Pacific B usiness R eview (International)

A Refereed Monthly International Journal of Management Indexed With Web of Science(ESCI)
ISSN: 0974-438X
Impact factor (SJIF):8.603
RNI No.:RAJENG/2016/70346
Postal Reg. No.: RJ/UD/29-136/2017-2019
Editorial Board

Prof. B. P. Sharma
(Editor in Chief)

Dr. Khushbu Agarwal
(Editor)

Dr. Asha Galundia
(Circulation Manager)

Editorial Team

A Refereed Monthly International Journal of Management

‘Smart Shopping’ with ‘Webrooming’: Consumer Shopping Behaviour in ‘New Normal’

Dr. Jasmine V.M

Asst. professor,

Dept. of Commerce

St. Joseph’s (Autonomous) College Devagari

Jasminevm83@gmail.com

 

Abstract

Smart consumers do thoughtful shopping practices by actively searching information relating to products/services with the intention of saving time, money and effort during their purchase process. The tremendous growth in information technology followed by lockdowns in Covid 19 accelerated the use of digital platforms in shopping and this has catapulted consumers’ power over the market and modified traditional smart shopping habits. At the same time, after the economic and social paralyse caused by the pandemic, people started to show their inherent urge to go outside for physical shopping. In this backdrop this research is an attempt to know and measure the influence of various factors that are contributing to the consumers’ online search for information and corporal visit at the shops for purchasing, known as webrooming behaviour. This research also shed light on the extent to which web roomers believe that webrooming behaviour in the ‘new-normal’ is impacting their perception about smart shopping. As per the research, webrooming experiences engender the feeling of savings in time, effort, money, utility and empower to do the right purchase. Again, the webrooming behaviour echoed a significant favourable impact on the smart shopping perceptions of consumers. Likewise, it was able to prove that webrooming behaviour in the new normal stimulates smart shopping perception. Above all, irrespective of the demographic variables under study, consumers’ smart shopping perception get influenced by their webrooming behaviour. Therefore, in this new normal scenario, retailers must develop efficient strategies that include updated websites and enticing store environments to combat the webrooming behaviour of smart customers.

Keywords: Webrooming, Smart Shopping, New Normal, Shopping Behaviour

Introduction

The phases of the shopping cycle that a goal-oriented consumer typically undergoes include, becoming motivated to shop, seeking out the best solution, evaluating various alternatives (such as categories, brands, and items), making a preliminary decision regarding the best category, brand, or item to buy, selecting the store or channel for purchase (e.g., catalogue, bricks-and-mortar store, Web site, or mobile site), navigating the "store," and reaching a decision(Shankar, 2011). The adoption of strategic approaches in accessing goods, services, and information results in an enhanced and smart shopping experience and that benefits the personal and social well-being of the customer (Zalega, T. 2017). As per the researches on efficiency theories of consumer shopping, smart shopping is the consumer’s efficient shopping behaviour followed by reduced cost and increased value (Ingene, 1984). The concept of smart shopping, as outlined by Atkins and Kim (2012), pertains to consumers actively aiming to reduce the consumption of time, money, or energy while maximizing the value gained from both pleasurable and functional aspects of their shopping experiences. Consumers consistently strive to enhance their shopping efficiency by minimizing the resources invested in shopping (such as time, money, and energy) and simultaneously maximizing the benefits gained, whether in terms of enjoyment or practical value. This practice significantly contributes to their overall smart shopping encounter, as elucidated by Tangen (2005).

The technological advancements generated online shopping channels for consumers to ride and it impacted tremendously on shopping behaviour (Shankar et al.2010). Furthermore, the ups and downs of the Covid-19 pandemic also brought discernible changes to traditional shopping behaviour. The lockdown, isolation, financial crisis and uncertainties caused by the pandemic disrupted human life and dramatically precipitated consumption (Mehta et al., 2020). However, this crisis situation has hastened the digital transition and it spurred the use of technology in shopping. Thus, we became more digitally empowered in making purchase decisions and learned to leverage our smart shopping using internet facilities. In this omnichannel era, to be smart, now consumers are using more than one channel interchangeably and even simultaneously to search and complete their purchase process (Flavián et al., 2020; Verhoef et al., 2015;Herhausenet al., 2015). Consumers categorized as maximizers show a heightened inclination towards emerging marketing channels, which offer an extensive array of market information to help them identify the most optimal choice available and enhance their shopping efficiency (Anitsal and Schumann2007; Downs1961; Ingene1984).Considering the pattern of multi-channel usage for shopping, consumers are classified as webroomers and showroomers. The web roomers search information online and make purchases offline, while the showroomers search information offline and do the purchase online. Academic literature shows that webrooming leads to more personal attribution than showrooming and intern it contributes to smart shopping (Flavián et al., 2020).

