, purchasing history,
and declared user preferences (Kwon, 2003). Ultimately, a VA becomes context-aware if its interactions with the
3. human, and other machines, are personalized to the current context. Contextual information is necessary to precisely
learn personal preferences and automate routines (Milhorat et al., 2014).
Self-learning allows VAs to interpret customers’ words better and reduce friction during interactions
(Sarikaya, 2017). With the recent introduction of unsupervised systems, which operate without manual human
annotation, VAs can detect unsatisfactory interactions or failures of understanding and automatically recover from
these errors. For instance, if the user says “Play ‘Good for What’” but meant to say “Nice for What” by Drake, the
VA corrects the error and initiates a successful song request (Sarikaya, 2018). The system learns how to address these
accuracy issues and deploys updates shortly after. Automatically applying corrections to a large number of queries
each day using self-learning techniques allows VAs to develop at a faster pace.
Interactional characteristics of voice assistants
The uniqueness of VA technologies brings up a new set of interaction rules modeled after the active (and
proactive) nature of these smart devices (Rijsdijk & Hultink, 2009). In contrast to traditional media, voice touchpoints
emphasize a bidirectional interaction with consumers. VAs are designed to process one request at a time and on a turn-
by-turn basis to decrease the speech recognition error rate coming from a possible voice overlap (Hansen, 1996). This
style of interaction represents a radical difference compared to sensorially richer devices like computers or
smartphones, which present multiple pieces of information on a screen concurrently. As such, voice channels present
both challenges and opportunities for the diffusion of voice commerce.
On the positive side, e-commerce has paved the way for voice shopping (Labecki, Klaus & Zaichkowsky,
2018). With the rise of the Internet, users have learned to deal with a combination of social, cultural, economic, and
technical barriers. In doing so, they have needed to overcome the initial diffidence of buying without directly seeing,
touching, or smelling an object. Voice technologies further limit the users’ senses; besides, consumers are asked to
make shopping decisions without browsing photos, videos, or any other animated content. Another celebrated feature
of voice shopping is the ease of making low involvement purchases. VAs are “always on” devices that can access a
user’s personal information upon request (Clark, Dutta & Newman, 2016). With a simple “yes” and without providing
additional information such as credit cards or address details, VAs can process orders, or even automate them.
On the negative side, an effortless decision-making process does not guarantee an optimal level of consumer
satisfaction. Shopping-related voice assistants offer a limited set of items for each product category based on their
understanding of the consumer and context. This simplified representation of the marketplace reduces consumers’
visibility of product alternatives and emphasize the critical role of ranking algorithms. The algorithm that ranks the
information represents a “black box” for the VA user, and often for its developers (Voosen, 2017). Such visual
limitations may increase product (and brand) polarization while enhancing the risk of the so-called filter bubble or
echo-chamber effects (Colleoni, Rozza & Arvidsson, 2014).
Methodology
A total of 31 semi-structured in-depth interviews were conducted in December 2018 to supplement an
interdisciplinary literature review. During the interviews with international AI-aware corporate executives and
consultants, theoretical perspectives were not employed to facilitate the emergence of insights (Avis, 2003). Interviews
were audio-taped, and transcriptions analyzed adopting an inductive line-by-line coding approach. This process
followed a constant comparative data analysis according to the grounded theory (Glaser & Strauss, 1967). Using
NVivo 12 for Mac, codes were grouped into themes and then re-evaluated to ensure that they reflect data extracts.
4. Through conceptualization, relationships among categories and sub-categories were established (Figure 1). The
emerging conceptual nodes were related to the dual agency and mediator roles of VAs as well as their main anticipated
effects on consumer brands.
Figure 1. Key conceptual nodes emerged during qualitative investigation.
