Decision Analytics Journal

This research examines the factors that influence the implementation of green procurement in small and medium enterprises (SMEs), namely top management commitment, regulations, costs, pressure from consumers, and transformational leadership as moderation. The research employs a quantitative approach with 206 samples from the Indonesian SME industry. It utilizes Structural Equation Modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (FsQCA). The data set includes factors such as top management commitment, regulations, costs, consumer pressure, and transformational leadership as moderation variables. The results show that management commitment, regulations, costs, and customer pressure influence green procurement. The role of the moderating variable shows that transformational leadership only strengthens the influence of costs on green procurement and regulation on green procurement. Meanwhile, the influence of top management commitment to green procurement and customer pressure on green procurement shows that transformational leadership does not strengthen these variables. The research's novelty lies in combining SEM and FsQCA to analyze the causal relationships among variables influencing green procurement in SMEs. It highlights the importance of understanding and effectively managing these relationships for organizations adopting green procurement practices.

Maulana Abdul Hafish

Ilyas Masudin

Fien Zulfikarijah

Tsiqatun Nasyiah

Dian Palupi Restuputri

An integrated structural equation modeling and fuzzy qualitative comparative analysis model for examining green procurement adoption drivers

绿色采购采纳驱动因素的结构方程建模与模糊定性比较分析集成模型

Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL) research for their architectural advantages. CNN relies heavily on hyperparameter configurations, and manually tuning these hyperparameters can be time-consuming for researchers, therefore we need efficient optimization techniques. In this systematic review, we explore a range of well used algorithms, including metaheuristic, statistical, sequential, and numerical approaches, to fine-tune CNN hyperparameters. Our research offers an exhaustive categorization of these hyperparameter optimization (HPO) algorithms and investigates the fundamental concepts of CNN, explaining the role of hyperparameters and their variants. Furthermore, an exhaustive literature review of HPO algorithms in CNN employing the above mentioned algorithms is undertaken. A comparative analysis is conducted based on their HPO strategies, error evaluation approaches, and accuracy results across various datasets to assess the efficacy of these methods. In addition to addressing current challenges in HPO, our research illuminates unresolved issues in the field. By providing insightful evaluations of the merits and demerits of various HPO algorithms, our objective is to assist researchers in determining a suitable method for a particular problem and dataset. By highlighting future research directions and synthesizing diversified knowledge, our survey contributes significantly to the ongoing development of CNN hyperparameter optimization.

Mohaimenul Azam Khan Raiaan

Sadman Sakib

Nur Mohammad Fahad

Abdullah Al Mamun

Md. Anisur Rahman

Swakkhar Shatabda

Md. Saddam Hossain Mukta

A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks

卷积神经网络中超参数优化技术的系统综述

The technological habitat in which successful firms may flourish is in rapid change. With digital transformation (DT), everything is connected and interdependent. Manual processes are now automated for most businesses. As a result, habits, and needs are changing, which, in turn, affects the conventional way firms offer their goods and services. Airlines are eager to participate in the trend as well. Those who adapt quickly to this new world will gain a greater share of the pie. However, most airlines have bulky and rigid systems designed based on the industry’s strict rules and regulations. Thus, the transformation process required to adapt to the new era of digitalization is not a simple task. Several interrelated factors, such as strategy, organization, customer, technology, operations, ecosystem, and innovation, have to be redefined to conceptualize a valid and functioning business framework. On the other hand, connectivity and accessibility are the game’s name from the passengers’ perspective. Acknowledging the necessities of the new era, the aim of this study is two-fold: (i) to highlight the essence of DT by examining the role of DT’s sub-dimensions in the civil airline industry, (ii) and propose a Digital Transformation Maturity (DTM) self-assessment tool for determining the DT maturity level of airline firms. The former is assessed by the judgments of aviation experts using Interval Type-2 Fuzzy AHP (IT2F-AHP), while the second stage was done with a survey in an airline company. According to the results, the digital strategy and the technology are highlighted as the most prominent dimensions of the proposed DTM tool. DTM evaluation of the selected airline reveals that the company is on the right path, with an overall score of 62 in its DT journey. However, some clear improvement opportunities are visible.

Ayşe Kıyıklık

Ali Osman Kuşakcı

Baboucarr Mbowe

A digital transformation maturity model for the airline industry with a self-assessment tool

航空公司数字化转型成熟度模型及自评工具

Fabián Achury-Calderón

John A. Arredondo

Leidy Catherinne Sánchez Ascanio

A novel predictive analytics model for forecasting short-term trends in equity assets prices

一种用于预测股票资产价格短期趋势的新型预测分析模型

Albert Wong

Eugene Li

Huan Le

Gurbir Bhangu

Suveer Bhatia

A predictive analytics framework for forecasting soccer match outcomes using machine learning models

使用机器学习模型预测足球比赛结果的预测分析框架

Félix O. Socorro Márquez

Giovanni E. Reyes Ortiz

Delys Y. Palacios Landaeta

Expanding strategic vision: The role of Non-Utopian Unreal Scenarios in decision-making

拓展战略视野：非乌托邦非现实情境在决策中的作用

Yuta Motoyama

Dynamic cost optimization under the cost incurring from production speed

生产速度导致的成本下的动态成本优化

Xuefeng Zhang

Yueyi Chen

Xiangqing Lu

Woraphon Yamaka

Asymmetric impacts of the world uncertainty index and exchange rates on tourism using non-linear autoregressive distributed lag models

使用非线性自回归分布滞后模型分析世界不确定性指数和汇率对旅游业的非对称影响

ISSN	2772-6622
h-index	14

Publication Information	Publisher: Elsevier Inc.，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2021，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:
Journal Fees	$1500Details
Average review cycle	10 weeks

Decision Analytics Journal

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An integrated structural equation modeling and fuzzy qualitative comparative analysis model for examining green procurement adoption drivers

A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks

A digital transformation maturity model for the airline industry with a self-assessment tool

A novel predictive analytics model for forecasting short-term trends in equity assets prices

A predictive analytics framework for forecasting soccer match outcomes using machine learning models

A novel predictive analytics model for forecasting short-term trends in equity assets prices

A predictive analytics framework for forecasting soccer match outcomes using machine learning models

Expanding strategic vision: The role of Non-Utopian Unreal Scenarios in decision-making

Dynamic cost optimization under the cost incurring from production speed

Asymmetric impacts of the world uncertainty index and exchange rates on tourism using non-linear autoregressive distributed lag models