In the post-pandemic era, consumers are altering their routine shopping scripts and exerting cognitive effort to adopt new marketing channels for information searches or purchases (Kothari and Dawar, 2022).   During the lockdown, many customers shifted their shopping from offline to online on account of safety measures. But, people have an inherent need for movement and they merely wish to venture outside. We use shopping as an excuse to get out, and we plan shopping trips to state our want "to get outside and go somewhere." (Mokhtarian and Salomon, 2001). Furthermore, physical shopping offers chances for consumers to engage with products through all five senses and enables social interactions during the shopping process (Avery et al., 2012). Therefore, people were prepared to adapt to the " new normal" at the pandemic dusk and excitedly awaited the opening of our markets, public spaces, and workplaces. Moreover, taking into account the limitations of simple online or offline buying decisions, consumers started to analyse the purchase decision as two staged processes involving decision relating to selecting a product and completing the purchase (Boyle et al., 2009; Balasubramanian et al., 2005) Now, smart consumers are more interested in collecting product-related information from online channels and in purchasing from physical stores (Flavián et al., 2020). Webrooming in the new normal satisfies both the needs, i.e., the need to become smarter in shopping and the desire to travel outside. While physical storefronts provide real-time purchasing experiences with immediate possession of the goods and no delivery wait, the internet enables smart consumers to save time and effort when researching products (Heitz-Spahn, 2013; Dempski et al. 2019). Audrain-Pontevia et al., (2013) explained that the explosion of information facilitated by the internet empowers consumers to engage in thorough and immediate online product comparisons, encompassing products, deals, and prices, thereby transforming them into more informed smart shoppers. Simultaneously, the elevated product insights they attain from online sources enhance their ability to negotiate more efficiently with vendors during actual transactions (Jangetal., 2017). Considering these ramifications in shopping behaviour, retailers also started offering online as well as offline avenuesto cater to diverse customer requirements within the context of the 'new normal'. Therefore, it is crucial for retailers to have a comprehensive grasp of consumer shopping behaviour in the framework of ‘new normal’, in order to change their physical and virtual store formats in response to consumer movements, technological innovation, and disruptors. Despite there are research articles on webrooming behaviour focusing on psychographic variables, shopping motivations, spending elements, product-related variables, etc., a gap is figuring out how webrooming behaviour affects smart shopping perception in the context of new normal.

This research is an attempt to uncover the feeling of ‘smart shopping’ as an antecedent of webrooming behaviour, framed with the perceived feelings derived from webrooming experience, in post-Covid life. This paper also tries to determine the extent to which multi-channel users in the ‘new normal’ period have webrooming behaviour. In the post-pandemic or new normal situation, it is crucial to understand whether web roomers really have a feeling that their time, effort, and money are saved and perceive their purchases as the right purchase. Additionally, this research articulates a clear understanding of how far webrooming is generating a smart shopping perception among consumers. When these factors are well understood, retailers can deploy more retail innovations, boost shop productivity, and provide better customer experiences (Lemon and Verhoef, 2016).

Theoretical Frame work

Smart Shopping

As per traditional research, smart shopping is a feeling of pride, pleasure or satisfaction realised from paying less price than his or her reference price or expected price (Mano and Elliot,1997; Manzur et al., 2011; Burton et al., 1998). To enjoy the feeling of winner, smart shoppers are ready to spend their time and effort. Besides they are ready to search for promotion-linked information that leads to price savings (Burton et al., 1998). But modern studies explain that a customer is felt as smarter if he is able to save time/effort (convenience) and perceives that he has made the right purchase (product matching their requirements) (Bicen and Madhavaram, 2013; Atkins and Kim, 2012). The purchase process consists of two major steps, i.e., information gathering and real purchase. Theinformation search at websites and e-mail campaigns provides continuous exposure in terms of price deals and promotion. These real-time online comparisons make customers smarter in shopping (Audrain-Pontevia et al., 2013). Technology is a smart shopping supporter, both in information search and physical purchase. Again, it is noticed from the earlier studies that real-time spending feedback controls the shopping behaviour of high-budget and budget customers. Besides, Ali and Sonkusare (2014) examined the effect of smart shopping carts developed with Radio Frequency Identification works on customers’ smart shopping behaviour.

