The Effect of Social Presence and Chatbot Errors on Trust

Round 1

Reviewer 1 Report

This research explored the AI potential of chatbots for creating positive change to support customers with a focus on the customer and psychological responses.  

The manuscript is well structured and interesting. The title is accurate and concise. In the entire manuscript, authors use standard technical and scientific terminology. After the Introduction, the authors explained the proposed method, as well as experimental results. The experiments and results were conducted according to the scientifically correct approach.

I felt the paper is very lengthy and Control Group's consideration in experiments would have added more impact on the results.   

Change:

I suggest making a separate section for the Literature review.

Add the manuscript's contribution to the introduction section. 

Line number 492: Table 8 is missing, is it referring to Table 4?. 

Manuscript refers to a few old research,  can be replaced by new ones.

Conclusion – The section number needs to be corrected.

Author Response

Response to Reviewer 1 Comments

We would also like to thank the reviewers for their productive comments and for sharing their views.

Point 1: I suggest making a separate section for the Literature review.

Response 1: Line 139

Literature review

      2.1. Literature view on anthropomorphism and social presence 

Point 2: Add the manuscript's contribution to the introduction section. 

Response 2:

Line 139 :

The contribution of the paper to the field of knowledge is important. Firstly ,the paper will  fill a gap in the marketing  research, where little attention has been paid to impact of the interconnection between customers and organizations,thus facing a new competitive reality,based on sustainable marketing strategies. Secondly ,the model developed and tested in this paper is, again, an important contribution for the study of the correlation between factors as error and gender,or the extent to which Social Presence and Perceived Competence mediate the relationships between anthropomorphic design cues and trust, through the deployement of competitive chatbots, taiylored for gaining satisfaction and loyalty among customers and better assist the management of the companies, for adopting responsible business operations

Point 3: Line number 492: Table 8 is missing, is it referring to Table 4?. 

Response 3: Also, Table 4  shows that all standardized factor

Table 4. Measurement models: items, re and reliability model fitting.

Point 4: Manuscript refers to a few old research,  can be replaced by new ones.

Response 4: We replace the old research with new ones

DeAngeli, A.; Johnson, G.I..; Coventry, L., The Unfriendly User: Exploring Social Reactions to Chatterbots. In: Proceedings of The International Conference on Affective Human Factors Design, 2001, 467–474.

With

Brandtzaeg, P. B. ; Følstad, A.,Why people use chatbots. In International Conference on Internet Science ,Springer, Cham,2017, 377-392.

30.Moon, Y., Intimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers, Journal of Consumer Research, 2000, 26 (March), 323–339.

With

Taddei, S.;Contena, B.,Privacy, trust and control: Which relationships with online self-disclosure?. Computers in Human Behavior, 2013, 29(3), 821-682

31.Nass, C.; Moon, Y., Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56(1), 2000, 81-103.

 With

Brave, S.; Nass, C.; Hutchinson, K. ,Computers that care: investigating the effects of orientation of emotion exhibited by an embodied computer agent. International journal of human-computer studies, 2005,62(2), 161-178.

35.Durlach, N.; Slater, M.,Presence in Shared Virtual Environments and Virtual Togetherness. Presence: Teleoperators and Virtual Environments, 2000, 9(2), 214-217.

With

Kilteni, K., Groten, R., ; Slater, M. , The sense of embodiment in virtual reality. Presence: Teleoperators and Virtual Environments, 2012,21(4), 373-387.

37.Nowak, K.L; Biocca, F.,The Effect of the Agency and Anthropomorphism on Users Sense of Telepresence, Copresence, and Social Presence in Virtual Environments. Presence: Teleoperators and Virtual Environments, 2003, 12(5), 481-494.

With

Bente, G.; Rüggenberg, S.; Krämer, N. C.; Eschenburg, F., Avatar-mediated networking: Increasing social presence and interpersonal trust in net-based collaborations. Human communication research, 2008, 34(2), 287-318.

