The Qualitative Comparative Analysis: An Overview of a Causal Complexity Approach

Research background: Alongside with the development of configurative comparative analysis aiming at identification of necessary and sufficient conditions, various formal methods used for this purpose have been formulated during the last decades. One of them is qualitative comparative analysis (QCA), one of approaches used for causal explanation of phenomena of cases performed in the field of international economics and global affairs.
Purpose of the article: The main purpose of the article is to provide a detailed overview of the QCA method in global context, to define its methodologic foundations and consequently introduce the key concepts of the method. The article also provides a comparison of QCA to typical tools of qualitative and quantitative approaches. On the basis of this part, both pros and cons of QCA are derived.
Methods: Basically, the methods of analysis, deduction and comparison are used to fulfil the purpose of the article. The existing and available papers and books coping with the topic of QCA and its position among other research methods are reviewed to provide an overview on the selected method.
Findings & Value added: The QCA is a method based on analysing stated relations. It bridges the quantitative and qualitative research and reveals certain patterns based on causal complexity principles, however, it is done regarding heterogeneity and diversity of individual researched cases. It is a method applicable to the middle number of cases, it means too few cases for statistical methods on the other hand too many cases for typical qualitative approaches.

Introduction
Alongside with the innovative ideas on qualitative methodology in late 1960s, configurative comparative approach came into existence and lately the method of qualitative comparative analysis (QCA) was introduced by Charles Ragin in 1980s. Formally it was developed as a research method mainly for purposes of comparative politics and social sciences [1]. However, during the time, especially in the last decade, the method was formalized into an analytic approach which is nowadays commonly used by many researchers and scientists from various fields and academic areas, e. g. business research, sociology as well as economics [2].
As a configurative method, the QCA uses formalization procedures as well as it relies on Boolean logic, for this reason, it is not a typical qualitative nor quantitative method [3]. It is ranked among so called theory-based approaches whose main aim is to evaluate varied combinations of explanatory factors (conditions) explaining presence, or on the contrary absence, of a certain phenomenon (outcome). That is why, it is extremely popular in disciplines dealing with quantitative data providing in-depth information.
Due to increasing global interest in this case-based method, an overview of its basic foundations and concepts is performed. Being familiar with the main principles of each research method, including the QCA, is a key step to perform analysis in an appropriate way. The methodologic foundations of QCA are summed up in the following chapter of the paper. Moreover, it is crucial to embed the qualitative comparative analysis into generally known sorting of qualitative and quantitative research methods to understand its position among these methods. The third chapter deals with this issue and it also derives pros and cons of using QCA.

Methodologic foundations of QCA
To use this method, three basic conditions need to be fulfilled. Firstly, there must be an indication of possibility that the researched phenomenon can be evaluated in the most appropriate way by complex causal context and using the QCA procedure. Secondly, it is necessary to coordinate theory and methodology, so if the QCA comes from the theory of sets, also the research theory should come up from sets [4]. The last condition is associated with the sample, which should be of middle size comprising from 10 to 50 individual cases.
Contrasting the statistical methods, the individual cases are understood in a holistic way, for this reason the emphasis is put on individual causal conditions forming together the whole system. This fact corresponds to principles of causal complexity, allowing different conditions represented by independent variables lead to the same outcomes, referring dependent variables [5]. Theory of causal complexity contrasts causal homogeneity, a situation when a dependent variable is explained entirely by an independent variable.
The QCA method can cover the causal complexity principles because of three mutually connected concepts as follows: causal asymmetry, conjunctural causation and lastly equifinality [6]. Causal symmetry highlights the fact that despite a combination of certain conditions leads to a certain outcome, the absence of the same combination will not automatically result in outcome absence. For example, diminishing trade barriers can result in higher globalization level, however, keeping some trade barriers does not guarantee lower globalization level.
Conjunctural causation is based on the fact, that each condition influences the outcome only if it is combined with one or more different conditions. To simplify this statement, it is more probable, that a combination of conditions leads to a certain outcome. Taking into consideration the previously mentioned example, the higher level of globalization is SHS Web of Conferences 9 2, 0 (2021) Globalization and its Socio-Economic Consequences 2020 8020 https://doi.org/10.1051/shsconf/20219208020 produced by combination of conditions, including diminishing trade barriers, increasing foreign direct investments, taking part in developing aid and many others. The concept of conjunctural causation is supplemented by equifinality outlining that various conditions, alternatively their combinations, can result in identical final outcome. Basically, many ways how to reach higher globalization levels can be found with or without existence of trade barriers.
As it was already mentioned before, the QCA method does not aim at establishing particular effects of a chosen variable. This method points at finding logical connection among causal conditions, or their combinations. This process is based on relations and logic of sufficiency and necessity. Therefore, researchers desire to answer via QCA questions aiming at finding out what conditions, or their combinations, are necessary or sufficient to generate an outcome.
In the scope of qualitative comparative studies, the sufficient and necessary conditions are defined using the three above mentioned concepts. A causation is defined as a necessary one, if it must be present for an outcome to occur. On the contrary, sufficient causation arrives at the moment when it can produce a certain outcome itself. On the basis of this, the necessary condition is defined as a situation when a certain outcome is present, consequently a necessary condition is present as well [7]. However, at the moment when a certain outcome does not occur, the necessary outcome can or cannot be present which is derived from the application of causal asymmetry.
If a sufficient condition is present, a certain outcome is present. this is the way, how the concept of sufficient condition is defined. In the case of its absence, the outcome can or cannot be present which is grounded in the assumption of equifinality. All the mentioned relations in this part are graphically illustrated for better understanding in Fig. 1.

QCA within qualitative and quantitative research methods
Charles Ragin, the founder of the QCA, and his followers managed to formulate an approach addressing limitations of both qualitative and quantitative research methods, while keeping their strong features. Thanks to it, the QCA offers a unique possibility to analyse complex relations between conditions and an outcome.
The previous chapter shows that the QCA has its foundations in set-theory and formal logic, contrasting to qualitative research based on induction and deduction or quantitative research typically grounded in regression and correlation. Qualitatively orientated methods are also case-oriented, like the QCA, however, they deal with a single case or a few ones [8]. On the contrary, the quantitative researches cope with so called large-N, meaning large sample sizes, but they are rather variable orientated. So QCA approach deals with social phenomena that used to be unresearched because of methodological gap in the past. Nowadays, QCA offers a possibility to explore also these issues.
One value added of QCA is the fact that it enables to conduct a cross-case comparison systematically and to find out complex causality in medium-N case studies. The qualitative methods using only a few cases cannot generate such a possibility of comparison among individual cases. The QCA preserves cases as holistic units through the analysis, thanks to which it is able to generate complex explanations of a certain phenomenon, contrasting variable orientated statistical methods [9]. Therefore, the sensitivity to complex causal relations is another advantage of QCA method contrasting statistical and single caseoriented methods.
Moreover, through this method both numeric and nonnumeric data can be processed, as this method commonly allows data transformation. Relationships between explanatory variables and outcomes can be derived on the basis of numeric, nonnumeric data or their combination. Concerning qualitative data, coding of variables can become an obstacle, as some researchers can tend to use simplified threshold values for them. As a consequence, results can be skewed and not revealing the right causal conditions impacting the outcome.
As the QCA has a different goal and orientation, sometimes its results can be generalized with limitation or cannot be generalized at all. This can be seen as a drawback of this , which is shared with the majority of qualitative research methods. The limited ability to generalize is usually depending on the way how the analysed cases are selected and on the extent to which they represent wider sample.
Besides problems associated with variable coding, QCA is criticized by experts because of instability of causal predictions based on its results [10]. Adding a single case containing extremely different causal conditions than the rest of sample can significantly change the results of QCA, as a consequence of changes in sufficient conditions. However, Ragin himself highlights than QCA application is not sufficient and that it should be accompanied by conscientiously interpreted results [3] on the basis of deep knowledge of the researched problematics. In addition, he makes suggestion to supplement QCA method with other qualitative procedures to come up with robust causal findings.