Webrooming

The definition of webrooming as expounded in the current study aligns with previous research that explores cross-channel shopping and several research works conducted on smart shopping (Neslin and Shankar, 2009; Huré et al., 2017; Verhoef et al.,2007; Van Baal and Dach, 2005).   Based on the review, it is noticed that webroomers are interested in making the best purchases and for that, they search for information intensively until they get the confidence to do the purchase (Flavián et al., 2016). Online searches of web roomers give knowledge empowerment to interact with retailers before finalising thepurchase (Walsh and Mitchell, 2010). Internet is noticed as the most preferred source of information for webroomers and they approach physical stores for real purchases (Albaetal., 1997; Dholakiaetal., 2005). Consumers consider different channels in their purchase decision and adopt the best combination that provides maximum benefit and minimum cost (Pauwels et al., 2011; Alba et al., 1997). Therefore, the final selection of any network depends upon the goals and motivation of consumers. Webrooming is one of the most preferred channel combinations as it uses the internet for search (which provides comprehensive information) and physical possession (which reduces the delivery time) (Singh and Swait, 2017; Peterson et al., 1997; Wollenburgetal.,2018; Heitz- Spahn, 2013). At the same time, in webrooming, especially in the case of durable goods, searching in online channels complements the offline purchase and it can never be treated as a substitute for physical purchase (Singh et al.2014).

Consumer Shopping behaviour in New Normal

            Kantar, a research firm, in their study titled "Market Dynamics During COVID- 19: Indian Consumer Sentiments Analysis" (2020), reports on the remarkable shift in attitudes, behaviours, and expectations of consumers in shopping. During Covid-19, scale up in the use of E-Commerce touch points for shopping happened and it led to digitalisation in buying. Lower spending both in shopping, doing planned purchases and learning to live with less were the other findings of Kantar report. It was anticipated that ‘digital technology platforms’ would function a strong role in terms of generatingawareness, reaching consumers,managing operations, and retention of consumers in post-Covid also. But, as per the studies conducted by different organisations, some of these shifts in behaviour won't last, while others may become permanent.  (Mehta et al., 2020).

Objective based hypothesis

Building upon prior research elucidating smart shopping perceptions and webrooming behavioural dimensions (Flavian et al., 2020), the author posited that webrooming behaviour within the context of the new normal gives rise to the formulation of perceptions related to intelligent shopping (including time and effort conservation, cost efficiency, making appropriate purchases, and attaining utilitarian value).  To achieve this, a three-dimensional structureof smart shoppingcontributed by Atkins and Kim (2012)is taken as the base. Web roomers’ perceptions of savings in time, effort, cost, ability to do the right purchase are measured along with their feeling about how far they were able to perceive webrooming in post-COVID-help them to improve hedonic/utilitarian value. The increased affordability of the internet (Fernándezetal.,2018) and the usage during the pandemic considerably changed web usage for information search. As a positive impact, consumers searching information on the web noticed as efficient in purchase (Gensler et al. 2017). Primarily, physical stores offer immediate product acquisition, thus allowing for time saved on deliveries (Aragoncillo and Orús, 2018; Wollenburg et al., 2018). Consequently, webrooming encounters are likely to exert a robust positive influence on consumers' perceptions of time & effort. Webroomers can efficiently seek information online, leading to decreased purchase uncertainty. This reduction in information asymmetry empowers webroomers to make purchases that align better with their specific needs (Flavian et al., 2016; Daugherty et al., 2008; Van der Veen and Van Ossenbruggen, 2015)

Objectives

  1. To identify and measure the influence of various factors contributing to the webrooming behaviour
  2. To analyse the influence of webrooming behaviour on the consumers’ smart-shopping feeling

 

Hypotheses

H1The consumers perceived feeling from webrooming experiences in the new normal context will have positive impact on webrooming behaviour

H2The webrooming experiences at new normal context will have significant positive influence on consumers’ smart-shopping feeling.

H3Demographic variables have impact on consumers' webrooming behaviour.

Methods and Measurement

Numerous studies conducted prior to Covid -19 is mentioning about the increasing trend of webroomingbehaviour among Asian customers (Nielsen, 2016). Considering this, current study is conducted in India, more specifically in Kerala. A total of 600 respondents were approached and 250 responses were found as matching with the webrooming behaviour. Respondents who have a practice of web searching for information and physical shopping habit were only considered. Respondents were pre-screened to ensure their webrooming behaviour.  Questionnaireprepared to collect primary data has been sent to respondents through various online platforms and the data collection periodcovers the month of November 2022.

