scholarly journals Tracing the progress of scenario research in business and management

2021 ◽  
Author(s):  
Arbrie Jashari ◽  
Victor Tiberius ◽  
Marina Dabić
Keyword(s):  
Business And Management

Managerial Implications of Student Activity Information System Implementation at Faculty of Business and Management

2020 ◽  
Vol 5 (1) ◽  
pp. 41
Author(s):  
Norshima Humadi ◽  
Muhamad Sukor Jaafar ◽  
Melissa Shahrom ◽  
Siti Halijjah Shariff
Keyword(s):  
Information System ◽  
Service Quality ◽  
Student Satisfaction ◽  
Direct Effect ◽  
Quality Model ◽  
Application Process ◽  
Student Activity ◽  
Managerial Implications ◽  
Business And Management ◽  
Activity Information

Faculty of Business and Management – Student Activity Information System (FBM-SAIS) was developed with an aim to manage the student activity application process effectively through the Internet. This study was conducted to determine the managerial implications of FBM-SAIS implementation to FBM, UiTM Selangor by focusing on the manpower and financial impact, as well as to determine the direct effect of SAIS Service Quality on SAIS student satisfaction. This study proposed SAIS ServiceQuality as a higher-order factor in order to determine a direct effect of SAIS Service Quality on student satisfaction towards SAIS implementation. Interviews were conducted to identify the managerial implications of student activity application process before and after SAIS implementation. Meanwhile, the quantitative data was gathered from 94 SAIS users who were FBM students through e-survey and was analyzed by using SmartPLS 3.0. The interview results showed that the implementation of SAIS did have an impact on the Faculty, such as increasing staff productivity and reducing costs. Moreover, the PLS-SEM analysis results showed that SAIS Service Quality positively influenced student satisfaction towards FBMSAIS implementation. This study provides an empirical validation of the SAIS Service Quality Model in the context of Higher Education.

An academic Indian summer

2017 ◽  
Author(s):  
Angela Penrose
Keyword(s):  
Oil Industry ◽  
Management Theory ◽  
Resource Based View ◽  
Oil Companies ◽  
International Firm ◽  
Younger Generation ◽  
National Governments ◽  
Business And Management

After her husband’s death in 1984 and retirement from INSEAD Edith enjoyed the resurgence of interest in her work and its increasing influence on aspects of economic, business, and management theory and on a younger generation of economists, many of whom visited her at her home near Cambridge. The chapter examines the influence of her seminal ideas on some key protagonists of the ‘resource-based view of the firm’, e.g. David Teece, Birger Wernerfelt, J. C. Spender, and Jay Barney. Due to her understanding of the international firm, in particular the oil industry, she undertook consultancies pertaining to arbitration between oil companies and national governments.

E-Business and Management Science: Mutual Impacts (Part 1 of 2)

2003 ◽  
Vol 49 (10) ◽  
pp. 1275-1286 ◽  
Author(s):  
Arthur M. Geoffrion ◽  
Ramayya Krishnan
Keyword(s):  
Management Science ◽  
Business And Management

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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