Basic concepts of QCA
Originally, the first version of QCA was based on crisp-set relations, so it was called crispset QCA (csQCA). Generally, each case can be attributed two values in csQCA. The values can be either 1 or 0, 1 defining membership in the set and 0 referring to non-membership [11]. This means that a certain condition or their combination is either able to result in a certain outcome or is not. Although this technique simplifies complex configurations using principles of Boolean algebra, the reality is more complex. Conditions and outcomes in real world are not always dichotomous, a large variety of sets between full membership and full SHS Web of Conferences 9 2, 0 (2021) Globalization and its Socio-Economic Consequences 2020 8020 https://doi.org/10.1051/shsconf/20219208020 non-membership exists. In fact, results of csQCA could be practically applied with significant limitations. Forthis reason, Ragin deepened his theory to so called fuzzy-set qualitative comparative analysis (fsQCA). FsQCA enables researchers to classify cases, in which conditions vary to some extent. The decimal values on a scale between 0 and 1 can be used to reflect various degrees of inclusion in a set. In this concept, the values of 0 and 1 can be called as extreme ones reflecting situations of "fully out", or "fully in", a set [12]. Value of 0.5 is usually used as the cross-over point, a threshold [1,7], establishing either membership or non-membership in a set. Fuzzy sets can be continuous (0, 0.2, 0.3 and so on) or defined by anchor points (e. g. 0, 0.33, 0.66, 1 in case of anchor fuzzy set with four thresholds). The choice of fuzzy sets depends on theory and it should correspond to differences in case. So if any difference can be distinguished between membership of 0.6 and 0.7, continuous scoring is not the best option and an anchor fuzzy set with four threshold points can be used to characterize the data. Generally, using the continuous values helps to maintain data more detailed, on the other hand, their interpretation is more complicated contrasting csQCA.
Some new QCA forms are appearing to tackle the main cons of fsQCA, however, they are not so popular yet. One of examples can be multi-value QCA (mvQCA) through which multilevel conditions can be analysed and consequently additional aspect of causal complexity can be captured. Recently, QCA techniques covering also time component have emerged. However, fsQCA remains the most commonly used concept of this method. This fact can be derived even from the number of published papers using QCA [13]. Therefore, the next section of the paper is devoted directly to performing of fsQCA.

Managing fsQCA analysis
First of all, a dependent variable and independent variables, explaining the dependent one, must be set. In this context, it is important to point out that as QCA deals with middle number of cases, the number of identified explaining variables cannot be too vast. There should be from four to six explaining variables depending on number of selected cases [14]. Consequently, data concerning a field of researcher's interest is collected, then the data matrix is created. Such a matrix represents the main input for fsQCA application. The raw data need to be calibrated to create a main input for fsQCA application. It means they should be given specific values ranging from 0 to 1 based on theoretical and empirical knowledge. The calibration process of qualitative data is not frequently discussed in literature and in some papers, it lacks transparency. The big challenge for researchers is to calibrate semantic concepts, whose straightforward calibration is not possible. Such a type of information should be calibrated systematically and in a transparent way. However, there are not yet any guidelines for this procedure.
The most common option of dealing with this issue is defining a calibration threshold and decision rules [1,7], which define values being still in a set or yet out of the set. As a result of calibration, data is scaled into set memberships, where values closer to 1 mean "rather in a set", on the other hand, values closer to 0 represent "rather out of a set".
Subsequently, the necessity of a single condition for the outcome must be tested. If a necessary condition exists, it means it is necessary for the presence or absence of outcome, such a condition is excluded from the following analysis. To find out an existence of such a condition, the parameter of consistency needs to be defined [15]. It serves for the same reasons as p-value in regression analysis, so it says to which extent the cases share a certain condition in the outcome. Low consistency of a configuration means that it is not supported by empirical evidence. According to Ragin [1], the threshold value of consistency should be set at 0.9 to assure an acceptable robustness of results. In compliance with qualitative research, the causes of an outcome are understood as combinations of necessary and SHS Web of Conferences 9 2, 0 (2021) Globalization and its Socio-Economic Consequences 2020 8020 https://doi.org/10.1051/shsconf/20219208020 sufficient conditions, that is why, the sufficiency of single condition or their combination for the outcome is analysed as well [16]. All the possible configurations of conditions are transformed into a truth table, in which columns are represented by individual variables, rows stand for individual configurations [17]. Maximum 2k (k stands for number of conditions) can be present in a truth table [18]. Truth tables can be also displayed by Venn diagrams showing all the possible relations between individual conditions. According to the set consistency threshold, the scores are coded, scores below the consistency threshold are coded by 0 illustrating their inconsistency, scores above this value are coded by 1. Moreover, a frequency threshold should be stated [15] for configurations to be included in the analysis. Frequency threshold refers to number of cases for which a configuration is valid [1,19]. As fsQCA usually deals with middle-N, this parameter is typically set at one case, if more cases are analysed it is regulated to the value of two.
The configurations in truth tables are further minimized on the basis of Boolean logic. If two configurations lead to the identical outcome and they differ only in one condition, such a condition can be viewed as irrelevant and is excluded [18]. This process is repeated till all the irrelevant conditions are removed away. Afterwards, the highest possible reduction of conditions is reached and makes the results easier to interpret [19]. Lastly, a post-QCA analysis is carried out to interpret the results in the scope of empirical and theoretical knowledge. All the research is evaluated including the practical meaning of found combinations.

Conclusion
The paper aims at QCA as one of configurative comparative analysis methods. The basic methodological foundations of QCA are provided to give an overview on this causal complexity approach. It follows that the QCA method is rooted in principles of causal asymmetry, conjunctural causation and equifinality. Thanks to them, the analysed cases are considered in a holistic way with the aim to find out which conditions, or their combinations, resulting in a required outcome.
These relations between conditions are evaluated on the basis of logic of necessary and sufficient conditions. Firstly, necessity conditions are tested and alternatively excluded, afterwards, testing of necessity conditions follow. Then all the possible configurations of conditions are displayed in a truth table. Such configurations need to minimalized using Boolean logic to get the highest reduction of conditions allowing easier interpretation of results.
In fact, all the concepts of QCA, including crisp-set and fuzzy-set QCA, bridges quantitative and qualitative research. The QCA method deals with middle sample size, both numerical and nonnumerical data can be used as inputs for this method. Compared to typical qualitative and quantitative methods, QCA generates complex explanations of a certain phenomenon. Obviously, using of QCA has also some limitations, especially in the lack of possible generalization or instability of causal predictions based on QCA.


Supplier selection and order allocation: a literature review

The goals of procurement managers in every industry usually are acquiring the right materials at the right time, at the right prices and quantities. To achieve these goals, the best suppliers should be selected. Supplier selection and order allocation have been studied extensively in the past. In this paper, we review the peer-reviewed journal publications in this area. The taxonomy in this research includes problem domain and operations research techniques. The problem domain is examined in three subcategories including Literature Reviews (LR), Deterministic Optimization (DO) models, and Uncertain Optimization (UO) models. Then, observations, recommendations, and future research directions in the field of supplier selection and order allocation are discussed.

Introduction
Purchasing and procurements are important activities in every organization. Supplier Selection and Order Allocation (SSOA) are prominent elements of purchasing and procurement . Both qualitative and quantitative factors such as quality, cost, and delivery time should be considered in the supplier selection problem . Therefore, supplier selection is a Multi-Criteria Decision-Making (MCDM) problem (Cheraghalipour and Farsad 2018;Moheb-Alizadeh and Handfield 2019).
Cost saving and minimization of risks can be achieved by using suitable supplier selection methods Arabsheybani et al. 2018). Some authors have combined supplier selection and order allocation together to solve these two problems simultaneously (e.g., Babbar and Amin 2018). Supplier selection and order allocation are very important in green supply chain management considering sustainability and environmental factors . Sustainable supplier selection encompasses cost, environmental, and social criteria and supplier's performance history Ghadimi et al. 2018).
Supplier selection is a strategic process in organizations, and plays a critical role in the success of them . Offering quantity discount is an important feature in the selection of the best suppliers. Therefore, each company can achieve low cost while allocating large volume orders to the suppliers .
In this paper, the related peer-reviewed journal papers have been found and reviewed via search in globally recognised databases such as ScienceDirect (Elsevier), Taylor and Francis, and Google Scholar. The main keyword is "supplier selection and order allocation" which is used to search related papers published between 2015 and 2020. As a result, 92 articles are analysed. The majority of other literature review papers in this field just have focused on supplier selection, and order allocation has been ignored. In addition, they have been written some years ago, and there is a need to have an updated literature review paper about supplier selection and order allocation. The structure of our paper is new among those literature review papers. Considering uncertainty in the reviewed papers and reviewing the applied operations research techniques are other main characteristics of our paper.
The other parts of this paper are as follows. The taxonomy and classification of the literature are provided in Section 2. Then, some observations and discussions are mentioned in Section 3. In addition, the related conclusions and future research avenues are provided in Section 4.

