Partial least-squares path modelling with latent variables

1979 ◽  
Vol 112 (4) ◽  
pp. 417-421 ◽  
Author(s):  
Robert W. Gerlach ◽  
Bruce R. Kowalski ◽  
Herman O.A. Wold
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Path Modelling

Smart shopping: the adoption of grocery shopping apps

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lee-Andra Bruwer ◽  
Nkosivile Welcome Madinga ◽  
Nqobile Bundwini
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Moderating Effect ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
Grocery Shopping ◽  
Intention To Use ◽  
Content Type

PurposeThe purpose of this paper is to determine the key factors influencing the adoption of grocery shopping and to examine the moderating effect of education between antecedents of the adoption of grocery shopping apps and user attitude and intention to purchase.Design/methodology/approachThis study adopted partial least squares structural equation modeling (PLS-SEM) to evaluate the relationship between the latent variables: perceived usefulness, perceived ease of use, attitude and intention to use grocery shopping apps. Partial least squares multigroup analysis (PLS-MGA) was used to examine the moderating effect of education. A total of 305 grocery shopping apps users were surveyed using a structural questionnaire.FindingsThe results indicated that all the factors considered in the framework were significant in predicting the intention to use the grocery shopping apps. The findings show that education has no significant impact on any relationship.Practical implicationsA better understanding of the factors that affect the acceptance of mobile grocery shopping apps is important for developing better strategic management plans.Originality/valueThis is one of the first studies to research the adoption of grocery shopping apps in a developing country, as well as the first to focus on consumers in South Africa.

scholarly journals Predictors of Likelihood of Adoption of Green Practices in Hotels: The Case of Abuja and Lagos, Nigeria

2017 ◽  
Vol 6 (1) ◽  
pp. 72
Author(s):  
Ladi Muazu ◽  
Basri Rashid ◽  
Noor Azimin Zainol
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Hotel Industry ◽  
Organizational Characteristics ◽  
Perceived Benefits ◽  
General Managers ◽  
Green Practices ◽  
Path Modelling ◽  
Innovation Characteristics ◽  
High Level

Despite the high level of awareness of sustainable practices around the globe, and literatures on its adoption in businesses, studies showed very few studies are conducted in less advanced countries like Nigeria. Given the rising consumption of resources, apprehension for the environs and sudden realization among people for practices that are friendlier, the trend is gradually picking up in the Nigerian hotel industry. The hotel is facing challenges accepting green practices due to little information on green practice and its perceived benefits, government inability to enforce regulations on environmental sanitation, and absence of enlightenment promotion of green practice. This study strives to determine factors likely to motivate hotels to adopt innovative ways of operating in Nigeria. Based on Rogers’ theory of innovation diffusion, this study decided to establish perceived innovation characteristics, perceived environmental characteristics, organizational characteristics and stakeholders’ influence as determinants for likely adoption. Data was collected from General Managers of hotels (363 in number) in Abuja and Lagos. Using Partial Least Squares (PLS) path modelling, the findings indicate all perceived predictors positively correlated to likelihood of adoption.

Experienced international business traveller’s behaviour in Iran: A partial least squares path modelling analysis

2016 ◽  
Vol 18 (2) ◽  
pp. 163-190 ◽  
Author(s):  
Sajad Rezaei ◽  
Milad Kalantari Shahijan ◽  
Naser Valaei ◽  
Roya Rahimi ◽  
Wan Khairuzzaman Wan Ismail
Keyword(s):  
Risk Perception ◽  
Least Squares ◽  
Partial Least Squares ◽  
International Business ◽  
Risk Perceptions ◽  
Information Sources ◽  
Destination Image ◽  
Travel Experience ◽  
Modelling Analysis ◽  
Path Modelling

Few researchers have examined travellers’ experience with destinations despite the importance of their attitudes, behaviour and perception in selecting destinations. Current study aims to examine the relationship between risk perceptions, motivation, information source, travel experience and destination image among experienced international business travellers in Iran. The total number of 234 valid questionnaires was collected from international business travellers and structural equation modelling was employed using partial least squares path-modelling analysis to assess measurement and structural model for reflective constructs. Our empirical results support the negative relationship between destination image and risk perception, travel experience and risk perception while information sources were found to be unrelated to travellers risk perceptions. The results further shown that information sources and destination image, information sources and motivation, motivation and travel experience and destination image are related. However, the partial least squares-multigroup analysis results reveal that the significance of path coefficients differs across various demographic subgroups. Moreover, our results support experience and risk perception as a second-order reflective construct. Practical and theoretical implications are discussed along with a discussion on research limitations.

Quantitative XRD Analysis by Partial Least Squares Application in a Commercial Product

1991 ◽  
Vol 35 (A) ◽  
pp. 117-126
Author(s):  
Arthur G. Mateos
Keyword(s):  
Data Compression ◽  
Least Squares ◽  
Diffraction Pattern ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Xrd Analysis ◽  
Commercial Product ◽  
Calibration Model ◽  
New Approach ◽  
Data Points

AbstractA new approach to quantitative XRD by Partial Least Squares (PLS) used region(s) or the entirety of the diffraction pattern of calibration standards (also called a training set) in the model. The basic concept of this approach states that the information in many observed variables, expressed as matrix I = (i1, i2 … , ik,) is concentrated onto a few underlying latent variables, called factors, by the process of data compression. In XRD, the data points of the diffraction pattern are compressed to few factors T, computed according to their ability to explain the variation in the diffraction pattern or matrix I. The procedure incorporates into the model that part of I that is correlated to C concentrations. Data compression preserves the redundancy between variables due to collinearity and stabilizies the predictions against noise in I. The resulting calibration model allows for detection of outliers. Another important effect of data reduction is the ability to analyze muticomponent systems even when lines of the components are overlapped, Examples of quantitative analysis by PLS are demonstrated in the analysis of a commercial product.

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