43.Corritore, C. L.; Kracher, B.; Wiedenbeck, S., On-line trust: Concepts, evolving themes, a model. International Journal of Human-Computer Studies, 2003, 58(6), 737-758.

With

Corritore, C. L.;Wiedenbeck, S.; Kracher, B.; Marble, R. P. ,Online trust and health information websites. International Journal of Technology and Human Interaction (IJTHI), 2012,8(4), 92-115.

52.Gefen, D.; Straub, D. W., Managing User Trust in B2C e-Services. E-Service Journal, 2003, 2(2), 7-24.

With

King, W. R.;He, J,A meta-analysis of the technology acceptance model. Information & management, 2006,43(6), 740-755.

53.McKnight, D. H.; Choudhury, V.; Kacmar, C., Developing and Validating Trust Measures for e -Commerce: An Integrative Typology. Information Systems Research, 2002, 13(3), 334-359.

With

McKnight, D, H.: Chervany, N.L, Reflections on an initial trust-building model.Handbook of trust research, 2006,29

Chaudhuri, A.; Holbrook, M.B., The Chain of Effects from Brand Trust and Brand Affect to Brand Performance: The Role of Brand Loyalty, Journal of Marketing, 2001, 65 (April), 81-93.

With                      

Carroll, B. A.;Ahuvia, A. C. ,Some antecedents and outcomes of brand love. Marketing letters, 2006,17(2), 79-89.

64.Zimet, G. D.; Dahlem, N. W.; Zimet, S. G.; Farley, G. K., Multidimensional Survey of Perceived Social Support. Journal of Personality Assessment, 1988, 52(1), 30-41.

With

Bruwer, B.; Emsley, R.;Kidd, M.; Lochner, C.; Seedat, S.,Psychometric properties of the Multidimensional Scale of Perceived Social Support in youth. Comprehensive psychiatry, 2008,49(2), 195-201.

Hu, L.T; Bentler, P.M., Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives, Structural Equation Modeling, 1999, 6 (1), 1-55.

With

MacKinnon, D., Introduction to statistical mediation analysis. Routledge,2012.

Point 5: Conclusion – The section number needs to be corrected.

Response 5: line 752

Conclusions

The recent developments in the fields of Artificial Intelligence, Machine Learning, and Natural Language Processing are clearly blurring the line between human and non-human. We suggest that despite the complexity behind the customers’ psychological and behavioral answers to virtual assistants, blurring this line even further could compensate for the lack of human contact in the online environments, while also increasing the customers’ willingness to trust the technology and engage in positive consumer responses.

Reviewer 2 Report

This study explores the effect of error and gender in chatbots, by examining the reactions of 240 US virtual workers to four different conditions (combining gender and error) when using their website developed for the purposes of this experiment.

The manuscript is well-written and well-structured, while it provides a detailed and up-to-date state of the art on the topic.

The authors use diagrams and website screenshots in order to give the reader a more elaborate picture on how their experiment unfolded, which makes the manuscript both reader friendly and understandable.

The authors have discussed their findings and provided a concise presentation of their main findings and implications, as well as the limitations of their study.

Some minor comments:

Avoid bullet points unless absolutely necessary. Be cautious as to include figure captions in the same page with the figure. Leave one blank line after bullets/numbering (e.g. l.243, l.249, l.319) and between subsections (e.g. l.370, l.384, l.747). Conclusions section is numbered “1”. In the authors contributions section it is stated that all authors contributed equally. This is quite difficult for so many co-authors. Maybe consider a more detailed breaking down of the contributing roles, according to the journal’s requirements. Remove gray shadow from Figure 6. Either color the whole box, or leave it blank.

Overall, it is a very well-written manuscript that deals with an interesting and “hot” topic. However, I do not see the link with the Sustainability journal in the article’s present form. In order for the authors to convince that their work is suitable for publication in Sustainability, they should first show their connection of their work to previously published work in the journal, and then elaborate on how their work will contribute to the aims and scope of the journal (https://www.mdpi.com/journal/sustainability/about ), focusing mostly on the “Socio-economic, scientific and integrated approaches to sustainable development” section. The links should be part of the abstract, introduction, and conclusions.