Taxonomy
Two dimensions are utilized in this review paper to categorize the papers. The first one is the problem domain. Besides, the second one is the operations research (optimization) methods. This classification is useful to analyze the supplier selection and order allocation problem based on both conceptual and mathematical viewpoints.

Problem domain
The problem domain comprises three subsections: Literature Reviews (LR), Deterministic Optimization (DO) models, and Uncertain Optimization (UO) models. Table 1 includes the related papers.

Literature reviews
Some authors have published literature review papers in the field of supplier selection and order allocation. Govindan et al. (2015) reviewed green purchasing and green supplier selection process of some articles published between 1997 and 2011. They found that Analytic Hierarchy Process (AHP) is the most popular MCDM method for assessing green suppliers. In addition, Fuzzy AHP is very popular in the environmental management systems. Yildiz and Yayla (2015) reviewed 91 articles that have been published between 2001 and 2014 about supplier selection. They stated that quality and cost are the most significant criteria in the supplier selection problem. Wetzstein et al. (2016) reviewed several papers in the supplier selection field published between 1990 and 2015. They mentioned that there are future research avenues in considering green and sustainable factors. Karsak and Dursun (2016) reviewed 149 articles published between 2001 and 2013 concentrating nondeterministic analytical methods (i.e., stochastic/fuzzy) under imprecise data. Simić et al. (2017) examined the last 50 years (50th anniversary of fuzzy sets theory established by Lotfiali Askar Zadeh in 1965) of articles in supplier selection and evaluation that are based on fuzzy sets theory, fuzzy models, and fuzzy hybridization. The authors combined individual and integrated approaches to effectively review the fuzzy supplier selection methods. The authors selected 54 papers published in the reputable journals. Alkahtani and Kaid (2018) studied some journal papers published between 1995 and 2018 focusing on supplier selection. They provided information about the trends, the research gaps, and the selection criteria in the supplier selection field. Ocampo et al. (2018) reviewed 240 articles from peer-reviewed journals published between 2006 and 2016 focusing on the applications of different approaches for supplier selection and evaluation which include individual and hybrid methods. The authors indicated that the novel methods in the literature include uncertainty, risk analysis, and sustainability factors. Aouadni et al. (2019) reviewed 270 articles published between 2000 and 2017 about supplier selection and order allocation. In their paper, about 17 %, 9 %, and 7 % of the reviewed papers were about AHP, TOPSIS, and ANP methods, respectively. They mentioned that   Wong (2020) fuzzy multiple-objective programming is a popular method in this area. In addition, some papers have used genetic algorithm to determine the orders. Chai and Ngai (2019) reviewed SSOA papers published among 2013 and 2018. They brought to light that MCDM methods and optimization are the most popular techniques for supplier selection and order allocation.

Deterministic optimization models
In this part, deterministic optimization methods for supplier selection and order allocation are discussed. We provide some information about the publications that have received several citations. Other publications are written and mentioned in the following table. Sodenkamp et al. (2016) used a novel approach (trade-off mechanism) because the current multi-objective methods were not capable to create positive and negative performance synergies. Bohner and Minner (2017) disscussed a mixed-integer linear programming model for solving the intricate issue of supplier selection by having a backup supplier who is not cost effective. However, it minimizes the risk related to the stock out condition. Nourmohamadi  studied purchasing decision-making through a TOPSIS method and a multi-choice goal programming model. In addition, they developed rough set theory and grey system. Moheb-Alizadeh and Handfield (2019) constructed a multiobjective optimization model for a manufacturer of automobile transmission systems for order allocation considering minimization of the CO 2 emissions. Nazeri et al. (2019) proposed a multi-objective model to test effective ranking in military's SSOA for outsourcing of hazardous materials. Wang et al. (2020) implemented carbon emission trading schemes using analytic network process-integer programming model and stipulated approach. They minimized cost and carbon emissions. The deterministic optimization models of supplier selection and order allocation are classified in Table 2 according to the multiple elements (sets) including parts, products, periods, suppliers, and scenarios. In some papers, it has been assumed that the parts can be assembled to make a product. Products usually represent the final products that can be sold in the markets. In addition, some papers have considered different periods such as months in their mathematical models. Besides, multiple potential suppliers have been considered by some authors. Furthermore, some authors have assumed different scenarios to analyze the problem under uncertainty.

Uncertain optimization models
In this part, we mention some important publications that have developed uncertain optimization models and have received several citations. Other papers are mentioned in the following two tables.
The paper of Azadnia et al. (2015) has been cited by more than 200 papers in Google Scholar. In this article, the authors introduced sustainable supplier selection by adding occupational health and safety management system. Those subcriteria are important components in sustainable supplier criteria. The authors utilized a fuzzy AHP and a rule based weighted fuzzy approach. They selected a sustainable supplier using a multi-product lot sizing order model. Çebi and Otay (2016) proposed a two-stage fuzzy method including fuzzy MULTIMOORA and fuzzy goal programming for the supplier selection and order allocation problem. They considered green supplier selection in the beverage industry. Govindan and Sivakumar (2016) studied the selection of the best supplier by minimizing the greenhouse gas emissions using a fuzzy TOPSIS and a multi-objective method. They determined the ranks of the green suppliers, and they classified the potential suppliers. Pazhani et al. (2016) proposed a mathematical model to find the optimal inventory level and showed that cost could be minimized if the transportation cost is considered in the objective. They also discussed the benefits of the integrated inventory management system approach comparing the sequential approach for solving the supplier selection problem. Ghorabaee et al. (2017) stated that a novel EDAS technique and interval type-2 fuzzy sets lead to a good multi-criteria green supplier selection model. Hamdan and Cheaitou   (2018) proposed a novel two-stage QFD model and an optimization model for SSOA. They solved the optimization model using GAMS software. Their method can handle the vagueness and uncertainty considering qualitative and quantitative criteria. Ahmadi and Amin (2019) introduced the supplier selection and order allocation in closed-loop network of cellular phone industry in Toronto Canada. They developed a fuzzy based solution approach using IBM ILOG CPLEX 12.7.1.0 software.  developed a bi-objective mixed-integer programming model (stochastic) for the SSOA problem. They illustrated the application of the model in the automotive industry.  introduced F-AHP and F-PROMETHEE to study uncertainty in the decision-making environment. Govindan et al. (2020) described sustainability through incorporating circular supplier selection and order allocation. They combined all activities such as waste reduction in transportation. Hasan et al. (2020) developed a Decision Support System (DSS) for companies operating under logistics industry 4.0. Jia et al. (2020) developed a robust optimization goal programming model for a steel company. They solved the problem by CPLEX, and optimized it considering the total cost, CO 2 emission, and environmental objectives. Kaur and Singh (2020) proposed a model with consideration of risks and disruption (both natural and man made), suitable for industry 4.0 environment. The uncertain optimization models are categorized in Table 3 according to the multiple elements. In addition, the uncertainty sources are shown in Table 4; Fig. 1.
Demand, capacity, and cost are the major sources of uncertainty in the reviewed papers.

Operations research techniques
We divide the references according to the operations research (optimization) techniques in Table 5. Several authors have utilized hybrid techniques in this field. Table 6 includes the types of the objective functions in different papers. In addition, single objective and multi-objective functions are illustrated in Fig. 2.

Observations and related recommendations
In this part, observations and recommendations according to the reviewed papers are provided.

The most popular domain
In this paper, we discussed three domains of supplier selection and order allocation including literature reviews, deterministic optimization models, and uncertain optimization models. The most popular domain is the uncertain optimization models (73 % of the papers). Deterministic optimization models (17 % of the papers), and Literature reviews (10 % of the papers) domains have the next ranks.

The most popular uncertainty source
According to Table 4; Fig. 1, the most popular sources of uncertainty are demand, capacity, and cost, respectively. Among them, demand has been considered more than the other factors (29 % of the papers). This parameter usually   affects the order allocation significantly, and it is considered as one of the constraints of the optimization models.

