Browsing by Author "Isler, Zulal"

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Citation - WoS: 4
Citation - Scopus: 4
Babbling Through Social Media: a Cross-Country Study Mapping Out Social Networks Using Ewom Intentions
(Springer, 2023) Zülal, İşler; Kıygı-Çallı, Meltem; El Oraiby, Maryam; Isler, Zulal
This research aims to determine the factors affecting the users’ electronic word-of-mouth (eWOM) seeking and sharing intentions and to reveal the interactions among and within clusters using social network analysis (SNA). This study includes three hierarchical sub-studies conducted in two countries, Turkey and Poland. First, we develop a segmentation for social networking site (SNS) users based on the frequency of sharing product-related information on SNSs. Second, we investigate the impact of several factors that affect eWOM seeking and sharing intentions using regression analysis. In the second sub-study, we also include the identified segments developed in the first sub-study as another factor that may have differentiated eWOM intentions. Third, to understand the degree of interaction among SNS users, we apply an SNA using the forecasted eWOM intentions scores from the second sub-study, which gives us hypothetical social networks. The results of SNA present strong interactions inter- and intra-clusters in both countries. Some key findings include the identification of three SNS user segments, including “Middlers,” that may be of particular interest to brands. We also find that in terms of eWOM intentions, users in Turkey are more active than in Poland. Although some predictors of eWOM seeking and sharing intentions differ between the two countries, users intend to be more active in eWOM seeking than in eWOM sharing. The comparative study provides valuable insights for decision-makers to engage different market segments via SNSs with various proposed features using suggested information contents for selected product categories.
Citation - WoS: 19
Citation - Scopus: 19
Optimization of Wastewater Treatment Systems for Growing Industrial Parks
(Elsevier, 2023) Savun-Hekimoglu, Basak; Isler, Zulal; Hekimoglu, Mustafa; Burak, Selmin; Karli, Deniz; Yucekaya, Ahmet; Ediger, Volkan S.
Wastewater treatment is one of the crucial functions of industrial parks as wastewater from industrial facilities usually contains toxic compounds that can cause damage to the environment. To control their environmental loads, industrial parks make investment decisions for wastewater treatment plants. For this, they need to consider technical and economic factors as well as future growth projections as substantial construction and operational costs of wastewater treatment plants have to be shared by all companies in an industrial park. In this paper, we consider the long-term capacity planning problem for wastewater treatment facilities of a stochastically growing industrial park. By explicitly modeling randomness in the arrival of new tenants and their random wastewater discharges, our model calculates the future mean and variance of wastewater flow in the industrial park. Mean and variance are used in a Mixed Integer Programming Model to optimize wastewater treatment plant selection over a long planning horizon (30 years). By fitting our first model to empirical data from an industrial park in Turkey, we find that considering the variance of wastewater load is critical for long-term planning. Also, we quantify the economic significance of lowering wastewater discharges which can be achieved by water recycling or interplant water exchange.
Citation - WoS: 2
Citation - Scopus: 3
Optimum utilization of on-demand manufacturing and laser polishing in existence of supply disruption risk
(Elsevier, 2022) Ulutan, Durul; Isler, Zulal; Kaya, Burak Erkan; Hekimoglu, Mustafa
3D printing has moved from being a rapid prototyping tool to an additive manufacturing method within the last decade. Additive manufacturing can satisfy the need in dire situations where spare parts distribution is an issue but access to a 3D printer is much more likely and rapid than access to original parts. Managing inventories of spare parts can be tackled with more ease thanks to the reduced part types with additive manufacturing. While quality (in terms of reliability) of additively manufactured spare parts in terms of mechanical properties seem to be lower than original parts (particularly due to the inherent staircase appearance and the corresponding stress concentration zones that can lead to premature fatigue failure), use of post-processing subtractive techniques to correct such surface irregularities are found to improve reliability. While each process adds another layer of complexity to the cost minimization problem, demand uncertainty and risk of supply disruption represent the modern global problems faced recently. The problem tackled in this study is the joint optimization of the supply reliability considering the effect of laser polishing parameters and the demand uncertainty. In this problem, a condition of random breakdowns of identical products is considered. Also, the original supplier of machine components is subject to exogenous disruptions, such as strikes, raw material scarcity, or the COVID-19 pandemic. As a result, the optimum control policy with the right cost parameters was shown via numerical experiments originated from mathematical analyses. This optimality can be critical in managing the system in the best possible way, particularly during times of unforeseen circumstances such as pandemics. (C) 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.