The principles outlined by Atkins and Kim (2012) concerning smart- shopping were taken into account as factors influencing webrooming behaviour and for those variables explaining the perceived feelings derived from webrooming experience in the new normal context is given for rating.Using five-point Likert scale, participants were asked the extent to which they believe that their webrooming experiences had helped them to save time, effort, money, and make wise product choices, and which in turn influenced in their webrooming behaviour.  Again, the respondents feeling regarding webrooming leading to smart shopping is also assessed using five-point Likert scale.

The collected data showed non-normality and so non-parametric tests were employed to analyse, including the Kruskal-Wallis H test and the Mann-Whitney U test, in addition to Regression analysis. The Kruskal-Wallis H test, sometimes referred to as "one-way ANOVA on ranks," is a rank-based nonparametric assessment used to determine statistically significant distinctions among two or more independent groups concerning a dependent variable. Serving as a nonparametric counterpart to the one-way ANOVA, it extends the applicability of the Mann-Whitney U test by accommodating comparisons across more than two independent groups. Multiple linear regression (MLR), also known as multiple regression, is a statistical methodology involving several explanatory variables to predict the outcome of a response variable. In this study, multiple linear regression was conducted with the objective of modelling the linear relationship between independent variables and dependent variables. The primary aim of the multiple linear regression was to identify the influence of explanatory variables such as consumers' perceived sense of economizing time, effort, money, and making correct purchases through webroom experiences within the new normal context on their webrooming behaviour. Moreover, simple linear regression was performed to analyse the impact of webrooming experiences on the perception of intelligent shopping within the new normal context.

 

 

Conceptual Framework

Figure 1

Analysis and Results

As per the Table 1, showing descriptive data of respondents, 76% of the respondents fall within the age range of 20 to -30 and this indicate that majority of the consumers doing webrooming belongs to very young generation. Female are found to be more in webrooming than male and people with the income category of four lakhs to eight lakhs also have interest in webrooming.

Table1

Descriptive Analysis

Classification

Group

Count

Percentage

 

Age

20-40

190

76

41-60

38

15.2

Above 60

22

8.8

 

Gender

Male

74

29.6

Female

176

70.4

 

Income

(Annual)

Up to 10000

5

2

1L-4L

26

10.4

4L -8L

163

65.2

Above 8L

56

22.4

Source: Primary Data Analysis

Scale validation

The exploratory factor analysis has been conducted to reduce the variablesand noticed more than 0.8 for every variable under study. For five constructs with Eigen value greater than one were named also.Thus, the variables connected to feeling of timesaving named as ‘time’, variables connected to feeling of less effort named as ‘effort’, variables connected to feeling of saving in money named as ‘money’, variables connected to feeling of suitable product purchase named as ‘right purchase’and finally variables connected to benefits named as ‘utility’.

Table 2

EFA results

 

Factor loading

From Webrooming I experienced quick shopping

.952

 

 

 

 

Webroominghelped me to spend my time wisely

.947

 

 

 

 

From Webrooming I experienced a convenient shopping

 

.960

 

 

 

From Webrooming I experienced a hassle-free shopping

 

.963

 

 

 

From Webrooming I experienced a effort less shopping

 

.967

 

 

 

It assisted me in obtaining what I desired at a cost I was willing to bear

 

 

.900

 

 

I was able to purchase at price lower than normal

 

 

.935

 

 

It enabled me to get the products what I actually needed

 

 

 

.969

 

During webrooming, my purchase aligned precisely with what I had been seeking.

 

 

 

.974

 

Webroom based shopping perfectly fit my requirements

 

 

 

.970

 

Actually,I got a good deal on webrooming

 

 

 

 

.908

Webrooming experience helped me toshop quality goods as and when needed

 

 

 

 

.887

Source:Primary Data Analysis

The composite reliability of the constructs under investigation surpasses the minimum threshold of 0.70, as established by Nunnally (1978). Moreover, the Average Variance Extracted (AVE) values for all constructs exceed the cutoff criterion of 0.50, as proposed by Fornell and Larcker (1981). Discriminant validity was verified through AVE by ensuring that the AVE values for the five categories were greater than the shared variances (represented by squared correlation coefficients) among all conceivable pairs of constructs (Fornell and Larcker, 1981). Thus, the analysis confirmed high construct validities for all latent constructs of perceived feelings from webroomingexperience. Consumer’s webrooming behaviour in terms of frequency of doing webrooming in shopping and the effect of such consumer webrooming behaviour on the perception of smart shopping are measured using single variables only. So, no validity reliability tests were done for both the variables.