The most popular technique
Based on the information in Table 5, Fuzzy TOPSIS, Fuzzymulti objective programming, Stochastic programming, and Mixed-integer linear programming are the most popular techniques in the literature of SSOA. Fuzzy TOPSIS is a useful technique to determine the weights (importance) of the suppliers. Fuzzy sets theory enables researchers to consider uncertainty in the parameters. Fuzzy multi-objective programming considers uncertainty and the effects of some objectives on the problem. There are several stochastic programing models in the literature which are based on the probabilities in the SSOA problem. Mixed-integer linear programming also have been utilized in the literature because it can handle both non-negative and 0-1 variables.

The most popular multi-objective method
There are several techniques for solving multi-objective problems. Based on our observation, weighted-sums method is the most popular one. Several authors also have utilized goal programming and ɛ-constraints method to solve multiobjective SSOA problems.

The most popular applications
The applications of the models have been categorized in Table 7. Several authors have considered case studies. "Automotive industry" is a popular application in the supplier selection and order allocation field. Table 8 includes the information related to the names of the journals. These journals have published papers related to SSOA. "Journal of Cleaner Production", "Computers & Industrial Engineering", "International Journal of Production Research", "International Journal of Production Economics", and "Expert Systems with Applications" have published several papers in this field. Table 9 includes the classification of the papers based on year and the mentioned three domains. The journal papers from 2015 to 2020 have been examined in this research. It is noticeable that    Arabzad et al. (2015) Max total stake-holder satisfaction score Scott et al. (2015) Min total cost Bohner and Minner (2017) Max total profit Cui et al. (2015) Min outsourcing cost Fu et al. (2016) Max total score Beauchamp et al. (2015) Max over all performance of the system Hu et al. (2018) Min deviation (cost, CO 2, emission, society and supplier's value) Jia et al. (2020) Min cost Esmaeili-Najafabadi et al. (2019), Jain et al. (2015), Kuo et al. (2015), Meena and Sarmah (2016), Mohammaditabar and Ghodsypour (2016), Pazhani et al. (2016), Torabi et al. (2015), Wang et al. (2020) Max conditional service at risk Sawik (2016) Max efficiency of the proposed system Shadkam and Bijari (2017)  (4) Min cost, late delivery, rate of defects, Max total utility of the system Çebi and Otay (2016) (2) Min cost, Max total score Cheraghalipour and Farsad (2018) (2) Max efficiency of supplier, Max order quantity Dotoli et al. (2015) (2) Min total cost, Max green value Duan et al. (2019) (3) Min total cost, carbon emission, Max total purchasing value Feng and Gong (2020) (2) Min cost, Max supplier's performance Ghadimi et al. (2018) (3) Max positive score of supplied material, Min negative score of supplied material and cost Ghorabaee et al. (2017) (2) Min total cost, Max total value purchasing Bektur (2020),  (5) Min total cost, total quality rejection, late delivery, waste, total carbon emission Govindan and Sivakumar (2016) (4) Min cost, defective items, delay in delivery, Max vendor performance Gupta et al. (2016) (4) Min price, delay, Max coverage of customer's suppliers, supplier evaluation more realistic Hajikhani et al. (2018) (2) Max total preference, Min total cost  (2) Max total performance, Min total cost Kaur and Singh (2020); Hamdan and Cheaitou (2017a, b) (3) Max green purchasing, Min cost, Min defects Hamdan and Jarndal (2017) (2) Max profit, Min loss Hamdi et al. (2016) our paper has been written in 2020. Therefore, the number of the published journal papers in 2020 are limited in Table 9.

Conclusions
In this research, three problem domains including literature reviews, deterministic optimization models, and uncertain optimization models have been considered, and the related papers have been gathered and analyzed. In addition, these papers (92 publications between 2015 and 2020) have been classified according to the operations research methods. Furthermore, observations have been provided and discussed. We have observed that most of the mathematical models in SSOA belong to the uncertain optimization models category. Supplier selection and order allocation methods may create competitive advantages for companies, and at the same time, poor selection of the suppliers may result in the failure of the companies. The basic criteria for supplier selection include cost, quality, and time. Recently, more green and environmental factors such as minimization of carbon emissions have been considered in the SSOA process. There are numerous directions for future research in the SSOA problem. Some of them are as follow: (i) Usually a few sources of uncertainty have been considered in the optimization models of order It is useful to consider several sources of uncertainty simultaneously using advanced methods such as robust optimization. (ii) Most of the SSOA papers have focused on manufacturing systems such as automobile It is valuable to consider SSOA in service industry such as healthcare systems (e.g., hospitals). (iii) Fuzzy sets theory and fuzzy logic have been combined with other techniques to handle However, there are some practical challenges in applying these methods in  More case studies can be considered in this area to show and discuss the applications of these methods. (iv) Under special circumstances such as COVID-19, the normal supplier selection and order allocation may not lead to excellent Developing new methods for these situations can be an avenue of future research. (v) There are several parameters such as cost, capacity, and demand in the optimization models of the order These parameters can be estimated using advanced forecasting techniques such as machine learning, deep learning, and neural To our knowledge, this area of research is new, and has not been explored in the SSOA papers.


The Effect of Reciprocity on Mobile Wallet Intention

The authors build on the literature on reciprocity by exploring a prevalent social and economic phenomenon in the Philippines where individuals with positive mobile phone balances can SMS loads to acquaintances. This practice is known as “pasaload”—an abbreviation of Pass-A-Load. The research was designed based on the literature review that in turn resulted in a research model that focused on six constructs: reciprocity, loyalty, habit, switching costs, trust, and future repurchase. Hypotheses were developed as the basis for a scaled survey of 1050 Philippine smartphone users, with the questions adapted from but adhering closely to the original questions from appropriate articles in the literature review. Overall the mediating effects from the two models are consistent with the expectations from the literature and analysis. In the Philippine context, habit might be a strong mediator even if the true financial and convenience costs to switch is low.

INTRodUCTIoN
This paper focuses on three questions. First, to what extent do the social constructs of reciprocity and trust affect Philippine telecommunication product continuance? While reciprocity has been studied extensively in the context of electronic communities and social media, it has not figured in technology use or acceptance, or in purchase intention. If the TAM (Davis et al. 1989) and UTAUT (Venkatesh et al. 2003) models explain intention from a usefulness perspective, we attempt to build on earlier literature on habit and product loyalty. Second, how do Philippine cultural values of bayanihan, or kinship, and utang na loob, or "debt of gratitude" reinforce the use of a popular Telecommunication mobile wallet application called "pasaload?" Third, can reciprocity and trust enhance the effects of customer switching costs, loyalty, and habit on their intent to repurchase?

Background
In 2014 the Philippines' population of 100 Million people had 1.16 SIM cards per capita: many Filipinos used multiple SIM cards or had more than one mobile phone. Moreover, 96% of the country's SIM cards were prepaid (Waring 2014). The Philippine telecommunications industry is effectively a duopoly, comprising two main players: SMART and Globe, and a few minor other telecommunications trust, behavioral trust, propensity to trust, and trustworthiness. Trust is difficult to define and is multidimensional (Rousseau et al. 1998). In the IT literature trust is present in IT and individual, groups, markets, and virtual collaboration (Chiu et al. 2006;Sidorova et al. 2008). In the e-commerce market literature in particular, trust is a belief in an attribute of a trustee, in most cases, web or e-commerce commercial site (McKnight et al. 2002).
Trust exists between buyer (truster) and seller (trustee) e.g. McKnight et al. (2002) research on initial trust for web vendors. Trust can be measured by the trustee's levels of ability, benevolence, and integrity. Ability can mean the trustee's reputation (Josang et al. 2007) Integrity itself is the trustee's ability to meet obligations, fulfill agreements, and maintain reliability (Bhattacherjee, 2002). Beyond initial trust, subsequent interactions may reinforce or diminish the strength of trust. Continued social interaction leads to enhanced ties, or trust (Granovetter 1973). Eventually after such repetition and social capital build up, trust for each other remains (Bigley & Pierce 1998). It is defined as the expectation or belief that persons will keep their promises and fulfill obligations with honesty, goodwill, and non-opportunistic motives (Tian et al. 2011).

