DIGITAL
MARKETING &
TECHNOLOGY
Articles Summary
2023-2024
,Table of Contents
Understanding Customers' Intention to Seek for a Better Deal in Electronic Marketplace: The Role of
Tolerance for Sacrifice Gap – Jiang (2003) ......................................................................................................... 2
Hughes, C., Swaminathan, V., & Brooks, G. (2019). Driving brand engagement through online social
influencers: An empirical investigation of sponsored blogging campaigns. ....................................................... 6
Ki, C. W. C., Cuevas, L. M., Chong, S. M., & Lim, H. (2020). Influencer marketing: Social media influencers as
human brands attaching to followers and yielding positive marketing results by fulfilling needs. Journal of
Retailing and Consumer Services, 55, 102133. .................................................................................................. 9
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the
Academy of Marketing science, 48(1), 79-95. ................................................................................................. 12
Yim, M. Y. C., Chu, S. C., & Sauer, P. L. (2017). Is augmented reality technology an effective tool for e-
commerce? An interactivity and vividness perspective. Journal of interactive marketing, 39(1), 89-103. ....... 14
Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder:
exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the
Academy of Marketing Science, 45, 884-905. ................................................................................................. 16
Rauschnabel, P. A., Babin, B. J., tom Dieck, M. C., Krey, N., & Jung, T. (2022). What is augmented reality
marketing? Its definition, complexity, and future. Journal of business research, 142, 1140-1150. .................. 19
Motyka, S., Grewal, D., Aguirre, E., Mahr, D., De Ruyter, K., & Wetzels, M. (2018). The emotional review–
reward effect: how do reviews increase impulsivity?. Journal of the Academy of Marketing Science, 46, 1032-
1051. .............................................................................................................................................................. 22
Purnawirawan, N., Eisend, M., De Pelsmacker, P., & Dens, N. (2015). A meta-analytic investigation of the role
of valence in online reviews. Journal of Interactive Marketing, 31(1), 17-27. ................................................. 27
Barcelos, R. H., Dantas, D. C., & Sénécal, S. (2018). Watch your tone: How a brand's tone of voice on social
media influences consumer responses. Journal of Interactive Marketing, 41(1), 60-80. ................................. 31
Javornik, A., Filieri, R., & Gumann, R. (2020). “Don't forget that others are watching, too!” The effect of
conversational human voice and reply length on observers’ perceptions of complaint handling in social
media. Journal of Interactive Marketing, 50(1), 100-119. ............................................................................... 36
Liebrecht, C., Tsaousi, C., & van Hooijdonk, C. (2021). Linguistic elements of conversational human voice in
online brand communication: Manipulations and perceptions. Journal of Business Research, 132, 124-135. . 40
Ho, S. Y., Bodoff, D., & Tam, K. Y. (2011). Timing of adaptive web personalization and its effects on online
consumer behavior. Information systems research, 22(3), 660-679. ............................................................... 44
Kalaignanam, K., Kushwaha, T., & Rajavi, K. (2018). How does web personalization create value for online
retailers? Lower cash flow volatility or enhanced cash flows. Journal of Retailing, 94(3), 265-279. ................ 50
1
, Understanding Customers' Intention to Seek for a Better Deal in Electronic Marketplace:
The Role of Tolerance for Sacrifice Gap – Jiang (2003)
Online consumers make frequent use of the Internet to search for product and price
information. In this study, an online model is proposed and empirically tested in investigating
the role of tolerance for sacrifice gap in understanding customers’ intention to seek for a better
deal. Data was collected through an online survey. Structural equation modeling was
employed to test hypotheses. The results show that tolerance for sacrifice gap was not only a
strong predictor of intention for continuing search but also itself being related to consumer
product knowledge, perceived control, and consumer product involvement. Direct
relationship between another construct “perceived reduction in sacrifice gap” and “intention
to seek for a better deal” was also found. Theoretical and managerial implications are
discussed.
This study focuses on the investigation of intention to seek for better deals by developing a
model designed to explore two predictors of such intentions. I argue that “tolerance for
sacrifice gap” and “perceived reduction in sacrifice gap” may be the two most important
functional elements leading to look for better deals via the Internet.
Sacrifice gap, differences between a consumer’s true desires and offerings in the marketplace
(Gilmore and Pine, 1997), is an indicator of the accuracy of the choice. The tolerance for
sacrifice gap and perceived reduction in sacrifice gap will represent the extent of maximizing
the accuracy of choice, therefore to motivate the seeking behavior. The proposed model,
starting from this accuracy-effort information processing perspective, organizes the individual
determinants of tolerance for sacrifice gap (motivation to attain accuracy) into two categories
of perceived ability to attain accuracy (consumer product knowledge, Internet previous
purchase experience, and perceived control), and efforts that consumers are willing to put in
attaining the accuracy (consumer product involvement). The latter relates to reduce the risk
of making an inaccurate choice. The risk is higher when price places an important role in the
transaction. The conceptual model can be viewed below. An explanation of the variables
follows below.