Author Response

Response to Reviewer 2 Comments

We would also like to thank the reviewers for their productive comments and for sharing their views.

Point 1: Avoid bullet points unless absolutely necessary.

Response 1:

Line 70. Change the bullets points in text

According to Deloitte’s report [4] “Chatbots moving beyond the hype”, the main demand-side factors include the following:

Increase in the pressure on call centers. The high turnovers rates, coupled with the need to decrease the operating costs and the necessity for constant personnel training are putting a lot of pressure on call centers to provide a better customer service.

Push for self-service. Customers want to have their problems handled straight away with a minimal effort involved, without waiting for an agent to fix them.

Shift to mobile messaging applications. According to Statista 2019 [17-18], 2.48 billion people worldwide will be using mobile messaging applications (e.g. Facebook Messenger, WeChat, Viber, Kik) by 2021. According to Brandtzaeg & Følstad [19], this fundamental change in online consumer behavior has led major companies to take on chatbots, as they are considered an efficient way to reach customers. In addition, Gartner [20-21] predicted that by 2021, more than 50% of companies will invest more in chatbots development than in traditional mobile apps development.

On the supply side, the renewed interest in chatbots is spurred by substantial advances in major technologies and computing power. Thus, chatbots succeeded in gaining traction from tech giants (Google, Facebook, Amazon, Microsoft) as early adopters who developed their own chatbots [2], [19]. These digital giants facilitated a faster acceptance of conversational bots by leveraging on people’s preference for chatting, as well as, on their acceptance of text communication as a social way of interacting. Also, a study performed by Grand View Research [22] forecasted that the chatbot market will reach $1.23 billion globally by 2025, with a compound annual growth rate of 24.3%.

Technological advancements. The recent developments in Artificial Intelligence (AI), Natural Language Processing (NLP) and Machine Learning improved the natural language interpretation and the prediction capabilities of chatbots [19]. According to a report developed by Deloite India [1], chatbots can be perceived now as intelligent agents, capable of learning from every interaction and understanding queries like human.

Maturing of chatbot platforms. Due to the increased popularity of chatbot technology, the platforms are developing, allowing business users to easily build, train and manage chatbots by themselves [4].

Development of e-commerce. In 2018, the global e-commerce retail sales amounted USD 2.8 billion, representing an increase of 112% compared to 2014 (USD 1.3 billion) from Statista 2019 [20-21]. Holtgraves et al. [2] posited that many e-service providers are willing to incorporate intelligent bots that use natural language processing in order to boost profits and increase the customer loyalty. Also, according to a report developed by Global Market Insights [23], since chatbots can provide personalized shopping experience and eventually improve customer satisfaction, this led to a faster acceptance of chatbots in the e-commerce sector.

Line 110: Change the buleets

Chatbots in customer service should be perceived as a combination of three elements [25]:

Interface between human and chatbots, which is increasingly using voice interaction by leveraging Natural Language Processing and Artificial Intelligence; Intelligence, as chatbots are more active in areas of broad expertise due to the advancements in machine learning and other techniques which allow them to understand and solve requests, as well as, to learn from each interaction; Integration, as chatbots can access a wide range of information from various sources due to the integration between systems and platforms, such as workforce management systems [26].

Point 2: Be cautious as to include figure captions in the same page with the figure.

 Response 2:

Line 212. We rearrange the figures on the same page

Line 690

 Point 3: Leave one blank line after bullets/numbering (e.g. l.243, l.249, l.319) and between subsections (e.g. l.370, l.384, l.747). Conclusions section is numbered “1”.

Response 3: line 752 correct and change the section number

Conclusions

The recent developments in the fields of Artificial Intelligence, Machine Learning, and Natural Language Processing are clearly blurring the line between human and non-human. We suggest that despite the complexity behind the customers’ psychological and behavioral answers to virtual assistants, blurring this line even further could compensate for the lack of human contact in the online environments, while also increasing the customers’ willingness to trust the technology and engage in positive consumer responses.

