Yönetim Bilişim Sistemleri Bölümü Koleksiyonu
https://hdl.handle.net/20.500.12440/1115
2024-03-29T07:25:21Z
2024-03-29T07:25:21Z
Long distance relationship with workplace: Remote work and workplace spirituality
Demirbağ, Kübra Şimşek
https://hdl.handle.net/20.500.12440/6136
2024-01-11T09:13:39Z
2023-01-01T00:00:00Z
Long distance relationship with workplace: Remote work and workplace spirituality
Demirbağ, Kübra Şimşek
In this study, workplace spirituality is discussed in the context of remote work and the COVID-19 pandemic. First, the focus is on the changes in the meaning and function of employees in the organization during the evolutionary process of industrialization and management paradigms. Afterward, conceptual frameworks for spirituality and workplace spirituality are presented, and in the last section, academic studies that deal with workplace spirituality with distance or hybrid work arrangements are included. Early studies offer insights and recommendations on conceptualizing, developing, and managing workplace spirituality. They all emphasize that spirituality is necessary for remote work as a tool to overcome stress and mental health problems and increase employee well-being. Unfortunately, the remote work and workplace spirituality literature is relatively narrow and needs to be expanded. © 2024 Kübra iyimsek Demirbaş;. Published under exclusive licence by Emerald Publishing Limited. All rights reserved.
2023-01-01T00:00:00Z
Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers
Cam, Handan
Cam, Alper Veli
Demirel, Ugur
Ahmed, Sana
https://hdl.handle.net/20.500.12440/6133
2024-01-11T06:52:39Z
2024-01-01T00:00:00Z
Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers
Cam, Handan; Cam, Alper Veli; Demirel, Ugur; Ahmed, Sana
This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML)-based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul. Our main motivation behind this study is to apply sentiment analysis to financial-related tweets in Turkish. We import 17189 tweets posted as "#Borsaistanbul, #Bist, #Bist30, #Bist100″ on Twitter between November 7, 2022, and November 15, 2022, via a MAXQDA 2020, a qualitative data analysis program. For the lexicon-based side, we use a multilingual sentiment offered by the Orange program to label the polarities of the 17189 samples as positive, negative, and neutral labels. Neutral labels are discarded for the machine learning experiments. For the machine learning side, we select 9076 data as positive and negative to implement the classification problem with six different supervised machine learning classifiers conducted in Python 3.6 with the sklearn library. In experiments, 80 % of the selected data is used for the training phase and the rest is used for the testing and validation phase. Results of the experiments show that the Support Vector Machine and Multilayer Perceptron classifier perform better than other classifiers with 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647 respectively. Other classifiers obtain approximately a 78,5 % accuracy rate. It is possible to increase sentiment analysis accuracy with parameter optimization on a larger, cleaner, and more balanced dataset by changing the pre-processing steps. This work can be expanded in the future to develop better sentiment analysis using deep learning approaches. © 2023
2024-01-01T00:00:00Z
The Elephant in the Room: New Skills and Work Dimensions of Turkish White Goods Industry Engineers in Industry 4.0 Era
Demirbag, Kubra Simsek
Yildirim, Nihan
https://hdl.handle.net/20.500.12440/6014
2023-10-24T06:34:36Z
2023-01-01T00:00:00Z
The Elephant in the Room: New Skills and Work Dimensions of Turkish White Goods Industry Engineers in Industry 4.0 Era
Demirbag, Kubra Simsek; Yildirim, Nihan
While the emergence of the Industry 4.0 (I4.0) concept has heightened concerns about how digital transformation will affect employees, engineers who create, develop, and spread the I4.0 tools and technologies are ignored. Also, the industries with the highest digital maturity should be prioritized since they are the readiest ones for the I4.0 transformation. In this context, our focus is the engineers in the Turkish white goods industry, one of the three industries with the highest level of digital maturity in Turkey. We aim to determine the new skills expected from engineers and to uncover the impact of the perceived usage level of I4.0 technologies by engineers on the dimensions of engineering work. In this article, we conducted two separate surveys for engineers and managers and sought answers to four research questions using factor analysis and regression analysis. The results show that the new skills are divided into intrinsic motivation, technology, and data and information skills. At the same time, the perceived usage level of I4.0 technologies influences the social characteristics dimension of engineering work. We revealed that the sociability level of the engineers who use data analytics, artificial intelligence, simulation, and RFID/RTLS technologies while performing their jobs tends to decrease, while the sociability level of the engineers who use adaptive robots and additive manufacturing technologies gets higher. IEEE
2023-01-01T00:00:00Z
Getting the measure of the fourth industrial revolution: advantages and challenges of Industry 4.0 in the Turkish white goods industry
Şimşek Demirbağ, Kübra
Yıldırım, Nihan
https://hdl.handle.net/20.500.12440/5985
2023-07-20T13:14:43Z
2023-01-01T00:00:00Z
Getting the measure of the fourth industrial revolution: advantages and challenges of Industry 4.0 in the Turkish white goods industry
Şimşek Demirbağ, Kübra; Yıldırım, Nihan
Purpose: Industry 4.0 (I40) is an open window of opportunity for Turkey, a developed country, to eliminate technological dependence and produce with maximum productivity. However, I40, which corresponds to the fourth wave of industrial revolutions, brings both opportunities and challenges. In this context, this study aims to reveal the foresight of managers in the Turkish white goods industry (TWGI) regarding the advantages and challenges of I40 and compare them with the literature. Design/methodology/approach: The Delphi method was used for the study. Data were collected from managers of companies that are members of the White Goods Suppliers Association (BEYSAD). Seventy managers from 55 companies participated in the first round, and 19 managers participated in the second round of Delphi. Findings: The results show that the most frequently cited advantages are productivity/resource efficiency, data and information-enabled effectiveness/productivity, quality 4.0 and competitiveness/strategy. The most frequently mentioned challenges are financial resources/investment, employee qualification/training, technical/processual challenges and organizational transformation/leadership. Research limitations/implications: The sample was limited to the managers of the TWGI. Practical implications: Players in similar ecosystems and policymakers should consider the advantages and respond to potential challenges when creating roadmaps, taking the necessary steps and positioning themselves in the marketplace. In particular, the TWGI – Turkey’s showcase in international markets – should consider the undeniable benefits of the I40 transition to increase innovation. Originality/value: The findings for the first time highlight the advantages and challenges of I40 in an industry in Turkey, and they will benefit the TWGI, which is among the leaders in Turkey in terms of digital maturity and innovation in its journey to I40. © 2023, Kübra Şimşek Demirbağ and Nihan Yıldırım.
2023-01-01T00:00:00Z