2
, Intention to seek for a better deal refers to the inclination of consumers to continue looking
for a “better deal” after they have found a product that fulfills their desires reasonably well. A
better deal may consist of a lower price for a product that has exactly the same desired
attributes, attribute amounts and quality levels as the one tentatively chosen.
There are likely to be individual differences among consumers in their tolerance for a given
magnitude of disparity between what is desired and what is offered by a product; i.e.,
differences in tolerance for a sacrifice gap of a particular size. It is known, for example, that
some consumers tend to be satisficers; that is, willing to accept a product that is considered
satisfactory but fails to fully meet ideal desires.
In electronic marketplace, perceived reduction in sacrifice gap is the degree to which a
consumer expects prior to actual purchase that his/her true desires can be fulfilled by a
product (service) found in the current web site.
The usefulness of information for purchasing products, and hence the tolerance for sacrifice
gap, may be a function of consumers’ perceptions of their self-knowledge (i.e., subjective
knowledge) about the product category. For instance, consumers who believe they are
knowledgeable about the product category may envision greater value content in the available
information in terms of enabling them to find a product that more closely matches their
desires.
The level of expertise of users on the Internet (Internet Use) is expected to impact their
tolerance for sacrifice gap in online shopping context. Heavy users of the Internet are more
likely to be knowledgeable about and used to online shopping features and hence will be more
reluctant to compromise for a less matched product. Put differently, more Internet-shopping
experienced consumers are less tolerant for sacrifice gap, therefore more inclined to continue
looking for a better deal even after they have found a product fitting their desires quite well.
In the proposed model for explaining “tolerance for sacrifice gap,” the concept of perceived
control is useful where the achievement of behavioral goals is contingent on external and
internal resources, for example, feeling in charge in the shopping process by interacting with
computer. For Internet shoppers, perceived control is the perception of ease or difficulty in
finding a good match, which is to reduce sacrifice gap.
Consumer search behavior has been found related to the involvement level (e.g., Kujala and
Johnson, 1993), because consumers with high involvement may exert more search efforts than
consumers with low involvement due to the differences in perceived benefits from search.
The results of the study can be found below:
3
MARKETING &
TECHNOLOGY
Articles Summary
2023-2024
,Table of Contents
Understanding Customers' Intention to Seek for a Better Deal in Electronic Marketplace: The Role of
Tolerance for Sacrifice Gap – Jiang (2003) ......................................................................................................... 2
Hughes, C., Swaminathan, V., & Brooks, G. (2019). Driving brand engagement through online social
influencers: An empirical investigation of sponsored blogging campaigns. ....................................................... 6
Ki, C. W. C., Cuevas, L. M., Chong, S. M., & Lim, H. (2020). Influencer marketing: Social media influencers as
human brands attaching to followers and yielding positive marketing results by fulfilling needs. Journal of
Retailing and Consumer Services, 55, 102133. .................................................................................................. 9
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the
Academy of Marketing science, 48(1), 79-95. ................................................................................................. 12
Yim, M. Y. C., Chu, S. C., & Sauer, P. L. (2017). Is augmented reality technology an effective tool for e-
commerce? An interactivity and vividness perspective. Journal of interactive marketing, 39(1), 89-103. ....... 14
Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder:
exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the
Academy of Marketing Science, 45, 884-905. ................................................................................................. 16
Rauschnabel, P. A., Babin, B. J., tom Dieck, M. C., Krey, N., & Jung, T. (2022). What is augmented reality
marketing? Its definition, complexity, and future. Journal of business research, 142, 1140-1150. .................. 19
Motyka, S., Grewal, D., Aguirre, E., Mahr, D., De Ruyter, K., & Wetzels, M. (2018). The emotional review–
reward effect: how do reviews increase impulsivity?. Journal of the Academy of Marketing Science, 46, 1032-
1051. .............................................................................................................................................................. 22
Purnawirawan, N., Eisend, M., De Pelsmacker, P., & Dens, N. (2015). A meta-analytic investigation of the role
of valence in online reviews. Journal of Interactive Marketing, 31(1), 17-27. ................................................. 27
Barcelos, R. H., Dantas, D. C., & Sénécal, S. (2018). Watch your tone: How a brand's tone of voice on social
media influences consumer responses. Journal of Interactive Marketing, 41(1), 60-80. ................................. 31
Javornik, A., Filieri, R., & Gumann, R. (2020). “Don't forget that others are watching, too!” The effect of
conversational human voice and reply length on observers’ perceptions of complaint handling in social
media. Journal of Interactive Marketing, 50(1), 100-119. ............................................................................... 36