The agency role of voice assistants
In their recommender agent role, VAs attempt to predict which items a target user will like based on expressed
preferences or implicit behaviors (Shen, 2014). This form of recommender system may replace traditional decision-
making when consumers feel time constraints or recognize the referrer as a particularly knowledgeable source
(Olshavsky & Granbois, 1979). End-users typically evaluate a virtual agent on its ability to personalize suggestions
that satisfy their needs. Consumers adopt algorithmic recommender systems if they are believed to match their interests
(Abdollahpouri et al., 2019). Higher accuracy of suggestions from a platform translates into not only an increase in
consumer satisfaction but also their overall trust in the technology (Li & Karahanna, 2015). In this context,
recommendation outcomes may correspond to consumer preferences more closely than if they had chosen
independently (André et al., 2018).
Due to their central role in a complex business network (Snehota & Hakansson, 1995), VAs do not consider
users as the only stakeholders benefiting from the recommendation outcome. The strategic goals of the retailer,
merchant, advertiser, and voice assistant itself, may differ from those of end-users. Thus, the user is not the sole focus
of a recommendation in almost every transaction on the VA. For instance, a VA might recommend a private label
over a consumer brand following the retailer’s objective to swiftly grow its shares in a specific product category. Thus,
the objectives of several parties need to coexist (Abdollahpouri et al., 2019).
The ultimate goal of recommendation personalization is the automation of the buying experience. Throughout
the collection of significant volumes of personal and behavioral information, VAs can push users to automate
repurchase, for instance, via “subscribe & save” promotional activities, increasingly popular on the e-commerce
websites. According to André et al. (2018), this power of attorney towards VAs goes at the expense of higher-order
psychological processes such as emotions and moral judgments. In the context of purchase automation, consumers
might have aspirational preferences that differ from the preferences suggested by their past behavior. These meta
preferences, also called preferences over preferences (Jeffrey, 1974), are apparent in the case of an environmentally
aware person who wants to use less bottled water but is regularly reminded to buy plastic bottles. The inherent tension
5. between the actual-self and the ideal-self represents a boundary for those consumers who follow VAs’ suggestions to
automate repurchases.
There is a brand of soap that my wife loves. One day the Amazon says, “Hey, you buy this all the time, why don't you
subscribe?”. Now, we have a subscription to soap, and every six months we get a bunch. If we have more than we need, we
adjust the delivery frequency. This product automatically shows up, and we are definitely going to buy the same brand. We are
locked in.
- Jim Sterne, Emeritus Director of the Digital Analytics Association (DAA), Author of “AI for Marketing.”
While functioning as a salesperson, VAs are redefining relationships among consumers, brands, and retailers
(Figure 2). As consumers’ relationships with VAs shift from limited influence to steadfast dependency, brands need
to understand which elements influence consumer choices and how to redesign their value chain (Mandelli, 2018).
Consumer brands feel threatened by the rapid adoption of VAs as the bargaining power is shifting in favor of VA
technology owners (Dawar & Bendle, 2018; WSJ, 2018). In the case of Amazon’s Alexa, the VA manufacturer is also
the retailer behind the most advanced voice shopping functionality, accounting for nearly 45% of the total U.S. retail
e-commerce (eMarketer, 2018).
Figure 2. Triadic relationship between brand, retailer, and consumer mediated by a voice assistant.
The impact of market mediation on consumer brands and retailers
VAs’ increasing mediation of consumer interactions with the market does affect the path to purchase
dynamics. The main concerns from consumer brands around VAs’ diffusion are related to brand visibility via organic
search results, the rise of retailers’ private labels, and the potential increase in advertising spending.
Search algorithms represent the gatekeeper for modern companies and retailers. Compared to display-
enabled smart devices, the optimization of voice search results on VAs presents three structural challenges due to the
nature of consumer interactions and information framing. First, during voice shopping users can review one to three
options before they start forgetting information such as price or quantity of the mentioned products. Reduced attention
span and short-term memory can negatively influence the satisfaction towards this shopping system, especially when
the user is required to search for products in an explorative way extensively. Second, VAs deliver search results to
users in the form of recommendations. The assistive nature of the interaction with VAs implies a delegation of
responsibility, at least in the absence of explicit requests by the user. Whenever a user directly asks for a specific brand
6. or product, VAs respond with the closest option available to them. However, when shopping for items without
specifying a brand, VAs are more likely to recommend their private labels, if available. In the case of Alexa, when a
brand name is not proactively mentioned by the user, the private label, under the name of Amazon’s Choice, appears
as the first recommendation in over 50% of instances (Cheris, Rigby & Tager, 2017). Third, the search engine results
continuously adapt to the user’s purchase history and the evolving understanding that VA acquires about its
interlocutor. However, after a user has purchased a product, for example, Nespresso coffee capsules, the subsequent
suggestions for coffee start from the same manufacturer. As such, this dynamic might reduce variety seeking in
shoppers.