Point 4: In the authors contributions section it is stated that all authors contributed equally. This is quite difficult for so many co-authors. Maybe consider a more detailed breaking down of the contributing roles, according to the journal’s requirements.

Response 4: We delete All authors contributed equally and replace with

Line 775. Author Contributions: Diana Cezara Toader conceived and designed the conceptualization; Cezar Toader and Mara Macelaru software; Gratiela Boca, Rita Toader, Diana Ighian and Adrian T. Radulescu performed the research and the analysis. All authors contributed in discussing the research, writing parts of the paper and commenting on draft versions and finalized the paper.

Point 5: Remove gray shadow from Figure 6. Either color the whole box, or leave it blank.

Response 5: Line 690.

The practical implications are presented in Figure 6.

Figure 6. Practical implications of our study. Source: By authors.

Point6: Overall, it is a very well-written manuscript that deals with an interesting and “hot” topic. However, I do not see the link with the Sustainability journal in the article’s present form.

Response 6: We modify the article form after Sustainability journal,

Point 7: In order for the authors to convince that their work is suitable for publication in Sustainability, they should first show their connection of their work to previously published work in the journal, and then elaborate on how their work will contribute to the aims and scope of the journal (https://www.mdpi.com/journal/sustainability/about), focusing mostly on the “Socio-economic, scientific and integrated approaches to sustainable development” section. The links should be part of the abstract, introduction, and conclusions.

Response 7:

Line 819:

Sustainability is a complex phenomenon, which manifests itself in a direction of healthy development of organizations, integrating social, economic and environmental problems into their development strategies [72-75]. Artificial intelligence, along with other new digital technologies, fundamentally redefines the business environment, supporting sustainable development by creating value for customers, stakeholders and for the environment, The rapid development in the last years of Artificial Intelligence, of digital technologies has led to an increased pro-activity in adopting new strategies related to the relations between consumers and organizations, in a sustainable way.

Rădulescu, C.; Toader, R.; Boca, G.; Abrudan, M.; Anghel, C.; Toader, D.C. Sustainable Development in Maramures County. Sustainability 2015, 7, 7622-7643. Rădulescu, C.M.; Ştefan, O.; Rădulescu, G.M.; Rădulescu, A.T.; Rădulescu, M.V. Management of Stakeholders in Urban Regeneration Projects. Case Study: Baia-Mare, Transylvania. Sustainability 2016, 8, 238. Pop, I.L.; Borza, A.; Bozga, A.; Ighian, D.; Toader, R. Achieving Cultural Sustainability in Museums: A Step Toward Sustainable Development. Sustainability 2019, 11, 970. Boca, G.D.; Saraçlı, S. Environmental Education and Student’s Perception, for Sustainability. Sustainability 2019, 11, 1553.

Author Response File: Author Response.pdf

Reviewer 3 Report

At a high level, this submission has a set of comments, strange formatting, and text coloring that make it look like a draft in the middle of edits more than a submission. The request for review did not disclose the state of the paper, so I'm reviewing it to the best of my ability with these edits present. I expect a final version will resolve those comments and make the formatting regular.

I appreciate the contributions of this work in terms of the user study primarily showing the results of varying correctness and gender of a virtual assistant. In addition to the suggested future studies using higher quality digital avatars, it may make sense to recommend future studies also include age to see whether young or old digital assistants give better results. The finding that errors from the digital assistant give lower user results is obvious, but still valuable to quantify. The results around gender are less expected and therefore a good contribution.

I found the use of the English language in this submission generally good, though at times some of the phrasing was awkward. I tried to provide some help in my notes below. Since this draft has many clear signs of being an in-progress draft, the authors should clearly clean up their presentation to make it follow a uniform style and ideally improve the English in the process.

The introduction makes a few claims about the types of AI that are used in chat bots, but it certainly isn't complete, nor should it really be a complete survey. I recommend changing the phrasing to sound less like you're trying to survey the entire space of AI chat bot algorithms and instead rely on the survey papers you cite more clearly. Maybe directly calling them survey's would help.

