Online Impulsive Buying Behavior Using Partial Least Squares Algorithm

In the online shopping context, the proliferation of digital platforms has contributed to an increase in impulsive buying behavior (IBB), which can sometimes lead to regret. This study aims to explore the intrinsic and extrinsic stimuli that influence consumers’ online impulsive buying behavior, which subsequently affects their post-purchase cognitive dissonance, with the moderating role of price consideration (PC). The conceptual framework was formulated using the Stimulus–Organism–Response (S-O-R) model. A total of 813 responses were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived utilitarian value (PUV), perceived enjoyment (PE), fear of missing out (FOM), and green trust (GT) positively impact online impulsive buying behavior (IBB), which, in turn, positively impacts post-purchase cognitive dissonance (PCD). Moreover, a significant moderating role of PC is found in the relationship between IBB and PCD, suggesting that consumers with low price consideration tend to regret their impulsive buying more. The findings provide insights that guide online retail sellers and digital marketers to develop or implement customized strategies based on the intrinsic and extrinsic stimuli that influence customers’ impulsive buying and subsequent post-purchase cognitive dissonance.

This study examines the influence of financial literacy and lifestyle on impulsive buying behavior among e-commerce users in Tulungagung, Indonesia. Using an explanatory quantitative approach and survey data from 120 respondents, the analysis was conducted with the Partial Least Squares (PLS) method via SmartPLS software. The findings reveal that while lifestyle significantly and positively impacts impulsive buying behavior, financial literacy does not have a significant independent effect. Together, these variables explain 54.7% of the variance in impulsive buying, suggesting a moderate influence and highlighting the role of other potential factors. The results emphasize that a modest lifestyle observed among the Tulungagung community mitigates impulsive buying tendencies despite active online shopping habits, whereas financial literacy alone may be insufficient to prevent impulsive behavior, particularly among younger consumers. These insights underscore the need for integrated strategies combining financial education and behavioral interventions to promote responsible consumption, offering practical implications for policymakers, educators, and e-commerce platforms.

Objective; This study aims to determine the impact of online promotions, trust, and website quality on online impulsive purchases, as well as to examine consumers' emotional responses after making online impulsive purchases. Methods; It specifically investigates the influence of online impulsive purchases on both positive and negative emotions. This study used an explanatory method with a quantitative approach. Originality/Value;Furthermore, the samples were 100 consumers of the Shopee Marketplace located in Lampung Province, Indonesia. The data were obtained through a questionnaire and analyzed using descriptive statistics and Partial Least Squares (PLS) analysis. Findings; The results showed online promotions and website quality had a significant influence on online impulsive purchases, while trust had no significant influence. Also, online impulsive purchases were found to have a significant influence on positive emotions, but no significant influence on negative emotions. Practical/Policy implication: Research shows that engaging online promotions and a high-quality website can encourage impulse buying, which is driven by emotions and situations, not beliefs. Impulse buying also increases positive emotions, so marketing strategies that stimulate it have the potential to increase customer satisfaction and loyalty.

The trend towards online shopping and the growing demand for makeup products among Vietnamese women have created both opportunities and fierce competition for e-commerce businesses in the makeup sector. To stand out, businesses need innovative strategies to capture attention and trigger purchase responses from customers. This study aims to explore how Augmented Reality (AR) influences customers’ hedonic values and then leads their impulse buying, based on the Stimulus-Organism-Response (SOR) theory. By employing a quantitative method, 209 responses from Vietnamese female customers were gathered and then analyzed using Structural Equation Modeling (SEM) with SPSS and AMOS software. The findings of this study indicate that four AR characteristics, including product informativeness (β = 0.538, p = 0.000), interactivity (β = 0.377, p = 0.000), vividness (β = 0.291, p = 0.000), novelty (β = 0.163, p = 0.027) positively affect customers’ hedonic value. Subsequently, customers’ hedonic value demonstrates a significant positive effect on their online impulse buying (β = 0.567, p = 0.000). Furthermore, the results also show that hedonic value plays a perfect mediator in the relationships between AR characteristics and online impulse buying. The findings of this study also provide managerial implications for firms in the makeup sector using AR effectively on e-commerce platforms to benefit from enhancing impulse buying.