Alexa does commoditize entire product categories, all the way from diamonds to detergents. During a product search, by the
time you get to the third item, you have forgotten what the first was and what the price of the second one was. You’re done
beyond the third results. You’ve become a commodity fighting for air space.
- Dr. A. K. Pradeep, CEO at MachineVantage, Co-author of “AI for marketing and product innovation.”
Private label development is seen as particularly dangerous by national brands (see Quelch & Harding,
1996). In utilitarian product categories characterized by low purchase involvement, the parallel expansion of private
labels and VAs represent a risk for category “commoditization” (Pradeep, Appel & Sthanunathan, 2018). An
emblematic example of this process comes from the battery business. A few years after its launch in 2009, Amazon’s
private label “AmazonBasics” accounts for 31% of the overall battery sales online by large margins from national
brands such as Duracell (21%) and Energizer (12%) (Neff, 2016). The price of private labels is reported to be 20%-
30% lower than national brands, on average (Collins & Metz, 2018). With Amazon’s private label portfolio growing
to 135 brands and more than 330 Amazon exclusive brands, similar trends gradually become visible in a variety of
product categories such as skincare, home improvement tool, and golf equipment (Jumpshot, 2018). The limited
“shelf space” available to merchants on in-home smart devices strengthens the private brands’ position. According
to Cheris, Rigby, and Tager (2017), in categories in which Amazon offers private-label products, Alexa recommends
the private-label products 17% of the time, although these products represent only about 2% of the total volume sold.
Amazon’s biased placement on VAs of its private labels against national brands challenges the traditional retail
marketing practice that expects a distribution of a given brand, “share of shelf,” proportional to its sales, “market
share.” Furthermore, consumers can decide to automate fully (e.g., subscription) or semi-automate (e.g., product
added to the shopping list) their purchases creating self-established lock-in mechanisms.
If I ask Alexa to send me twenty AA batteries, I will probably get Amazon’s branded batteries. However, if I explicitly ask for
Duracell, I receive my preferred brand, provided it is available on the platform. Thus, companies have to invest in branding
even more than they did before so that consumers asked for a product by the name.
- Jim Sterne, Emeritus Director of the Digital Analytics Association (DAA), Author of “AI for Marketing.”
For decades, advertising represented the primary tool to generate brand awareness, improving both recall
and recognition. With the rise of the Internet, the concept of advertising transmuted to search engines where
advertisers buy promotional spaces in response to a set of keywords searched by the user. Within digital advertising,
“search advertising” represents the most successful format, accounting for 45% of the total spending (IAB & PWC,
2018). Advertisers face an overall cost increase of search ads with a particular impact on highly competitive consumer
products. For instance, the cost per click on the search term “laundry detergent liquid” reached $17 on Amazon in a
7. given period (Koksal, 2018). Search advertising in the form of voice has a paramount role in voice commerce
marketing. Although brands are generally positive towards this new form of investment, the peculiarities of the voice
channel induce concerns. Compared to web browser navigation where search engines can display ten results per page
and up to five advertisements, VAs can only suggest a few results with limited space for sponsored messages. This
scarcity of space might increase competition among advertisers with a consequent rise in advertising costs.
From the voice commerce perspective, VAs pose a challenge to advertisers. They bring up a “real estate” problem. While I can
display several ads on the same Google Search results page, I don’t have the same ad space on smart speakers. Thus, I expect
the cost of voice ads to be more than two times higher than regular search ads. Am I able to justify this cost increase?