On Page 13, in the paragraph from lines 426-430 the authors mention that a source exists for the photo they used and they used an online tool to generate the gender swapped version. I'd suggest that both of these sources should be referenced here.

I'm not sure Figure 6 is very useful in its current form. I suggest either revising it to make it more useful or removing it to to shorten the article.

Minor comments:

The line spacing on the first page appears to change for lines 37-41 Page 2, line 68 has a trailing '-' that probably shouldn't be there. Page 2, line 70, the comment suggests adding bullet points to make the list clear. I agree with that. Section 1.1, the line spacing is again different. Page 4, line 143, the comma is after the space instead of before. The line spacing varies throughout the rest of the submission, but it should be standardized before publication. At the top of page 8, there are a series of single-sentence paragraphs, which is a bit odd. I recommend grouping the sentences appropriately into paragraphs. Line 483 on page 15 "fewer techs savvy" should be "less tech savvy". In Figure 6, the phrase ending "a compelling error-free" seems to be missing the final object of the sentence. At the top of page 21, there's another series of single-sentence paragraphs that could be combined into better paragraphs.

Author Response

First of all we want to say Thank you very much, for your effort and attention to correct the draft and give us such good suggestions.

Minor comments:

The introduction makes a few claims about the types of AI that are used in chat bots, but it certainly isn't complete, nor should it really be a complete survey.

Point 1: I recommend changing the phrasing to sound less like you're trying to survey the entire space of AI chat bot algorithms and instead rely on the survey papers you cite more clearly. Maybe directly calling them survey's would help.

Response 1: Line 37-61

Chatbots are able to understand the user’s intent by deciphering verbal or written requests and responding with appropriate information. According to a report performed by Deloitte [1] and the investigations of human–robot interactions conducted by Holtgraves et al. [2], chatbots leverage Artificial Intelligence to process language, which enables them to perceive the intended meaning of human speech. Moreover, based on the studies performed by Radziwill and Benton [3] and Deloitte Digital [4] chatbots were shown to be intelligent and autonomous agents, which learn from past interactions to improve responses over time. Also, Shawar and Atwell [5] surveyed several chatbot systems in various practical domains and posited that virtual assistants combine the architecture of a language model with computational algorithms in order to simulate a human conversation. Indeed, the complexity of chatbots' algorithms may vary, but the computer agents are usually programmed to answer to user requests with pre-scripted statements as stated by Brandtzaeg and Følstad [6]. However, Radziwill and Benton [3] and Abdul-Kader and Woods [7] focused their studies on the most advanced chatbots which leverage machine learning (i.e. Markov chains) in mimicking intelligent conversations in a similar way like humans

Point 2: The line spacing on the first page appears to change for lines 37-41 Page 2, line 68 has a trailing '-' that probably shouldn't be there.

Response 2:

The recent increase in the interest for chatbots is attributable to several factors, both on the demand and supply side.

Point 3:  Page 2, line 70, the comment suggests adding bullet points to make the list clear.

Response 3:  Line 75-127 (page 2)

The recent increase in the interest for chatbots is attributable to several factors, both on the demand and supply side. According to Deloitte’s report [4] “Chatbots moving beyond the hype”, the main demand-side factors include the following:

Increase in the pressure on call centers. The high turnovers rates, coupled with the need to decrease the operating costs and the necessity for constant personnel training are putting a lot of pressure on call centers to provide a better customer service. Push for self-service. Customers want to have their problems handled straight away with a minimal effort involved, without waiting for an agent to fix them. Shift to mobile messaging applications. According to Statista 2019 [17-18], 2.48 billion people worldwide will be using mobile messaging applications (e.g. Facebook Messenger, WeChat, Viber, Kik) by 2021. According to Brandtzaeg and Følstad [19], this fundamental change in online consumer behavior has led major companies to take on chatbots, as they are considered an efficient way to reach customers. In addition, Gartner [20-21] predicted that by 2021, more than 50% of companies will invest more in chatbots development than in traditional mobile apps development. On the supply side, the renewed interest in chatbots is spurred by substantial advances in major technologies and computing power. Thus, chatbots succeeded in gaining traction from tech giants (Google, Facebook, Amazon, Microsoft) as early adopters who developed their own chatbots [2], [19]. These digital giants facilitated a faster acceptance of conversational bots by leveraging on people’s preference for chatting, as well as, on their acceptance of text communication as a social way of interacting. Also, a study performed by Grand View Research [22] forecasted that the chatbot market will reach $1.23 billion globally by 2025, with a compound annual growth rate of 24.3%. Technological advancements. The recent developments in Artificial Intelligence (AI), Natural Language Processing (NLP) and Machine Learning improved the natural language interpretation and the prediction capabilities of chatbots [19]. According to a report developed by Deloite India [1], chatbots can be perceived now as intelligent agents, capable of learning from every interaction and understanding queries like human. Maturing of chatbot platforms. Due to the increased popularity of chatbot technology, the platforms are developing, allowing business users to easily build, train and manage chatbots by themselves [4]. Development of e-commerce. In 2018, the global e-commerce retail sales amounted USD 2.8 billion, representing an increase of 112% compared to 2014 (USD 1.3 billion) from Statista 2019 [20-21]. Holtgraves et al. [2] posited that many e-service providers are willing to incorporate intelligent bots that use natural language processing in order to boost profits and increase the customer loyalty. Also, according to a report developed by Global Market Insights [23], since chatbots can provide personalized shopping experience and eventually improve customer satisfaction, this led to a faster acceptance of chatbots in the e-commerce sector.

 Point 4:  Section 1.1, the line spacing is again different. I agree with that.

Response 4: We have made the changes

Point 5: Page 4, line 143, the comma is after the space instead of before.

Response 5:  Line 157 (143)

Among the top three benefits of implementing chatbots, they mentioned: enhanced employee productivity, improved ability to manage client queries by networking with other bots, as well as, provision of customers with a personalized and unique shopping experience and 24/7 access to information, therefore  companies across industries are willing to leverage the benefits of intelligent bots in order to streamline their activities, automate tasks, improve productivity, customer acquisition and retention, as well as, foster the engagement of both employees and clients. Also, Gartner [20] predicted that twenty-five percent of all firms will integrate the chatbot technology for their customer service by 2020.

Point 6: On Page 13, in the paragraph from lines 426-430 the authors mention that a source exists for the photo they used and they used an online tool to generate the gender swapped version. I'd suggest that both of these sources should be referenced here.

Response 6: Line 426-430 /479-484

In order to select equally attractive avatars for the male and female versions of the virtual 
assistants, we decided to use only one picture and apply the gender swap functionality in two photo editing software: FaceApp and Adobe Photoshop. In this way, we could ensure that both genders have the same face shape, skin tone, eye color, hair color, as well as, smile. Also, we selected the picture from an online database which provides free content to be used for personal or commercial purposes. Kratochvil [58].

 Figure 4. Manipulation of Gender. Source: Kratochvil [58]

 [71]  Kratochvil, P. (n.d.). Sport Customer Service Free Stock Photo - Public Domain Pictures. Retrieved from https://www.publicdomainpictures.net/en/view-image.php?image=295430&picture=sport-customer-service.

Point 7: At the top of page 8, there are a series of single-sentence paragraphs, which is a bit odd. I recommend grouping the sentences appropriately into paragraphs.

Response 7: Line 258

 3.1. Conceptual model and research hypotheses

We place our research in the context of digitalization of business models, as well as, the advancements of artificial intelligence, neuro-linguistic programming and machine learning in the area of human-computer interaction (HCI), with particular emphasis on interactions in the e-commerce sector. In doing so, we argue for paying greater attention to the determinants of trusting beliefs and positive consumer responses in a human-chatbot interaction. For the development of our conceptual model and the research hypotheses, we relied on two communications theories and one paradigm (Social Information Processing Theory, Media Equation Theory, CASA Paradigm), as well as, on previous research findings. By extending these theories to the context of consumer behavior coupled with the previous research findings, we propose that during an online commercial experience, consumers respond positively to social cues designed to portray a real customer service representative, as this will increase the sense of realism.