 

Table 3

Validity

 

AVE

Time

Effort

Money

Right

Time

0.901

       

Effort

0.928

0.569

     

Money

0.842

0.456

0.582

   

Right

0.942

0.358

0.469

.369

 

Utility

0.805

0.253

0.412

0.263

0.436

Source: Primary Data Analysis

Hypothesis Testing

Multiple regression has been conducted to measure the various factors contributing to the webrooming and it is shown in the table 4.

H1:The consumers perceived feeling from webrooming experiences in new normal context will have significant positive influence onwebrooming behaviour.

H1a  The consumers perception on saving of time from webrooming experiences at new normal context will have significant positive influenceon webrooming behaviour.

H1b.The consumers perception on saving of effort from webrooming experiences at new normal context will have significant positive influence on webrooming behaviour.

H1c. The consumers perception on saving of money from webrooming experiences at new normal context will have significant positive influence on webrooming behaviour

H1d: The consumers perception on right purchase from webrooming experiences at new normal context will have significant positive influence on webrooming behaviour.

H1e  The consumers perception on utility from webrooming experiences at new normal context will have significant positive influence on webrooming behaviour.

Table 4

Multiple Regression

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

2.118

.330

 

6.420

.000

Save time

.149

.059

.213

2.527

.013

Save effort

.123

.057

.193

2.156

.033

Save Money

.042

.066

.053

.629

.056

Right product

.125

.052

.171

2.426

.016

Provide utility

.188

.051

.258

3.652

.000

a. Dependent Variable: Webrooming Behaviour

      Source: Primary Data Analysis

 

Constant 2.118+ reduced time .213+ less effort .193+ less money .056+ right product 0.171+ improved utility 0.258

One unit of increase in the feeling of webrooming help to ‘save time’ have 0.213 impact on the consumers’ webrooming behaviour. For every one unit increase in the feeling of ‘less effort’ and ‘less money’ is needed to do shopping under webrooming have impacts of 0.193 and 0.056 on webrooming behaviour respectively. In the same way, the feeling of ‘right purchase’ made possible and improved ‘utility’ from webrooming have an influence of 0.171 and 0.258 on their webrooming behaviour respectively. Except in the case of variable named ‘save money’, in all other identified factors resulted positive impact on webrooming behaviour with less than .05 ‘p’ value and hence, null hypotheses are rejected and alternative will be accepted. In the case of the feeling ‘save money’, even though it reflected a positive association with webrooming behaviour, the ‘p’ value obtained is found is above .05. Therefore, null hypothesis with regards to this is to be upheld.

H2:The consumers’ webrooming behaviour have a positive impact on smart shopping

This hypothesis is tested with simple regression analysis and it is given in the table 2. The results of the analysis can be interpreted as, for one unit of increase in the feeling of webrooming behaviour have a 0.216 impact on the consumers’ smart shopping perception.

Table 2

Simple Regression

 

Co-efficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

T

Sig.

B

Std. Error

Beta

1

(Constant)

2.001

.677

 

2.955

.004

webrooming

.379

.141

.216

2.692

.008

a. Dependent Variable: smart shopping

Source: Primary Data Analysis

 

H3    Demographic variables have   impacts on consumers' webrooming behaviour

 

H3a: There is significant relationship between Age and Webrooming Behaviour in New Normal.

H3b: There is significant relationship between Annual Family Income and Webrooming Behaviour in New Normal

H3c: There is no significant relationship between Gender and Webrooming Behaviour in New Normal

Table3

Analysis of demographic variable

Grouping Variable

Kruskal-Wallis H/ Mann-Whitney U

df

Z

Asymp. Sig.

Age

3.141

2

 

.208

Annual Income

.454

3

 

.929

Gender

2217.500

 

-1.101

.271

Source: Primary Data Analysis

Here, the test result in relation to age shows Kruskal-Wallis H as 3.141, and the p value as to 0.208. Since the asymptotic significance is above 0.05, we can say that age and webrooming behaviour in new normal are independent. Hence, the null hypothesis that posits 'no significant relationship between age and the shift in online buying behaviour' remains unchanged or upheld. The test result related to income shows Kruskal-Wallis H as 0.454, and the p value is equal to 0.929. Since, the asymptotic significance is above 0.05, we can say that annual family income and webrooming behaviour in new normal are independent. Here the H0 i.e.,‘there is no significant difference between the annual family income and the webrooming experiences in new normal’ is not rejected.

The Mann Whitney test output from the above tables shows the U test value as 2217.5, Z value as -1.101 and the P value is equal to 0.811. It leads to the conclusion that, there is no significant difference between the gender and the webrooming behaviour in new normal because the asymptotic significance is above the level of significance, which is 0.05. Therefore, the null hypothesis is upheld.