Switching Costs
Consumers incur switching costs when they transition between products. To own a smartphone includes not only new product and breaking contract fees, time, and effort, but also psychological costs such as learning curve and uncertainty when switching to a new provider. Switching may occur when consumers are dissatisfied with their current choice and are aware of potential replacements (Battacherjee et al. 2001). Maintaining high switching costs has thus been a critical management strategy to keep consumers loyal. In the technology context the more effective information systems can increase switching costs of users, thus deterring churn and defection (Shapiro & Varian 1998). This is reflected in the lock-in contracts companies give in exchange of free handsets in most countries. To increase consumer loyalty, telecommunication firms might also push consumer loyalty programs. If a consumer switches, losing those loyalty benefits adds extra switching costs. Lam et al. (2004) argued part of switching cost may involve loyalty benefits consumers give up when their relationship with the service provider ends (Lam et al. 2004). Past research found a strong relationship between switching costs and loyalty (Amoroso & Chen 2017). For instance, given the fierce competition among mobile financial apps in China, financial mobile apps, especially leading ones, commonly use the same strategy of giving coupons, discounts, points to their loyal consumers. Such programs increase consumers' switching costs.

Loyalty
Loyalty is defined by Lin et al. (2015) as consumer dependence and goodwill toward a product or service, as a result of culminating in consumer satisfaction. Loyalty is a strong commitment behavior toward a vendor or brand; and a loyal consumer not only continuously comes back to the same vendor or brand but also helps the vendor/brand sustain long-term growth through advocacy. Furthermore, a vendor/brand can also gain from cross-/up-selling opportunities, forecast accuracy, receive constructive feedback, leading to marketing and service costs reduction and brand image improvement. Hence, establishing consumer loyalty and retention is an important business activity, as it often leads to more consumer purchases, increasing in both sales and profits (Chi 2005).
Loyalty is a commitment behavior and develops as the vendor earns the trust of the consumer. Prior research found that consumers trusting an online vendor are more likely to have the intention to share personal information with the vendor and to allow the vendor to personalize products and services for them. A loyal consumer not only continuously comes back to the vendor but helps the vendor win fierce competition and sustains long-term growth through word-of-mouth (Reichheld & Schefter 2000). Lin, et al. (2015) defined loyalty as the consumer generating dependence and goodwill to a product or service, culminating in repurchase intention behavior.

Habit
Habit is the extent to which people automatically perform specific behaviors because of prior learning. The use of a mobile phone, an everyday common appliance, is susceptible to automatic behavior (Oulette & Wood 1998;Aarts et al. 1998). De Guinea & Markus's meta-study (2009 defines habit as learned behaviors that evolve into repeated behavior without conscious intention. An analog to habit is inertia, a construct of Status Quo Bias (SQB). SQB posits that people will maintain an existing action in the face of a superior action (Samuelson & Zeckhauser 1988). In the IS field Kim & Kankanhalli (2009) use SQB to describe user resistance to a new system. On the one hand, habit and inertia are simultaneously cognitive and affective (Polites & Karahanna 2012). Habit is a learned response automatically triggered by stimulus cues; inertia is a conscious choice to stay with the status quo (De Guinea & Markus 2009). The choice of status quo may also be driven by other constructs such as cognitive misperceptions, loss aversion, uncertainty, and psychological commitment (Lee & Joshi 2016). On the other hand, habit may be unconscious. De Guinea & Markus (2009) state, "behavioral inertia implies that use of a system continues simply because it is what the individual users have always done, and therefore without giving it much, if any, thought." Habit therefore seems frequently interchanged with Inertia. Cognitive inertia implies that an individual consciously continues to use the (incumbent) system even if he or she is aware that it might not be the best system. Affective inertia reflects how users continue to use a system because it is stressful to change.

Future Repurchase Intention
Future repurchase intention is the strength of consumers' intention to perform a specified behavior. In the IT context intention predicts actual usage of a technology (Bhattacherjee 2001). It was proposed that continuance intention is a post-initial adoption behavior (Limayem et al. 2007). Continuance intention does well in predicting actual usage of a technology or application. Any factors that influence behavior act as indirect influences through continuance intention. Previous literature has established that continuing IT use, like continuance intention, is fundamentally intentional and rational; users make rational decisions based on perceptions such as ease of use and usefulness, expectations from experience, beliefs. But users also use affective, emotional responses, including satisfaction and cognitive absorption; for example, emotion, not just cognition, may be an input to the continuing use decision or intention formation (De Guinea & Markus (2009). The IS literature has proposed various general models of IT usage intention: Bhattacherjee (2001) and Limayem et al. (2007) complement the theories of TAM (Davis et al. 1989) and UTAUT (Venkatesh et al. 2003). However, this paper tests the relationships of three extant drivers of continuance intention-loyalty, switching costs, and habit.

Reciprocity and Trust
In the field of entrepreneurship, where networking and partnering appear critical to success, reciprocal behavior creates a willingness to trust others when starting a business, but the added component of negative reciprocity may influence exit decisions. (Caliendo et al. 2006). Wu et al. (2008) directly examine consumer reciprocity as a mediator in the relationship between brand trust and loyalty, product familiarity and future purchase intentions both theoretically and through an empirical study of printer users. Reciprocity is present in the Perceived Organizational Support, (POS) literature. If employees perceive that they receive support from their organization so-called POS -that supports employees, the employees will in turn respond in a trusting way with care, affective commitment, and reciprocity. (Eisenberger et al. 2001;Aselage & Eisenberger 2003). Several studies (Berg et al. 1995;Wu et al. 2008;Caliendo et al. 2010;Sanchez-Franco & Roldan 2015) have established the correlation between trust and reciprocity. Trust increases the ability of people to work together and promotes reciprocity (Villares et al. 2011). Reciprocity as a form of social interaction may be the antecedent, and continuing mutual byproduct with trust (Schoorman et al. 2007). In game theory trust and reciprocity may even be simultaneous (Villares et al. 2011). A variation of trust is trustworthiness, the qualitative level of trust perceived by others in a person. (Wilson & Eckel 2010;Amoroso & Mukahi 2013;Fleming et al. 2014). In the IS literature, reciprocity and trust are forms of relational social capital; trust covaries with reciprocity to lead to higher volume and quality of knowledge sharing in virtual communities (Chiu et al. 2006). We therefore hypothesize: H1a: Reciprocity is positively correlated with Trust.

Reciprocity and Habit
Reciprocity is a key driver of user motivation to contribute habitually to knowledge communities (Wasko & Faraj 2005;Kankanhalli & Tan 2005;Chen et al. 2017). In the social media context, Social Q & A Communities become communities of practice and that may generate habitual exchanges and the expectation of reciprocal information exchange (Wu & Korfiatis 2013). In game theory, reciprocity acts as an input variable in understanding repeated, cooperative behaviors (Goldstein et al. 2011). The feeling of obligation, or indebtedness, occurs commonly in social, family, and organizational situations. Among peers in a formed social network there was a tendency for members to share knowledge and contribute. There was also a perceived social pressure to continue this contribution (Chow & Chan 2008). When the member is structurally embedded, he or she contributes even more (Wasko & Faraj 2005). Families are another type of social group. There are moral obligations and indebtedness between young adults and their parents. (Freeberg & Stein 1996) This is particularly felt for collective societies such as in Asian cultures, where youths retain strong traditions of duty, respect, and support to and for family members (Fulgini et al. 1999). Such a tradition may extend to Asian peer groups such as those in the Philippines. We therefore posit: H1b: Reciprocity is positively correlated with Habit.

Trust and Loyalty
In marketing, multi-dimensional trust drives perceptions of value and ultimately loyalty (Sirdeshmukh et al. 2002). Trust also builds commitment and then positive behaviors such as lower propensity to leave, aka loyalty (Morgan & Hunt 1994). As mentioned earlier Wu et al. (2008) examine how trust affects loyalty with printer consumers. One possible context for trust and loyalty may be in the pasaload process; it is not just a financial mechanism but is also an interaction of social and economic exchanges. In the pasaload context trust is an important antecedent of reciprocity: before exchanges can occur, there must be a modicum of trust between senders and receivers of prepaid loads. Amoroso & Mukahi (2013) found that a consumer's propensity to trust leads to increased loyalty among persons trusting and being trusted. Therefore, we hypothesize that: H2a: Trust is positively correlated with Loyalty.

