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In the automotive industry, the high process complexity becomes an important issue because of the increased number of product and process variants demanded by the customers. To avoid quality defects in assembly and losses in such a complex manufacturing environment, new predictive support systems are required. This study aims to develop a multiple attribute decision support system (DSS) for the prediction and quantification of the risk of failures on the workstations of a leading Turkish automotive manufacturing company.

Initially, the factors affecting the failures in workstations and the attributes to evaluate the factors are identified. Subsequently, the relations among the attributes are specified and priorities of them are calculated. Finally, the risk of failures is calculated and tested in a pilot study and validated with real production data.

To the best of authors’ knowledge, this is a unique study that computes the risk scores on the workstations via DSS. The DSS has various advantages for improvements of the manufacturing quality: the risk of failures can be detectable and comparable, the effect of changes in the design of new workstations can be observed. Stations that have medium or high complexity scores demonstrated strong correlation with failure rates. A sensitivity analysis is conducted to predict the effect of improvement actions on the riskiness of the workstations.

High level of production complexity becomes a crucial issue for companies that use various production processes. Considering this fact, it is a requirement for companies to observe and monitor the risk factors, especially in the assembly lines to be able to eliminate failures derived from complexity. Accordingly, to measure risk scores of the workstations in the assembly lines, a decision support for companies aids executives to manage the complexity level in a reliable and effective way. In this study, the authors develop such a DSS for TOFAS, a leading Turkish automotive company. The proposed DSS is verified and applied through a pilot study on a specific basic production unit. A sensitivity analysis is also conducted to see the effects of potential improvements on the risk scores. Additionally, the trend of risk scores for the stations can also give valuable information for tracing the changes in the time horizon. The proposed DSS also enables an opportunity for the executives in their decision of design processes of new production lines by allocating limited resources in an appropriate way based on the risk scores of possible workstations. The proposed DSS is the first and unique proactive failure prevention model developed in a Fiat Chrysler Automobiles (FCA) plant across the world. TOFAS executives also plan to introduce and enlarge the usage of the model to other FCA plants. It may also be possible to apply the model to other assembly lines in any sector. Another plan of the executives of TOFAS is developing a software, which manages each parameter, to constitute data to the DSS to run this system more instantly and effectively. Moreover, they can take integration actions of the software with world-class manufacturing problem management system that is currently in use in TOFAS.

This paper provides a quantitative assessment of the “asset ratio” rule defined in Turkey as part of measures taken to stimulate the economy amid the Covid-19 pandemic. The main objective of the new rule was to boost credit growth in the economy and provide lending for credit-constrained households and firms that are in need. A secondary aim was to shift the denomination structure of the deposits toward domestic currency. Hence, the paper focus particularly on how the policy affected the growth rate of loans and the share of domestic deposits relative to foreign ones among the commercial banks. The policy was also heavily criticized due to the possibility that it will subjugate the banking system to excessive risk. The paper explore this possible impact by measuring how much the policy affected the default risk allowances in the banking system.

The new policy required banks with deposits above a threshold level, i.e. large banks, to maintain a certain asset ratio. Banks with deposits below the threshold, i.e. small banks, were held exempt from it. The paper implement a difference-in difference methodology to assess the quantitative impacts of the asset ratio policy by taking large banks as the treatment group, and small banks as the control group.

Difference-in-difference estimation results suggest that the asset ratio policy resulted in a 9.6% rise in loans and an 8.4% rise in government securities. Deposits also increased, with no significant change in their composition. The policy initially generated a 7% increase in the credit risk allowances of banks in the treatment group, which vanished in the following periods. Based on all these, the paper argue that the policy was successful in providing liquidity to the economy without jeopardizing the financial stability.