Discussion and Conclusion

The tremendous growth in information technology and Covid 19 related lockdowns led to the development of many new and technology-driven shopping habits among customers. People began to spend more time in online, inquiring about products before making actual purchases from brick-and-mortar establishments. This study is an effort to ascertain the extent to which the results of such webrooming experiences are determining webrooming behaviour in the ‘new normal’ context among people and how far it is influencing their smart shopping perception. Here, the researcher took the initiative to develop a multiple regression model explaining the perceived feelings from webrooming experience and its influence on webrooming behaviour. Besides, a simple regression model is also developed explaining how webrooming behaviour affects consumers' smart shopping perceptions. This research stands apart from previous work as it evaluates how webrooming behaviour in the "new normal" affect customers' perceptions of smart shopping.

Webrooming constitutes a shopping behaviour aimed at mitigating the risk associated with online purchases by leveraging available information to enable buyers to make more informed and prudent decisions (Flavian, Gurrea, and Orus2016). This research paper analyses the webrooming behaviour, based on how consumers perceive their webrooming experiences in the context of the new normal in terms of time, effort, money, purchases, and utility. Hypotheses framed and tested show that webrooming experiences generate a feeling of savings in time, effort, money and enables to enjoy right purchase and provides increased utility.  The Exploratory Factor Analysis (EFA) analysis displays that the identified twelve variables measuring perceived feeling of webrooming experiences in ‘new normal’ have a significant influence on the variables named ‘save time’, ‘save effort’, ‘save money’, right purchase’ and ‘provide utility’ and this in turn collectively leads to ‘webrooming behaviour’. Similarly, the webrooming behaviour reflected a significant positive impact (0.216) on the consumer’s smart shopping perception and it was able to demonstrate that webrooming behaviour in the new normal stimulates smart shopping perception.

Further, we observed that the demographic variables, namely age, gender and annual income, have no influences on consumers’ webrooming behaviour in the ‘new normal’ and these variables are found as independent of webrooming behaviour. Irrespective of these demographic variables consumers' smart shopping perception get influenced by their webrooming behaviour and at the same time, in developing the feeling of smart shopping, webrooming have a significant role. So, retailers must modify their marketing strategies considering the consumer’s interest in using more than one channel in their smart shopping behaviour. They should arrange active, updated and attractive websites that promote their products along with storefronts. The presence of competent salespeople is also vital for dealing with smart and potential shoppers and for the smooth operation of any retail establishment.

 

References

Alba,J.J.L., Weitz, B., Janiszawski, C.,Lutz, R., Sawyer, A.,Wood, S.,1997.Interactive home shopping: consumer, retailer, and manufacturer incentives to participate in electronic market places. J. Mark.61(3),38–53.

Ali, Z., &Sonkusare, R. (2014, August). RFID based smart shopping: an overview. In 2014 international conference on advances in communication and computing technologies (ICACACT 2014) (pp. 1-3). IEEE.

Anitsal, I., and D. Schumann. 2007.“Toward a Conceptualization of Customer Productivity: The Customer’s Perspective on Transforming Customer Labor into Customer Outcomes Using Technology-based Self-service Options.”The Journal of Marketing Theory and Practice15(4): 349–363. doi:10.2753/MTP1069-6679150405

Aragoncillo,L.,Orús,C.,2018.Impulsebuyingbehaviour:an online-offline comparative and the impact of social media. Spanish J. Market.ESIC22(1),42–62

Atkins, K. G., and Y. K. Kim. 2012.“Smart Shopping: Conceptualization and Measurement.”International Journal of Retail and Distribution Management40 (5): 360–375. doi:10.1108/09590551211222349

Audrain-Pontevia, A. F., N' Goala, G., Poncin, I ., 2013. A good deal online : the Impacts of acquisition and transaction value on E-satisfaction and E-loyalty.J.Retail.Consum.Serv.20(5),445–452

Avery, J., Steenburgh, T. J., Deighton, J., and Caravella, M. (2012). Adding bricks to clicks: predicting the patterns of cross-channel elasticities over time. J. Mark. 76, 96–111. doi: 10.1509/jm.09.0081

Balasubramanian, S., R. Raghunathan, and V. Mahajan. 2005.“Consumers in a Multichannel Environment: Product Utility, Process Utility, and Channel Choice.” Journal of InteractiveMarketing19 (2): 12–30. doi:10.1002/dir.20032.