Influencer marketing has emerged as a powerful tool of digital persuasion; nevertheless, the processes according to which it affects consumer decision-making have not been explored well enough in the case of emerging markets where the institutional trust in online shopping is weak. The paper explores the influencer marketing effect on consumer purchase intention through analyzing the impact of influencer credibility, content quality, and artificial intelligence (AI)-driven personalization through the Pakistan social media environment. The study is based on the Source Credibility Theory and the Stimulus Organism Response (S-O-R) model and informed by attitude-based approaches as represented in the literature on consumer behavior and constructs an integrated model of both cognitive and affective influence pathways. The study utilized a quantitative, cross-sectional research design that involved the use of a structured survey in the sample comprised of active social media users in Pakistan and who have been recently exposed to influencer-endorsed content. There were 218 valid responses analyzed through the Partial Least Squares Structural Equation Modeling (PLS-SEM) that is appropriate in the context of theory expansion and the complicated models with mediation and moderation. According to the results, influencer credibility and AI-improved personalization have a significant positive effect on consumer trust and emotional attachment, and miscellaneous contents generally have a positive impact on purchase intention through a functional assessment and not relational bond. However, the most notable predictor of the purchase intention was consumer trust and the emotional attachment did not readily translate to the actual buying behavior. The consumer attitude portrayed a small yet significant moderating influence on the trust purchase intention relationship. The results emphasize the importance of trust-driven mechanisms over emotional persuasion in risk sensitive online space and give context specific insights into influencer marketing practices in new market.

The phenomenon of impulse buying has become an important subject in the study of consumer behavior. With increasing competition in the retail market, a better understanding of the factors that influence impulse buying is essential for retailers to design effective marketing strategies. This issue is important because it provides deeper insight into consumer behavior and offers guidance for retailers in developing more effective marketing approaches. This study aims to identify and analyze the effect of price discounts and hedonic shopping value on impulsive buying and to test the role of positive emotions in mediating the effects of these variables on impulsive buying. The population of this study consisted of retail consumers in minimarkets in Tangerang, Banten, The population is not known for certain. The sample size was 384 respondents. The sample was collected using non-probability sampling techniques, specifically purposive sampling. The data collected were analyzed using Microsoft Excel, Statistical Product and Service Solutions (SPSS), and Partial Least Square (PLS) with path analysis. The results of the study showed that price discounts have a significant impact on impulsive buying. Hedonic shopping value also significantly affects impulsive buying. The indirect effect of price discounts on impulsive buying through positive emotions is not significant. Hedonic shopping value has a significant indirect effect on impulsive buying through positive emotions.

As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.

E-commerce is a place for people to make both planned and impulsive purchases, especially for younger generations such as gen Z. This study aims to determine the effect of website quality, sales promotion, and shopping lifestyle on impulse purchases in generation Z e-commerce users (Tokopedia, Shopee, Lazada) in DKI Jakarta. This study uses quantitative descriptive analysis which then the data is obtained from distributing questionnaires using non-probability sampling method with purposive sampling. The sample used was 100 generation Z people aged 17 years and over e-commerce users (Tokopedia, Shopee, Lazada) in DKI Jakarta. The data analysis used is descriptive and inferential data analysis techniques using Partial Least Square (PLS) with SmartPLS 3.2.9 software. The results of this study indicate (1) website quality has a positive and significant effect on impulse purchases with a path coefficient of 0.256. (2) sales promotion has a positive and significant effect on impulse purchases. Shopping lifestyle has a positive and significant effect on impulse purchases with a path coefficient of 0, 376.