Liebrecht, C., Tsaousi, C., & van Hooijdonk, C. (2021). Linguistic elements of conversational human voice in
online brand communication: Manipulations and perceptions. Journal of Business Research, 132, 124-135. . 40
Ho, S. Y., Bodoff, D., & Tam, K. Y. (2011). Timing of adaptive web personalization and its effects on online
consumer behavior. Information systems research, 22(3), 660-679. ............................................................... 44
Kalaignanam, K., Kushwaha, T., & Rajavi, K. (2018). How does web personalization create value for online
retailers? Lower cash flow volatility or enhanced cash flows. Journal of Retailing, 94(3), 265-279. ................ 50
1
, Understanding Customers' Intention to Seek for a Better Deal in Electronic Marketplace:
The Role of Tolerance for Sacrifice Gap – Jiang (2003)
Online consumers make frequent use of the Internet to search for product and price
information. In this study, an online model is proposed and empirically tested in investigating
the role of tolerance for sacrifice gap in understanding customers’ intention to seek for a better
deal. Data was collected through an online survey. Structural equation modeling was
employed to test hypotheses. The results show that tolerance for sacrifice gap was not only a
strong predictor of intention for continuing search but also itself being related to consumer
product knowledge, perceived control, and consumer product involvement. Direct
relationship between another construct “perceived reduction in sacrifice gap” and “intention
to seek for a better deal” was also found. Theoretical and managerial implications are
discussed.
This study focuses on the investigation of intention to seek for better deals by developing a
model designed to explore two predictors of such intentions. I argue that “tolerance for
sacrifice gap” and “perceived reduction in sacrifice gap” may be the two most important
functional elements leading to look for better deals via the Internet.
Sacrifice gap, differences between a consumer’s true desires and offerings in the marketplace
(Gilmore and Pine, 1997), is an indicator of the accuracy of the choice. The tolerance for
sacrifice gap and perceived reduction in sacrifice gap will represent the extent of maximizing
the accuracy of choice, therefore to motivate the seeking behavior. The proposed model,
starting from this accuracy-effort information processing perspective, organizes the individual
determinants of tolerance for sacrifice gap (motivation to attain accuracy) into two categories
of perceived ability to attain accuracy (consumer product knowledge, Internet previous
purchase experience, and perceived control), and efforts that consumers are willing to put in
attaining the accuracy (consumer product involvement). The latter relates to reduce the risk
of making an inaccurate choice. The risk is higher when price places an important role in the
transaction. The conceptual model can be viewed below. An explanation of the variables
follows below.
2
, Intention to seek for a better deal refers to the inclination of consumers to continue looking
for a “better deal” after they have found a product that fulfills their desires reasonably well. A
better deal may consist of a lower price for a product that has exactly the same desired
attributes, attribute amounts and quality levels as the one tentatively chosen.
There are likely to be individual differences among consumers in their tolerance for a given
magnitude of disparity between what is desired and what is offered by a product; i.e.,
differences in tolerance for a sacrifice gap of a particular size. It is known, for example, that
some consumers tend to be satisficers; that is, willing to accept a product that is considered
satisfactory but fails to fully meet ideal desires.
In electronic marketplace, perceived reduction in sacrifice gap is the degree to which a
consumer expects prior to actual purchase that his/her true desires can be fulfilled by a
product (service) found in the current web site.
The usefulness of information for purchasing products, and hence the tolerance for sacrifice
gap, may be a function of consumers’ perceptions of their self-knowledge (i.e., subjective
knowledge) about the product category. For instance, consumers who believe they are
knowledgeable about the product category may envision greater value content in the available
information in terms of enabling them to find a product that more closely matches their
desires.
The level of expertise of users on the Internet (Internet Use) is expected to impact their
tolerance for sacrifice gap in online shopping context. Heavy users of the Internet are more
likely to be knowledgeable about and used to online shopping features and hence will be more
reluctant to compromise for a less matched product. Put differently, more Internet-shopping
experienced consumers are less tolerant for sacrifice gap, therefore more inclined to continue
looking for a better deal even after they have found a product fitting their desires quite well.
In the proposed model for explaining “tolerance for sacrifice gap,” the concept of perceived
control is useful where the achievement of behavioral goals is contingent on external and
internal resources, for example, feeling in charge in the shopping process by interacting with
computer. For Internet shoppers, perceived control is the perception of ease or difficulty in
finding a good match, which is to reduce sacrifice gap.
Consumer search behavior has been found related to the involvement level (e.g., Kujala and
Johnson, 1993), because consumers with high involvement may exert more search efforts than
consumers with low involvement due to the differences in perceived benefits from search.
The results of the study can be found below:
3