Figure 1 depicts our conceptual model. We focused on the direct relationships between anthropomorphic design cues and social presence, as well as, anthropomorphic design cues and perceived competence.

Point 8: Line 483 on page 15 "fewer techs savvy" should be "less tech savvy".

Response 8: Line 574

Therefore, we expect respondents who are less tech savvy and who give a high importance to human relations will feel less comfortable and more uncertain when interacting with virtual agents than those who are eager to embrace technological developments and communicate in a computer-mediated environment.

Point 9: I'm not sure Figure 6 is very useful in its current form. I suggest either revising it to make it more useful or removing it to to shorten the article. 2)  In Figure 6, the phrase ending "a compelling error-free" seems to be missing the final object of the sentence.

Response 9: We choose the first suggestion. We delete the Figure 6.

Point 10 At the top of page 21, there's another series of single-sentence paragraphs that could be combined into better paragraphs. 

Response 10: Line 980 (page 21)

5.2. Limitations and avenues for future research

Our research is subject to several limitations. First, the manipulation of the anthropomorphic design cues covered limited elements, such as: identity cue (name), visual cue (avatar), interactivity (delayed answers, typing icon). However, our current findings on the significant warmth perceptions produced by highly anthropomorphized agents should encourage further exploration of relevant social cues in an online retail context. Thus, we encourage researchers to design and deploy more advanced virtual assistants that leverage speech recognition, as well as, improved visual and identity cues (i.e. three-dimensional model). This might provide new insights into how people respond to virtual assistants mimicking emotions in real-time voice interactions and whether customers would apply social heuristics and engage in mental models from Human-to-Human interaction.

Second, we used a sample of respondents (N=240) recruited via Amazon's Mechanical Turk. Even though the sample served to increase the external validity and general reliability of the research findings, the study results cannot be generalized to all customer segments in the online environment. Thus, in order to cross-validate the results, future research might incorporate more heterogeneous samples for better customer segmentation. Third, the study empirically established that anthropomorphic female virtual assistants lead to positive consumer responses in the context of sportswear, a relatively low-risk product category. We encourage researchers to assess whether the effects of anthropomorphic design cues hold in other contexts, such as in the case of riskier products (i.e. mortgages) or hedonic products (i.e. luxury products). We believe that a deep understanding of the most relevant purchase situations in which anthropomorphized virtual assistants improve socialness perceptions, perceived competence and subsequently lead to trust and positive consumer responses would help companies wisely employ social cues in the online environment.

Lastly, the mediation paths for both social presence and perceived competence were not statistically significant in either of the empirical studies. Therefore, we encourage researchers to test other potential mediators or moderators of the relationship between Anthropomorphic Design Cues and Trust. An interesting construct would be the consumer search strategy in order to assess which strategy will facilitate the development of an optimal online experience marked by enjoyment, namely the flow experience.

Point 11. In addition to the suggested future studies using higher quality digital avatars, it may make sense to recommend future studies also include age to see whether young or old digital assistants give better results.

Response 11:  Line 1070-1072

Our research is subject to several limitations. First, the manipulation of the anthropomorphic design cues covered limited elements, such as: identity cue (name), visual cue (avatar), interactivity (delayed answers, typing icon). However, our current findings on the significant warmth perceptions produced by highly anthropomorphized agents should encourage further exploration of relevant social cues in an online retail context. Thus, we encourage researchers to design and deploy more advanced virtual assistants that leverage speech recognition, as well as, improved visual and identity cues (i.e. three-dimensional model). This might provide new insights into how people respond to virtual assistants mimicking emotions in real-time voice interactions and whether customers would apply social heuristics and engage in mental models from Human-to-Human interaction. Besides the previously mentioned social cues and the suggested technical improvements, marketing practitioners could also study the impact of the age of virtual assistants on the customers’ perceptions and assess whether the younger or the older ones would lead to more positive consumer responses.