- Maurizio Miggiano, Head of Digital at Generali (Ex Mediacom).
Conclusions
As voice assistants become better at learning consumer preferences and habits, they will increasingly influence
consumer behaviors (Simms, 2019). In doing so, VAs may assume a central relational role in the consumer market
and progressively mediate market interactions. These fast-changing market dynamics within the context of voice
shopping may have a severe impact on consumer brands and retailers. Loss of brand visibility, the increased relevance
of retailers’ private labels, and the growth in advertising costs are just some of the consequences anticipated by
marketing and technology experts. In light of these potential dynamics, researchers are called to study the interplay
between consumers, brands, and retailers’ behaviors in response to “machine behaviors” (Rahwan et al., 2019).
Providing structure and guidance to researchers and marketers in order to further explore this emerging stream of
research is fundamental.
References
1. Abdollahpouri, H., Adomavicius, G., Burke, R., Guy, I., Jannach, D., Kamishima, T., ... & Pizzato, L. (2019). Beyond Personalization:
Research Directions in Multistakeholder Recommendation. arXiv preprint arXiv:1905.01986.
2. Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., & Steggles, P. (1999). Towards a better understanding of context and
context-awareness. In International symposium on handheld and ubiquitous computing (pp. 304-307). Springer, Berlin, Heidelberg.
3. Ahmadian, M., & Lee, O. K. D. (2017). AI-based voice assistant systems: Evaluating from the interaction and trust perspectives.
4. André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., ... & Yang, H. (2018). Consumer choice and autonomy in the
age of artificial intelligence and big data. Customer Needs and Solutions, 5(1-2), 28-37.
5. Avis, M. (2003). Do we need methodological theory to do qualitative research?. Qualitative health research, 13(7), 995-1004.
6. Bentley, F., Luvogt, C., Silverman, M., Wirasinghe, R., White, B., & Lottrjdge, D. (2018). Understanding the long-term use of smart
speaker assistants. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(3), 91.
7. Brill, T. M. (2018). Siri, Alexa, and Other Digital Assistants: A Study of Customer Satisfaction With Artificial Intelligence Applications.
DBA Dissertation.
8. Chaudhuri, S., & Terlep, S. (2018, February 27). The Next Big Threat to Consumer Brands (Yes, Amazon’s Behind It). The Wall Street
Journal. Retrieved July 29, 2019, from https://www.wsj.com/articles/big-consumer-brands-dont-have-an-answer-for-alexa-1519727401
9. Cheris, A., Rigby, D., & Tager, S. (2017, November 9). Dreaming of an Amazon Christmas?. Bain & Company. Retrieved August 3, 2019,
8. from https://www.bain.com/insights/retail-holiday-newsletter-2017-issue-2
10. Clark, M., Dutta, P., & Newman, M. W. (2016). Towards a natural language programming interface for smart homes. In Proceedings of the
2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 49–52). Germany.
11. Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political
homophily in Twitter using big data. Journal of communication, 64(2), 317-332.
12. Collins, K., & Metz, C. (2018, August 17). Alexa vs. Siri vs. Google: Which Can Carry on a Conversation Best?. The New York Times.
Retrieved July 23, 2019, from https://www.nytimes.com/interactive/2018/08/17/technology/alexa-siri-conversation.html
13. Creswell, J. (2018, June 23). How Amazon Steers Shoppers to Its Own Products. The New York Times. Retrieved July 22, 2019, from
https://www.nytimes.com/2018/06/23/business/amazon-the-brand-buster.html
14. Dawar, N., & Bendle, N. (2018). Marketing in the Age of Alexa. Harvard Business Review, 96(3), 80-86.
15. eMarketer (2018, May 29). Net Digital Ad Revenue Share Worldwide, by Company, 2016-2019. Retrieved from
https://www.emarketer.com/Chart/Net-Digital-Ad-Revenue-Share-Worldwide-by-Company-2016-2019-of-total-billions/205364
16. eMarketer (2019, June 28). Which Select Activities Have US Smart Speaker Owners Done on Their Smart Speakers. Retrieved from
https://www.emarketer.com/Chart/Which-Select-Activities-Have-US-Smart-Speaker-Owners-Done-on-Their-Smart-Speakers-of-
respondents-by-demographic-June-2019/229112
17. Gaikwad, S. K., Gawali, B. W., & Yannawar, P. (2010). A review on speech recognition technique. International Journal of Computer
Applications, 10(3), 16-24.