Bicen, P., & Madhavaram, S. (2013). Research on smart shopper feelings: An extension. Journal of Marketing Theory and Practice21(2), 221-234.

Burton, S., Lichtenstein, D.R., Netemeyer, R.G., Garretson, J.A.,1998. A scale for measuring attitude toward private label products and an examination of its psychological and behavioural correlates. J.Acad.Mark.Sci.26(4),293–306.

Daugherty,T.,Li,H.,Biocca,F.,2008. Consumer learning and the effects of virtualex- perience relative to indirect and direct product experience. Psychol. Mark .25,568–586

Dempski, K., M. Redding, R. Scaff, M. Schmaltz, and K. &Voelker. 2019. A new era for retail cloud computing changes the game, 1–40. Dublin, Ireland: Accenture

Dholakia,R.R., Zhao, M., Dholakia,N.,2005.Multichannel retailing: a case study of early evidence. J.Interact. Mark.19(2),63–74.

Downs A. 1961.“A Theory of Consumer Efficiency.” Journal of Retailing37 (1): 6–12, 50.

Verhoef, P.C.,Neslin,S.A., Vroomen,B., 2007.Multichannel customer management: understanding the research-shopperphenomenon.Int.J.Res.Mark.24(2),129–148.

Fernández, N.V.,Pérez, M.J.S.,Vázquez-Casielles,R.,2018.Webroomers versus how-roomers :are they the same ? J. Bus.Res.92,300–320

Flavián,C., Gurrea, R., Orús,C.,2016.Choiceconfidencein the webrooming purchase process: the impact of online positive reviewsandthemotivationtotouch.J.Consum.Behav.15(5),459–476

Fornell, C. and Larcker, D.F. (1981), “Structural equation models with unobservable variables and measurement error: algebra and statistics”, Journal of Marketing Research, Vol. 18 No. 3, pp. 382-8

Gensler,S.,Neslin,S.A.,Verhoef,P.C.,2017.The show rooming phenomenon: it's more than just about price. J. Interact.Mark.38,29–43.

Harris,P.,Riley,F.D.O.,Hand,C.,2018.Understandingmultichannelshopperjourneyconfiguration:an applicationofgoaltheory.J.Retail.Consum.Serv.44,108–117.

Heitz-Spahn,S.,2013. Cross-channel free-riding consumer behaviour in a multichannel environment: an investigation of shopping motives, socio demographics and product categories. J. Retail.Consum.Serv.20(6),570–578

Herhausen,D.,Binder,J.,Schögel,M.,Herrmann,A.,2015.Integratingbrickswithclicks:retailer-levelandchannel-leveloutcomesofonline–offlinechannelintegration.J.Retail.91(2),309–325)

Hsiao, M. H. (2009). Shopping mode choice: Physical store shopping versus e-shopping. Transportation Research Part E: Logistics and Transportation Review, 45(1), 86-95.

Huré,E.,Picot-Coupey,K.,Ackermann,C.L.,2017.Understanding omni-channel shopping value: a mixed-method study. J. Retail.Consum.Serv.39,314–330.

Ingene, C. A. 1984.“Productivity and Functional Shifting in Spatial Retailing: Private and Social Perspectives.”Journal of Retailing60 (3): 15–36.

Jang,S.,Prasad,A.,Ratchford,B.T.,2017. Consumer search of multiple information sources and its impact on consumer price satisfaction. J. Interact.Mark.40,24–40

Kalyanam, K., and A. A. Tsay. 2013.“Free-riding and Conflict in Hybrid Shopping Environ-ments: Implications for Retailers, Manufacturers, and Regulators.”The Antitrust Bulletin58(1): 19–68. doi:10.1177/0003603X1305800102

Kang,J.Y.M.,2018. Showrooming, webrooming, and user-generated content creation in the omnichannel era.J.InternetCommer.1–25.

Kothari, A., & Dawar, S. (2014). Sustainable Consumption: An Imperative for India. Asian Journal of Research in Business Economics and Management, 4(7), 53-61.

Kushwaha,T.,Shankar,V.,2013.Aremultichannelcustomersreallymorevaluable?Themoderatingroleof product category characteristics. J.Mark.77(4),67–85.

Lemon,K.N.,Verhoef, P.C.,2016.Understanding customer experience throughoutthecustomerjourney.J. Mark.80(6),69–96.

Mano,H., Elliott,M.T.,1997. Smart shopping: the origins and consequences of price savings. Adv. Consum. Res. 24,504–510.

Manzur, E., Olavarrieta, S., Hidalgo, P., Farías, P., Uribe, R., 2011.Store brand and national brand promotion attitudes antecedents. J. Bus.Res.64,286–291.