Drawing on the stimulus-organism-response (S-O-R) paradigm this study focuses on online sales promotion, impulse buying tendency and social influence to examine online impulsive buying behavior of online customers of Pakistan. The study further assesses the mediating role of urge to buy impulsively on the relationship of sales promotion, impulse buying tendency, social influence and online impulse buying behavior. A total 576 questionnaires were distributed to students from four universities of using convenience sampling technique. The study has used SPSS (version 21.0) and PLS-SEM (version 3.2.8) for analyzing data at 0.05 critical level. According to the analysis results all the factors including sales promotion, impulse buying tendency and social influence significant influence on online impulse buying behavior. Results revealed the positive impact of targeted constructs on online impulse buying behavior with mediating role of urge to buy impulsively.The study adds new theoretical linkages and empirical evidences on the interaction between by sales promotion, impulse buying tendency, social influence and online impulse buying behavior with theoretical support of S-O-R Model. Moreover, this study is useful for e-marketers in assisting them to develop appropriate policies and customer-driven strategies by addressing customer’s online impulse buying behavior in south Asian context.

With the advancement of immersive technologies, metaverse museum exhibitions have become an increasingly important medium through which audiences access cultural content and experience artistic works. This study aims to identify the key factors influencing visitors’ processual experiences in metaverse museum exhibitions and to explore how these factors collectively contribute to the formation of satisfaction with the visiting experience. Adopting an interdisciplinary theoretical perspective, the study integrates the Experience Economy theory with the Stimulus–Organism–Response (SOR) model to construct a systematic theoretical framework. This framework reveals how exhibition-related stimuli affect visitors’ behavioral intentions through psychological response pathways. Specifically, perceived educational appeal, interactive entertainment, escapist experience, and perceived visual aesthetics are defined as stimulus variables, while psychological immersion, emotional trigger, and cognitive engagement are introduced as organismic variables to explain their effects on satisfaction with the visiting experience and social sharing intention as response variables. Based on 507 valid responses, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for empirical analysis. The results indicate that interactive entertainment and escapist experience have significant positive effects on psychological responses, serving as key drivers of deep visitor engagement. Emotional Trigger acts as a significant mediator between exhibition stimuli and satisfaction with the visiting experience, which in turn significantly predicts social sharing intention. In contrast, perceived educational appeal and perceived visual aesthetics exhibit weaker impacts at the cognitive and behavioral levels. This study not only identifies these weakened pathways but also proposes optimization strategies grounded in experiential construction and cognitive synergy, offering guidance for enhancing the educational function and deep experiential design of metaverse exhibitions. The findings validate the applicability of the Experience Economy theory and the SOR model in metaverse cultural contexts and deepen our understanding of the psychological mechanisms underlying immersive cultural experiences. This study further provides a pathway for shifting exhibition design from a “content-oriented” to an “experience-driven” approach, offering theoretical and practical insights into enhancing audience engagement and cultural communication effectiveness in metaverse museums.

This study examines digital anxiety in the era of technology and social media, which causes consumer confusion in purchasing decisions, especially among Generation Z (born 1997-2012) in Indonesia. This generation is susceptible to Fear of Missing Out (FOMO), social media addiction (consisting of weak behavioral control and dysfunctional behavior), and social anxiety, which can trigger impulsive buying and post-purchase regret. The main objective is to analyze the influence of FOMO, social media addiction, and social anxiety on impulsive buying and the influence of impulsive buying on post-purchase regret. The method uses a quantitative approach with Structural Equation Modeling (SEM) based on Partial Least Square (PLS) version 3.0, involving a minimum of 209 Generation Z respondents who are active on social media and have shopped online through purposive sampling in Indonesia. The results show that FOMO, weak behavioral control, and dysfunctional behavior have a significant positive effect on impulsive buying, while social anxiety is not significant. Impulsive buying also has a significant positive effect on post-purchase regret. The implications of the research include understanding the negative impacts of social media on consumer well-being, as well as recommendations for digital platforms and governments to create regulations and awareness campaigns to prevent regret in Generation Z.

Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table. When the goal is to find the shared information between two tables, the approach is equivalent to a correlation problem and the technique is then called partial least square correlation (PLSC) (also sometimes called PLS-SVD). In this case there are two sets of latent variables (one set per table), and these latent variables are required to have maximal covariance. When the goal is to predict one data table the other one, the technique is then called partial least square regression. In this case there is one set of latent variables (derived from the predictor table) and these latent variables are required to give the best possible prediction. In this paper we present and illustrate PLSC and PLSR and show how these descriptive multivariate analysis techniques can be extended to deal with inferential questions by using cross-validation techniques such as the bootstrap and permutation tests.

The phenomenon of impulse buying has been attracting the attention of researchers and practitioners worldwide for decades and it continues to attract attention in e-commerce domain in recent years. However, there are few studies on the determinants of online impulse buying behavior of young consumers especially in developing countries. Therefore, this study research aims to fill this gap and explore the influencing factors on young consumers' impulsive behaviour while online in North Macedonia. Followed by the accelerated growth of e-commerce in North Macedonia especially during and after pandemic, the interest on impulse buying behaviour of online consumers has increased from marketing and business perspective. This study examines the crucial factors impacting consumers online impulse buying behaviour based on the extended Technology Acceptance Model (TAM). A survey was conducted via an online questionnaire, among 151 young respondents from March to May 2023. Demographic data was collected as well as data considering shopping online habits and structure of goods/services shopped online. The basic constructs of the initial model, such as perceived ease of use, perceived usefulness, attitude towards use, were examined. Specific new constructs of interest were added in the research such as individual characteristics (impulsiveness) and environment mainly including website quality measured by several features in relation to impulse buying. The data analysis supports the presumption that personal tendency to buy on impulse is the most significant predictor of impulse buying intention online. The model developed in this study provides relevant theoretical and practical implications by confirming that some of the tested factors are critical in online impulsive buying behaviour of generation Z consumers in a developing country context. Findings can be instrumental in the context of web design and choice of products and/or services to be offered as items for impulse buyers in North Macedonia.

This study aims to examine the effect of sales promotion on impulsive buying among TikTok Shop users, with shopping motivation as a moderating variable. The rapid growth of social commerce has encouraged businesses to intensively use sales promotions such as discounts, free shipping, and flash sales, which can trigger unplanned purchasing behaviour. This research employs a quantitative approach using a survey method conducted on TikTok Shop users who have previously made purchases. The sample was selected through purposive sampling, and the data were analysed using the Partial Least Square (PLS) method with the assistance of SmartPLS software. The results indicate that sales promotion has a positive and significant effect on impulsive buying. Furthermore, shopping motivation is proven to moderate the relationship between sales promotion and impulsive buying, strengthening the impact of promotional activities on consumers with higher shopping motivation. This study contributes to consumer behaviour literature and provides practical insights for businesses in designing effective promotional strategies on TikTok Shop.

We aimed to identify the browning of white adipocytes using partial least squares regression (PLSR), infrared spectral biomarkers, and partial least squares discriminant analysis (PLS-DA) with FTIR spectroscopy instead of molecular biology. PLSR helps distinguish human beige adipocytes treated with norepinephrine and rosiglitazone. When PLSR was based on the selected regions of 3997–3656 and 1618–938 cm−1, PLSR achieved an R2 of cross-validation of 88.95, a root mean square error of cross validation (RMSECV) of 2.13, and a ratio performance deviation (RPD) of 3.01. Infrared spectral biomarkers [1635 cm−1 (β-sheet amide I), 879–882, 860–3 cm−1 (A-form helix), and 629–38 cm−1 (OH out-of-plane bending)] were identified in human beige adipocytes based on spectral differences between human beige adipocytes and human white adipocytes, principal component analysis-linear discriminant analysis (PCA-LDA) cluster vector, U-test, and Fisher’s score per wavenumber. PLS-DA yielded a useful classification of adipocytes and expression distribution of adipogenesis genes in adipocytes. PLSR, infrared spectral biomarkers, and PLS-DA using FTIR spectroscopy are proposed as effective tools for identifying specific biological activities in a limited environment through features that do not require labeling and are relatively inexpensive in terms of time and labor.