The findings of this study show that asset ratio policy is effective in increasing credit growth in countries with limited policy space such as Turkey. While saying this, the importance of the robust and prudent structure of the banking system in the economy should be underlined. Otherwise, the policy may have an unintended consequence of raising systemic risk. The policy suggestions also apply to advanced countries where the monetary policy has reached a natural limit due to the zero lower bound (ZLB). The ZLB problem encouraged these countries to use quantitative easing schemes in the aftermath of the Covid-19 crisis, just like the global financial crisis. However, it may take a long time to undo the effects of this policy on the balance sheets of central banks. In such cases, asset ratio policy can also be considered as an alternative tool for advanced economies notwithstanding the fact that the banking system should be prudent, well-capitalized and the country should have enough fiscal space. The main objective of the asset ratio policy was to help SMEs that were in urgent need of liquidity at the beginning of the crisis. The bank balance sheet data used in this paper does not contain information about the borrowers of the loans extended during the implementation of the policy. Analysis of this dimension using matched bank-firm level data will better demonstrate the success of the policy in achieving this goal. The paper address this as the main limitation of the paper and leave that analysis for future research.

This paper provides an important contribution to the literature by assessing a new unique policy whose objective is to stimulate loans and mitigate the impact of the Covid-19 crisis on the economy. The policy in question is predicted to have effects on the asset and liability structure and risk exposure of the banking system in Turkey. The quantitative analysis in this study estimates these impacts and discusses the effectiveness of the new policy in providing a relief for firms and households in need. Whether or not the policy caused a disruption in the sound structure of the banking system in Turkey is another question addressed in the paper.

This paper aims to propose an extended model to examine these motivations. As technology-led changes have revolutionized the marketplace, researchers and practitioners have grown keen to understand customers’ motivations for engaging in co-innovation in online communities.

The research model is based on a review of previous literature and relevant business practices.

The proposed conceptual model can be used to test empirically and explicate customers’ attitude towards engagement in co-innovation communities in the hospitality industry. Three major motivations drive customer engagement in online co-innovation communities (OCCs): brand equity, sense of community and monetary incentive. Customers’ prior experience with co-innovation projects also moderates the effects of the three motivations on customers’ attitude towards engagement in OCCs.

The proposed model highlights the importance of engaging customers through OCCs to create service innovations. These OCCs advance customers’ active participation in the firm’s co-creation and co-innovation process. Leading service firms already rely on online brand communities to stay on the cutting edge. Co-creation represents a unique, strategic partnership between the firm and the customer that can enhance both the customer experience and the firm’s innovativeness.

This study provides an initial exploration of the key components of the co-innovation of service through online communities in the hospitality industry.

This study proposes a conceptual framework for analyzing customer management strategies and their effects on Internet-based platform performance based on a review of the relevant literature, and provides directions for future research.

A literature review of relevant research articles on customer management in platform firms was conducted.

First, a framework based on the market maker view of platform firms suggests customer acquisition, customer retention and customer governance are the main customer management subprocesses toward improving platform firm performance. Second, the most studied customer management strategies for each subprocess contribute to platform performance based on the mechanisms of building customer network, developing customer network effect and managing sustainable customer networks.

This study proposes a framework that identifies customer acquisition, customer retention and customer governance as three key customer management subprocesses in platform firms. It also summarizes the most studied customer management strategies/actions for each subprocess. With this analytical framework, it identifies underexplored key issues in customer management for further research.

The current study aimed to develop and validate Mobile Information Security Awareness Scale (MISAS) based on the prototype model for measuring information security awareness and the relevant literature.

The scale was developed and validated with the participation of 562 students from four universities. The construct validity of the scale was tested through exploratory factor analysis and confirmatory factor analysis.

The reliability of the scale was tested through corrected item-total correlations and Cronbach alpha. The MISAS includes six factors and 17 items. The identified factors were labeled as backup, instant messaging and navigation, password protection, update, access permission and using others' devices.

The scale included only the human aspects of mobile information security. The technical aspects are not within the scope of this study. For this reason, future studies might develop and validate a different scale focusing on the technical aspects of mobile information security.

The developed scale contributes to the literature on the human aspects of mobile information security.