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1 – 3 of 3Heba Al Kailani, Ghaleb J. Sweis, Farouq Sammour, Wasan Omar Maaitah, Rateb J. Sweis and Mohammad Alkailani
The process of predicting construction costs and forecasting price fluctuations is a significant and challenging undertaking for project managers. This study aims to develop a…
Abstract
Purpose
The process of predicting construction costs and forecasting price fluctuations is a significant and challenging undertaking for project managers. This study aims to develop a construction cost index (CCI) for Jordan’s construction industry using fuzzy analytic hierarchy process (FAHP) and predict future CCI values using traditional and machine learning (ML) techniques.
Design/methodology/approach
The most influential cost items were selected by conducting a literature review and confirmatory expert interviews. The cost items’ weights were calculated using FAHP to develop the CCI formula.
Findings
The results showed that the random forest model had the lowest mean absolute percentage error (MAPE) of 1.09%, followed by Extreme Gradient Boosting and K-nearest neighbours with MAPEs of 1.41% and 1.46%, respectively.
Originality/value
The novelty of this study lies within the use of FAHP to address the ambiguity of the impact of various cost items on CCI. The developed CCI equation and ML models are expected to significantly benefit construction managers, investors and policymakers in making informed decisions by enhancing their understanding of cost trends in the construction industry.
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Keywords
Mohammad Arief, Rita Indah Mustikowati and Yustina Chrismardani
Digitalization in marketing activities has made it easier for people to make purchase decision. This platform encourages every firm to optimize digitalization as part of its…
Abstract
Purpose
Digitalization in marketing activities has made it easier for people to make purchase decision. This platform encourages every firm to optimize digitalization as part of its marketing strategy. Optimization of attractive digital marketing involves advertising attractiveness, influencer marketing and online customer reviews. This study aims to investigate advertising attractiveness, influencer marketing and online customer reviews on purchase decision.
Design/methodology/approach
The study was conducted with a quantitative approach. A total of 120 respondents were involved in this study by using convenience sampling techniques in data collection. Multiple linear regression was used to analyze the data.
Findings
The results of the study show that influencer marketing and online customer reviews have an impact on online purchase decision. Meanwhile, advertising attractiveness does not show any influence on purchase decision.
Practical implications
Despite the start-ups have modified the website by increasing the content to make it more informative, it seems that customers are not interested in making a purchase. Therefore, notwithstanding the role of website attractiveness, the use of physical attractiveness is still considered an effective way to encourage customers to make purchasing decisions. In this way, a firm needs to make adjustments between the customers' personality, lifestyle and attitudes and endorsers.
Originality/value
This study developed previous empirical studies which a positive relationship between advertising attractiveness, influencer marketing, online customer reviews and purchase decision. The development of the model was carried out by elaborating variable indicators. In addition, the source of increasing credibility was not based on physical attractiveness, but rather emphasizes the website quality.
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