Trust and Future Purchase Intention
Trust can be "a social lubricant that reduces the cost exchange…in daily market exchange." In the IT and marketing literature, the construct of trust is normally used to describe a consumer's trust for a product, a website, or a company reputation (Chen et al. 2002;Bauer et al. 2002;Pornpratang et al. 2013) in our study we use trust in the social context, i.e. trust in a community (Gilbert & Karahalios 2009). We propose that trust leads to a greater degree of repurchase intention: trust, trustworthiness and loyalty positively simultaneously interact. Trust reinforces repurchase intention (and vice versa). The process starts when someone asks another for his or her surplus cash from the mobile phone's prepaid card primarily via the much cheaper SMS or text messaging. It does not involve a transaction among strangers, since a probable relationship already exists via mobile phone communications-among classmates, fellow employees, relatives and friends, and so on. Trust is therefore the precondition for pasaload repurchase, especially since the process involves multiple two-party relationships. Therefore, we posit: H2b: Trust is positively correlated with Future Repurchase Intention.

Switching Costs, Future Repurchase Intention, and Habit
Switching costs were found to have a positive impact on continuance intention because switching costs make switching to alternatives difficult for consumers (Kim, et al. 2014;Zhou & Lu 2011). Switching costs also were found to increase consumers' propensity to continue use a service (Liaw and Liang 2013). Switching costs rise when consumers are satisfied with their current choices, despite potential replacements (Battacherjee et al. 2001). Status Quo Bias (SQB) describes how people, given satisfaction with the status quo, maintain an existing action, i.e. develop a habit, even in the face of a superior action or products (Samuelson & Zeckhauser 1988). We therefore hypothesize: H3a: Switching Costs are positively correlated with Future Repurchase Intention. H3b: Switching Costs are positively correlated with Habit. Cyr et al. (2006) found that loyalty is an indicator of continuance intention and an important construct in the context of online financial transactions. Holland & Baker (2001) developed an e-business marketing model and found that creating brand site loyalty leads to both behavioral and attitudinal outcomes from consumers, such as repeat visits of, strong support of, and favorable attitude toward the website. Shih (2011) and Amoroso & Ogawa (2013) found relationships between attitude and loyalty and between repurchase intention and loyalty and concluded that the both the behavioral loyalty model and the attitudinal loyalty model were predictors of repurchase intention. Loyalty was also found to be a key to online retailers to enhance satisfaction and increase repeated use intention for online consumers (Amoroso & Ogawa 2013), and for mobile app repurchase (Amoroso & Lim 2015). Therefore, we hypothesize that: H4: Loyalty is positively correlated with Future Repurchase Intention.

Habit and Future Purchase Intention
The habit construct, in the IS field, is a learned, and then an automatic-unconscious behavior, that impacts intention and usage. Information systems studies link habit to continuance intention (Kim & Malhotra 2005;Cheung & Limayem 2005;Liao et al. 2006;Lankton, et al. 2010;Venkatesh, et al. 2012). Habit limits the predictive power of intention on usage behavior; past online behaviors have a significant effect on continued usage, and initial use can significantly impact future repeated use (Cheung & Limayem 2005). Liao et al. (2006) tested habit, perceived usefulness, and trust to strongly determine online purchase behavior. Kim et al. (2012) found strong relationships between habit, perceived switching costs, and continuance intention. Hsu et al. (2015) found that habit moderated the relationships between purchase intention and perceived value, trust, and satisfaction.
In the marketing literature the constructs of habit and inertia are used to predict a consumers' continuance intention, and brand loyalty (Polites & Karahanna 2012;Bawa 1990;Greenfield 2005;McMullan 2005). There might be a "tug-of-war" between conscious (self-regulating) and unconscious (habitual) behavior in mobile phone use (Soror et al. 2015). Habit appears to be a "push" variable for loyalty and repeat consumer purchases and, by association, higher levels of satisfaction (Amoroso & Ogawa 2013). Consumers may be creatures of habit and prefer repetition as the path of least effort. Once consumers consciously attach themselves to a brand that meets rational or emotional needs, habit may trump satisfaction and loyalty in predicting continuance intention (Lafley & Martin 2017). Habit might also make users blind to novelty (Berinato 2017). Habit can therefore represent a kind of switching cost. Therefore, we propose: H5: Habit is positively correlated with Future Repurchase Intention.

Research Model
Our research model comprises six constructs: loyalty (Bajaj & Nidomulu 1998;Karahanna et al. 1999); habit (Polites & Karahanna 2012); switching costs (Bhattacherjee et al. 2001); reciprocity (Belk 2009); trust (Gilbert & Karahalios 2009.); and future repurchase intention (Bhattacherjee 2001;Limayem et al. 2007). The framework in Figure 1 shows how we address these research gaps. Appendices A and B summarize the literature used to support the theory for the reciprocity and trust constructs in the model.

Measures
We operationalized constructs for mobile technologies by using validated items from prior research (see Table 1). From  and , we used scales for loyalty, and habit from that research. For reciprocity and switching costs, we examined the future repurchase intention to use mobile technologies as a combination of actual and planned reciprocity ). All items identified in existing instruments were categorized according to literature. To limit survey length, we selected two to five scales for the measurement of each of the constructs, wording them similarly to the original studies (See Table 1). We eliminated redundant or ambiguous items, which might load on more than one factor in subsequent analysis.

data Collection
We used the snowball sampling approach per Kosinski et al. (2015) to achieve a rapid increase sample size. Social media samples provide an inexpensive and relatively high-quality alternative for collecting data (Balter & Brunet 2012). Given enough participants, the representativeness of the population can be improved, coming close to true randomization (Kosinski et al. 2015). Benefits from snowball sampling include increasing the sample size and determining respondents yet unknown. Atkinson & Flint (2001) found that snowball sampling may be applied as a more formal methodology for making inferences about a population which might be difficult to locate. The initial sample for the survey was 1,113 Filipino consumer respondents. After scrubbing the data and deleting incomplete answers we generated a final sample of 1,050 respondents, for a response rate of 94.3%.
The gender breakdown was very close to half with 48% male and 52% female. Although most of the sample covers persons in the 18-20 age group (46.4%), there was strong participation of 26-30-year-olds (20.2%). Because we used the snowball approach to collecting data, we got respondents through age 50 (9.2%) and even at the younger age, less than 18 (5.8%). We feel that the sample shows diversity in the population. Most of the living budget for the Filipino respondents was between 250-500 pesos per week (29.8%) with a larger group having a budget over 500 pesos (37.7%). (USD 1 = 50 pesos) Respondents with less than 50 pesos allocated for load per week were predominantly post-paid consumers (21.9%) or both pre-paid and post-paid. The majority of respondents claimed having a load budget of between 50-100 pesos per week (32.6%), followed by 100-250 pesos (16.6%).
The majority of respondents passed or received load only one time per week (50.5%). Very few respondents passed or received load every day (3.5%) or even as much as 4-5 days per week (16.2%). We asked respondents: if they ran out of load, did they borrow load (55.7%), get a loan for load (27.0%), or get a gift for the load (21.9%)? We defined loans as giving back the load plus additional interest agreed upon in the future; whereas borrowing load is an agreement to pay back the exact amount in the future; and receiving a gift of load that does not require any pay back in the future. We also asked respondents if they purchased load, did they do so at a retail outlet (66.9%) or from another source (33.1%). The consumer purchase behavior for the mobile phone plan was predominantly prepaid (78.1%), followed by postpaid (15.1%). Respondents were also asked if they took advantage of promo plans by the carriers, such as 50 pesos for unlimited texts for a week. We found that the majority of respondents (70.9%) did take advantage of promos while 29.1% did not purchase promos from the carriers.

Measurement Model Assessment
The research constructs are based on prior research where scales fit the construct's conceptual meaning, to ensure reliability and construct validity. Construct validity and reliability was established by Cronbach alpha, AVE, and factor analysis. All measurement scales showed high Cronbach alphas (see Table 1) at α 3 0.70 for most of the measures (Moore & Benbasat, 1991). Although continuance intention (α=.683) and loyalty (α=.692) are slightly under 0.70 we observe reasonable scale reliability, consistent with prior research dealing with similar constructs.
Discriminant validity analysis refers to testing statistically whether two constructs differ; convergent validity tests through measuring the internal consistency within one construct. Indicators for different constructs should not be so highly correlated as to lead one to conclude that they measure the same thing. This would happen if there were definitional overlaps between constructs. The average variance extracted (AVE) estimate, which measures the amount of variance captured by a construct in relation to the variance due to random measurement error, ranged from 0.688 to 0.812. Discriminant validity requires that the square roots of the AVE should be greater than correlation between two constructs. We calculated the square roots of the AVE and compared with each correlation scores and we found that with all constructs the AVE was greater than the correlations between the constructs, indicating that all the constructs share more variances with their indicators than with other constructs. Thus, our measures exhibited sufficient discriminant validity.
We conducted exploratory factor analyses (EFA) and confirmatory factor analysis (CFA) using Amos 25. EFAs were used to examine the basic structure of the measurement items. Using a principle components extraction method, all of the measures were analyzed through the Varimax rotation. The EFA factors loadings are reported on Table 1 where each of the measurement items loaded cleanly on one and only one factor, without any cross-loadings. Next, a CFA with maximum likelihood was conducted on the 12 indicators of the 4 latent variables to further ensure the reliability and validity of the measurement items. The structural model fit was estimated to test the proposed research model and hypotheses. The final factor analysis accounted for 72.4% of the total variance. While all EFA loadings ranged from 0.522 to 0.751, the CFA loadings ranged from 0.571 to 0.814. The excellent fit indices and lack of cross-loadings lend support to the construct validity of each measurement in the model as indicated by the earlier EFA and CFA results. These coefficients also provided evidence for the convergent validity, suggesting that each construct is well represented by its own indicators.