18. Galloway, S. (2017). The four: the hidden DNA of Amazon, Apple, Facebook and Google. Random House.
19. Glaser, B., & Strauss, A. (1967). Grounded theory: The discovery of grounded theory. Sociology the journal of the British sociological
association, 12(1), 27-49.
20. Gollnhofer, J. F., & Schüller, S. (2018). Sensing the Vocal Age: Managing Voice Touchpoints on Alexa. Marketing Review St. Gallen,
35(4), 22-29.
21. Han, S., & Yang, H. (2018). Understanding adoption of intelligent personal assistants: a parasocial relationship perspective. Industrial
Management & Data Systems, 118(3), 618-636.
22. Hansen, J. H. (1996). Analysis and compensation of speech under stress and noise for environmental robustness in speech recognition.
Speech communication, 20(1-2), 151-173.
23. Hoffman, D. L., & Novak, T. P. (2017). Consumer and object experience in the internet of things: An assemblage theory approach. Journal
of Consumer Research, 44(6), 1178-1204.
24. Hoy, M. B. (2018). Alexa, Siri, Cortana, and more: An introduction to voice assistants. Medical Reference Services Quarterly, 37(1), 81–88.
25. IAB & PWC (2019). IAB Internet Advertising Revenue Report 2018 Full Year Results. Retrieved from https://www.iab.com/insights/iab-
internet-advertising-revenue-report-2018-full-year-results/
26. Jeffrey R.C. (1974). Preference among preferences. J Philos 71(13): 377–391. https://doi.org/10.2307/2025160
27. Jiang, J., Hassan Awadallah, A., Jones, R., Ozertem, U., Zitouni, I., Gurunath Kulkarni, R., & Khan, O. Z. (2015, May). Automatic online
evaluation of intelligent assistants. In Proceedings of the 24th International Conference on World Wide Web (pp. 506-516). International
World Wide Web Conferences Steering Committee.
28. Jumpshot (2018). State of the Amazon Era: Data Report Q1 2018. Retrieved from https://go.jumpshot.com/q1-data-report.html
29. Knote, R., Janson, A., Eigenbrod, L., & Söllner, M. (2018). The what and how of smart personal assistants: Principles and application
domains for IS research.
30. Koksal, I. (2018, December 11). How Alexa Is Changing The Future Of Advertising. Forbes. Retrieved June 7, 2019, from
https://www.forbes.com/sites/ilkerkoksal/2018/12/11/how-alexa-is-changing-the-future-of-advertising/#693c38221d4d
9. 31. Kumar, V., Dixit, A., Javalgi, R. R. G., & Dass, M. (2016). Research framework, strategies, and applications of intelligent agent
technologies (IATs) in marketing. Journal of the Academy of Marketing Science, 44(1), 24-45.
32. Kwon, O. B. (2003). “I know what you need to buy”: context-aware multimedia-based recommendation system. Expert systems with
applications, 25(3), 387-400.
33. Labecki, A., Klaus, P., & Zaichkowsky, J. L. (2018). How bots have taken over brand choice decisions. In Proceedings of the Future
Technologies Conference (pp. 976-989). Springer, Cham.
34. Lee, S., & Choi, J. (2017). Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and
reciprocity. International Journal of Human-Computer Studies, 103, 95-105.
35. Li, S. S., & Karahanna, E. (2015). Online recommendation systems in a B2C E-commerce context: a review and future directions. Journal of
the Association for Information Systems, 16(2), 72.
36. Mandelli, A. (2018). Intelligenza artificiale e marketing: Agenti invisibili, esperienza, valore e business. EGEA spa.