Mehta, S., Saxena, T., & Purohit, N. (2020). The new consumer behaviour paradigm amid COVID-19: permanent or transient?. Journal of health management22(2), 291-301.

Mills, P. K., R. B. Chase, and N. Margulies. 1983.“Motivating the Client/Employee System as aService Production Strategy.”Academy of Management Review8 (2): 301–310. doi:10.5465/AMR.1983.4284740.

Mokhtarian, P.L., 2004. A conceptual analysis of the transportation impacts of B2C e-commerce. Transportation 31, 257–284.

Nathwani, D. (2022). The New Normal Consumer: A Study on Onilne Shopping Behaviour of Consumers. SJCC Management Research Review, 11-25.

Neslin, S.A., Jerath,K., Bodapati,  A.,Bradlow, E.T., Deighton,J., Gensler, S.,Lee,L.,Montaguti,E.,Telang,R., Venkatesan, R., Verhoef, P.C.,Zhang,Z.J., 2014.The interrelationships between brand and channel choice. Mark.Lett.25(3),319–330

Neslin, S.A., Shankar, V., 2009. Keyissuesinmultichannelcustomermanagement:cur-rentknowledgeandfuture directions.J.Interact.Mark.23,70–81

Nielsen, N. V. (2016). What Are Connected Shoppers Doing and Not Doing Online?. Available online at: https://www.nielsen.com/ssa/en/insights/article/2016/what-are-connected-shoppers-doing-and-not-doing-online/

Nunnally, J.C. (1978), Psychometric Theory, 2nd ed., McGraw-Hill, New York, NY

Pauwels,K.,Leeflang,P.S.,Teerling,M.L.,Huizingh,K.R.,2011.Does online information drive off-line revenues?: only for specific products and consumer segments! .J. Retail. 87(1),1–17

Peterson, R.A., Balasubramanian, S., Bronnenberg, B. J. , 1997. Exploring the implications of the Internet for consumer marketing. J. Acad.Mark.Sci.25 (4),329–346.

Shankar, V. (2011). Shopper marketing. Massachussets, USA: Marketing Science Institute.

Shankar, V., A. Venkatesh, C. Hofacker, and P. Naik. 2010.“Mobile Marketing in the Retailing Environment: Current Insights and Future Research Avenues.” Journal of Interactive Market-ing24 (2): 111–120. doi:10.1016/j.intmar.2010.02.006.

Shankar, V., and S. Balasubramanian. 2009.“Mobile Marketing: A Synthesis and Prognosis.” Journal of Interactive Marketing23 (2): 118–129. doi:10.1016/j.intmar.2009.02.002.

Singh,S.,Ratchford,B.T.,Prasad,A.,2014.Offline and online search in used durables markets .J. Retail. 90 (3), 301–320.

Singh,S.,Swait,J.,2017.Channels for search and purchase: does mobile Internet matter? J. Retail. Consum. Serv.39,123–134

Tangen, S. 2005.“Demystifying Productivity and Performance. ”International Journal of Productivity and Performance Management 54 (1): 34–46. doi:10.1108/17410400510571437.

VanBaal,S.,Dach,C.,2005.Freeridingandcustomerretentionacrossretailers'channels.J.Interact.Mark.19(2),75–85

Vander Veen, G., van Ossenbruggen, R., 2015. Mapping out the customer's journey : customer search strategy as a basis for channel management .J. Mark.Channels22(3),202–213

Verhoef, P.C.,Kannan, P.K., Inman, J.J.,2015.Frommulti-channelretailingtoomni-channelretailing:introductiontothe special issue on multi-channelretailing.J.Retail.91(2),174–181

Verhoef, P. C., Neslin, S. A., and Vroomen, B. (2007). Multichannel customer management: understanding the research-shopper phenomenon. Int. J. Retail Distrib. Manage. 24, 129–148. doi: 10.1016/j.ijresmar.2006.11.002

Walsh,G.,Mitchell,V.W.,2010.The effect of consumer confusion proneness on word of mouth, trust, and customer satisfaction. Eur. J. Market.44(6),838–859.

Wollenburg,J.,Holzapfel,A.,Hübner,A.,Kuhn,H.,2018.Configuring retail fulfilment processes for omni-channel customer steering.Int.J.Electron.Commer.22(4),540–575.

Zalega, T. (2017). Smart shopping in consumer behaviour of Polish seniors (report from own research). Acta ScientiarumPolonorum. Oeconomia16(3).

 

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