Assessing the Predictive Model
Figures 2 and 3 present the final SEM models for the impact of consumer trust and habit as intermediary constructs on repurchase intention in the Philippines without the reciprocity and trust constructs, and with reciprocity and trust. The path diagrams were produced with AMOS 25. The first model shows the effects using trust and habit as independent variables mediated through loyalty and switching costs, respectively, and without reciprocity and trust variables. on future repurchase intention (see Figure   Table 1

. Construct Reliability and Validity
2). To determine whether the sample data are consistent with this studied hypothesized distribution, we developed a structural equation model (SEM) and conducted a chi-square goodness of fit test. From the goodness of fit tests, we found that the χ 2 is at 638.2 with 127 degrees of freedom; where NFI (.967), RFI (.951), IFI (.977), TLI (.965), and CFI (.977) are reasonable with most indicators greater than 0.90, with the accepted value of RMSEA at 0.048. These imply a good fit of the model, which the proposed three constructs could generally well explain trust and of consumers toward future repurchase decisions in the Philippines.
The constructs in Model #1 showed strong R 2 values for future repurchase intention (67%). Loyalty showed a highly statistically significant path coefficient with future repurchase intention (b=.97), while switching costs showed a significant path with habit (b=.95) and with future repurchase intention (b=.88). Habit was a strong predictor of future repurchase intention (b=.81), and had a strong relationship to loyalty (b=.81), similar to the findings of Amoroso & Chen (2017) where switching costs showed a weak relationship with continuous intention, but habit showed a stronger path coefficient with continuous intention. This model shows the strong impact of switching costs on trust, habit, and future purchase intention behavior.
Model #2 shows the effects of reciprocity and trust, and reciprocity as an antecedent of trust and habit (see Figure 3). With the SEM goodness of fit tests (see Table 6), Χ 2 is at 676.7 with 127 degrees of freedom; where NFI (.885), RFI (.910), IFI (.905), TLI (.885), and CFI (.905) are reasonable greater than 0.90, with the accepted value of RMSEA at 0.054, implying a good fit of the model. The constructs in Model #2 showed strong R 2 , strong explanation of variance with future repurchase intention (83%). Adding reciprocity as an antecedent variable shows a 16% increase in variance explained from Model #1 to Model #2. Again, loyalty shows a strong path coefficient with future repurchase intention (b=.96) like Model #1 (b=.97). Trust shows a strong path coefficient with loyalty (b=.84). Switching costs showed a markedly weaker path coefficient with future repurchase intention (b=.26) in this model with the addition of reciprocity than in Model #1 (b=.88). Switching

Figure 2. Structural Equation Model #1 without Reciprocity and Trust
costs has a strong relationship with habit (b=.92) like Model #1 (b=.96), however the path between habit and future repurchase intention was slightly weaker (b=.65) than Model #1 at b=.81) showing the relative impact of reciprocity. Reciprocity, being added as an antecedent construct, shows strong relationships with both trust (b=.66) and habit (b=.60) -showing its importance in boosting the levels of the mediator variables on future repurchase intention.

Mediation Analysis
We tested mediation, using bootstrapping in Amos 25, as recommended by MacKinnon (2008). Bootstrapping is a resampling method to estimate standard errors and create confidence intervals. Mediation analysis confirms the mediation effects of satisfaction and habit on continuous intention and consumer satisfaction in our research model. Bootstrapping is also useful in assuming a more normal distribution, required as an assumption of SEM, reducing type 1 errors (Changya & Wang 2010). Bootstrapping can be used if the variables have measurement errors that then cause the mediation effect to be potentially underestimated. We ran the predictive SEM model to get indirect, direct and total effects. Cheung & Lau (2008) recommended the generation of 1,000 bootstrap samples in order to determine type 1 error rate. The results of the bootstrapping procedure estimate the standard error and potential biases of each path. We examined standardized indirect and two-tailed significance. The criterion for mediation was the identification of a significant indirect effect of the predictor on the outcome, rather than a significant decrease in the direct effect (Rucker et al. 2011). Both loyalty and habit showed mediator effects. Trust to future repurchase intention was found to be mediated by loyalty (p=.002), where the direct effect without the mediator is b=.32 and the direct effect with the mediator is b=.21. Similarly, switching costs to future repurchase intention was found to be mediated by habit (p=.001) where the direct effect without the mediator is b=.26 and with the mediator is b=.11.

Theoretical Contributions
The paper demonstrates the potential explanatory power of social mediation on technology use. While past IS literature delved on physical, i.e. instrumental, functional, ease of use, habit, loyalty, satisfaction attributes, in models such as TAM (Davis et al. 1989) and UTAUT (Venkatesh et al. 2003), we might now account for the shifts in the availability, ubiquity, and interconnectivity of current IS technologies, and how they generate social interaction, which might enhance physical drivers of tech use. This is not only reflected on use of tech as communication devices; computers have become social capital building and social capital maintenance devices as well (Gilbert & Karahalios 2009;de Zuniga et al. 2012). Table 2 shows the maximum likelihood estimates and resultant hypotheses support. SEM model #1 shows the confirmatory support for earlier, "seminal" IT models on repurchase intention. For instance, Limayem et al. (2007) discuss habit; Kim et al. (2014) cover switching costs;and Cyr et al. (2005) assess loyalty. The contexts for such studies varied from intention to repurchase from e-commerce sites, to intention to reuse applications, but common to these contexts was that all constructs in the base model ultimately derive from the users' direct interaction with the product, sans social factors: Were they loyal to the product? Did they form habitual use of a product that was hard to break? Did their switching costs about the product lock them in to continued use, despite their desire to break away? SEM model #1 alone provides support for factors to consider in creating a "sticky" IT product, such as an app or an e-commerce site.
SEM model #2 introduces a new set of factors: the role of a social or socially-mediated feature in the product-in this case, reciprocity and trust. The factors add significant variance explained in the repurchase intention of the product. We measured not so much the social play, interactivity, and aesthetic qualities of social tech games such as "League of Legends," but pasaload, a relatively benign transaction with no sophisticated user interfaces other than a basic text screen. In other words, pasaload does not fundamentally change the physical way users interact with the mobile phone. However, in subtle ways, pasaload reciprocity makes the mobile phone more than just a communication appliance; it becomes a mechanism of exchange, and therefore trust and social capital building (Chiu et al. 2006;Gilbert & Karahalios 2009).
The IT literature notes that a product is "sticky," given loyal, habitual customers, and customers who believe their switching costs are too high to change products. Base model #2 illustrates how the same loyalty, habit and switching costs can be dramatically enhanced with a simple load-sharing mechanism. More deeply, the act of load sharing opens a pathway to understanding how such a feature alters standard user behavior about the product. As indicated earlier, reciprocity might straddle between pure exchange and pure sharing. The debt of gratitude strongly influences the pasaload phenomenon in Philippine mobile users. Though unquantifiable, the exchange may still be considered as a social capital transaction more than an economic one. If reciprocity is withheld or delayed, there may be social penalties, at least in the Philippine context. In effect, a pasaload network could have the same behaviors as a social community, such as shirking, response, mutual monitoring, and altruism (Bowles & Gintis 2001;Uyen & Prociuncula 2010). In the context of virtual knowledge communities, trust and reciprocity are merely one aspect of relational social capital. Chiu et al. (2006) have modeled three different aspects social capital" relational, structural and cognitive dimensions (e.g. social ties, identification, shared language, shared vision) to knowledge sharing quantity and quality. Any positive social capital seems to make not only products more "sticky" but also non-consumer activities such as virtual communities sustainable and empowering.
Sharing as a socio-cultural phenomenon not only builds the social network and the community of users, but also reduces friction, such as the cost of search-well documented in electronic marketplace literature, and once initiated, lowers price sensitivity to products--effectively easing the burden of the decision to repurchase (Alba et al. 1997;Bakos 1998;Lynch & Ariely 2000). The pasaload reciprocity model of behavior provides an accessible and immediate "supply" of funds, and given reciprocal expectations, both the strength of the network and mobile usage should increase. Finally, as suggested in model #2, the implied economic and social factors are more deeply embedded in reciprocity than in the other constructs of trust, loyalty, and habit, and thereby reinforce the construct as an antecedent variable that influences other variables. The Philippine telecommunications model of reciprocity may be generalizable to any system of exchange and/or sharing: as reciprocity builds, so do trust, loyalty, and habit, thus resulting in ultimate stickiness and reuse of a product or system or service.