37. Milhorat, P., Schlogl, S., Chollet, G., Boudy, J., Esposito, A., & Pelosi, G. (2014). Building the Next Generation of Personal Digital
Assistants. Paper presented at the Advanced Technologies for Signal and Image Processing (ATSIP), 2014 1st International Conference on,
Sousse, Tunisia.
38. Moriuchi, E. (2019). Okay, Google!: An empirical study on voice assistants on consumer engagement and loyalty. Psychology & Marketing,
36(5), 489-501.
39. Neff, J. (2016, November 3). Amazon’s private labels already dominate battery and speaker sales online . AdAge. Retrieved July 3, 2019,
from https://adage.com/article/digital/amazon-private-label-dominates-batteries-speakers/306602
40. Newman (2018). The Future of Voice and the Implications for News. Report by Reuters Institute and University and Oxford, UK.
41. Olshavsky, R. W., & Granbois, D. H. (1979). Consumer decision making—fact or fiction?. Journal of consumer research, 6(2), 93-100.
42. Pradeep, A.K., Appel, A., & Sthanunathan, S. (2018). AI for Marketing and Product Innovation: Powerful New Tools for Predicting Trends,
Connecting with Customers, and Closing Sales. Wiley.
43. Purington, A., Taft, J. G., Sannon, S., Bazarova, N. N., & Taylor, S. H. (2017). Alexa is my new BFF: social roles, user satisfaction, and
personification of the amazon echo. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing
Systems (pp. 2853-2859). ACM.
44. Quelch, J. A., & Harding, D. (1996). Brands versus Private labels-Fighting to win”.
45. Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., ... & Jennings, N. R. (2019). Machine behaviour.
Nature, 568(7753), 477.
46. Rijsdijk, S. A., & Hultink, E. J. (2009). How today’s consumers perceive tomorrow’s smart products. Journal of Product Innovation
Management, 26(1), 24–42.
47. Russell, S. J., & Norvig, P. (2010). Artificial Intelligence-A Modern Approach (3rd internat. edn.).
48. Sacks, H., & Schegloff, E. A. (1979). Two preferences in the organization of reference to persons in conversation and their interaction.
Everyday language: Studies in ethnomethodology, 15-21.
49. Sarikaya, R. (2017). The technology behind personal digital assistants. IEEE Signal Process. Mag.34, 67–81.
50. Sarikaya, R. (2018, December 7). The Role of Context in Redefining Human-Computer Interaction. Alexa Blogs. Retrieved July 10, 2019,
from https://developer.amazon.com/blogs/alexa/post
51. Schweitzer, F., Belk, R., Jordan, W., & Ortner, M. (2019). Servant, friend or master? The relationships users build with voice-controlled
smart devices. Journal of Marketing Management, 1-23.
52. Sciuto, A., Saini, A., Forlizzi, J., & Hong, J. I. (2018). Hey Alexa, What's Up?: A mixed-methods studies of in-home conversational agent
usage. In Proceedings of the 2018 Designing Interactive Systems Conference (pp. 857-868). ACM.
10. 53. Shen, A. (2014). Recommendations as personalized marketing: insights from customer experiences. Journal of Services Marketing, 28(5),
414-427.
54. Simms, K. (2019, May 15) How Voice Assistants Could Change the Way We Shop. Harvard Business Review. Online version. Retrieved
June 19, 2019, from https://hbr.org/2019/05/how-voice-assistants-could-change-the-way-we-shop
55. Snehota, I., & Hakansson, H. (Eds.). (1995). Developing relationships in business networks. London: Routledge.
56. Sterne, J. (2017). Artificial intelligence for marketing: practical applications. John Wiley & Sons.
57. The Wall Street Journal (2018, February 27). Amazon’s Alexa Leaves Consumer Brands Speechless [video]. Retrieved from
https://www.wsj.com/video/amazons-alexa-leaves-consumer-brands-speechless
58. Voosen, P. (2017). The AI detectives. Science (New York, NY), 357(6346), 22.