Practical Contributions
The findings in SEM model #1 suggest that habit might be a strong mediator even if the true financial and convenience costs to switch is low: throughout the Philippines, the many company stores of Globe and SMART are complemented by many thousands of neighborhood stores, street vendors, and mall kiosks who have ready-to-sell SIM cards. Note however that pasaload only functions within the same telecommunications firm, raising the practical inconvenience of having an entire network of users switch.
The marketing and product specialists are familiar with switching cost, loyalty and habit as "tools" to influence consumers. Given the inconvenience for users to switch, the marketing strategist might shift priorities in the marketing budget from raising switching costs to building on social connection initiatives that increase usage (and company profits) while at the same time reinforce the other mediators -such as habit and loyalty. One obvious tactic would be to increase reminders of pasaload as a gift to target specific network users during key events such as Christmas or Valentine's days among friends. Companies already promote "events" from birthday reminders to "specials" such as Father's Day. Some events may be "extended"-preparations for weddings, for example, are a source of continuous social exchange and an opportunity for offering products to the different groups. Brand building via pasaload need not be completely commercial, even as marketing executives suggest opportunities for socialization via pasaload. For example, one socially responsible initiative occurs in the Philippines during emergencies such as typhoons where land lines temporarily cease to function.
SEM model #2 suggests that reciprocity augments switching costs as a mediator. Pasaload itself might be tacked on to switching costs as a new fee. Pasaload shows the hidden power of social mediation in a product, without players actually socializing. It effectively becomes a social exchange tool, thus adding a strategic dimension to marketing. A socially mediated product therefore opens a new dimension for product design and marketing. For example, "feel good" and "do good" products and services help build social esteem among members of the network. Moreover, a telecommunications company might consider adding the accumulation of social capital as a more sustainable competitive edge against other competing products, over and above the traditional factors of the 4Ps of price, brand, Table 2. Hypotheses Support features, and delivery. Such effects are easily executable in the digital space such as in social media products like Facebook, Instagram, and Pinterest (de Zuniga et al. 2012), and the popular literature already recognizes the power of socially mediated streaming sites as YouTube and the streaming platform such as Twitch (Clark 2017) that has created careers for individuals with their own growing networks and is a ready vehicle for building consumer awareness and capturing them.
In the digital space reciprocity is even more convenient: with a click, users may reciprocate, or send an SMS to give a load to a friend. Such convenience may be more difficult to reproduce in the physical, rather than digital world. But technology can be so ubiquitous with the growth of the internet of everything devices which make reciprocity possible. Today, retailers, like Wal-Mart or Target, can provide barcode readers to shoppers for bridal registries. Might such strategies also extend to reciprocal gift-giving to family and friends? Anecdotal experience from one of the Philippine telecommunications firms suggests the following social interaction: Even if the giver of pasaload may continue to have positive social capital with the recipient, if the former perceives that his or her gifts are one-sided, the prospective recipient may not get another load but instead a chiding message, usually in the vernacular saying in essence: "That's enough for now." While the translation does not capture the nuances of the original, the implications are in essence: "We're still friends (Bayanihan or kinship) but you owe me (utang na loob) and it's your turn (to give me something)." Reciprocity, however, is not necessarily a monetary equivalent. For example, the passage of time without an actual transmission of a pasaload may be sufficient to satisfy the original giver. More likely, a token amount and not a repayment of the accumulated pasaload will be sufficient. As stated earlier, reciprocity involves some implicit contract to return favors, even if the returned favors are economically unequal (Belk 2009). Marketing strategists know that not all consumers are equal-not only in terms of income but also in persuasive ability. The social exchange of pasaload can reinforce this phenomenon and develop individuals within their network who in effect become informal enhancers of a brand or product.
In the Philippines, the duopoly between SMART and Globe generates intense competition for customers. The thrust of efforts to reduce churn and capture market seems to be through competitive pricing or physical product enhancements. As such, telecommunication product managers might consider reciprocity-based options. If at present, individual plans predominate, there might be opportunities instead for reciprocating micro-community plans, where users join as cliques or "barkadas," in the local vernacular. Already one competitor has considered giving SIM cards to groups of friends to effectively lock them into one system. Likewise, group plans for nuclear families, where reciprocating is almost obligatory (i.e. head of family purchases a large load, then passes smaller loads to children) and thus might lock customers in and increase convenience, loyalty, and raise switching costs. To encourage heavier use or product, telecommunication firms might also discount pasaloads if sent to cliques or friends (Bowles & Gintis 2001).
The two telecommunications firms are riding the trend towards data science with big data analysis. Telecommunication product managers already possess substantial big user data for analytics. The two competitors are aggressively expanding their resource pool of data analysts. By tracking customer usage, they can identify pasaload peaks and valleys and send out occasional prompts to the effect that: "Isn't it time to pasaload?" By now SMART and Globe must be able to track the millions of pasaloads to and from, to who and when. The analysts can be directed to assess inter-friend and inter-family pasaload and correlate it with usage as well as repayment security. Such analytics may reveal seasonal, cyclical, occasional patters on social consumption/reciprocity, which leads to more efficient plans to make pasaloads last longer and to buy more than normal loads.
Reciprocity may be extended beyond pasaload and into e-money. Currently Japanese consumers make most retail purchases via their mobile NFCs (Near-Field Chips). In the Philippines, a telecommunications firm might allow pasaload to pass through third parties like retail and financial companies. An arrangement can take place whereby a retailer that is not normally part of the reciprocal network can, through the telecommunication firms, participate in pasaload transactions for gift-giving, holiday presents, and special occasions. These options for telecommunication firms in the Philippines are currently limited by banking, anti-trust, and anti-competitive regulations that act as barriers to telecommunication firms selling of financial products. However, globalization and the need to attract foreign direct investment from multinational corporations are gradually wearing down these barriers.
Beyond pasaload, reciprocity as an antecedent may work well in strategic marketing communications. Reciprocity can be enhanced beyond the exchange of goods or services for cash as a transaction to a sharing that connects the company to the product-based network or community of consumers. It can reinforce existing practices. For example, various loyalty programs offer points and other incentives. The communications message may be adjusted from "we want your loyalty" to "we are doing something for you." Corporate social responsibility initiatives are indirectly a key part of marketing communications. Reciprocity could call attention to these initiatives as part of sharing, or participation by multiple stakeholders as socially responsible. Marketing communications in a social network almost by definition requires a social or sharing component and a message or advertisement involving reciprocity could result-for example, in viewers not clicking the "Skip Ad" button, a process that can explored.
Finally, reciprocity and trust are only two forms of social capital building. Chiu et al. (2006) identify other social building behaviors such as identification, or the recognition of a person as an in-member of a group (e.g. Facebook groups), or having a shared language, (e.g. Instagram will have features that teenagers easily and uniquely identify with versus their parents), even a shared vision (e.g. special interest groups, members-only blogs) (Chiu et al. 2006). Philippine telecommunication firms have actively complemented their services with popular external apps like Facebook, in order to make their phone products stickier. SMART and Globe, for example, give free data to Philippine Facebook users, (though without messaging or image downloading) knowing that Facebook is almost a necessary appliance for Filipinos (Castro 2018). However, the telecommunication firm itself might investigate how to simulate these other social capital building activities beyond complementary apps and into their in-house native products.

Limitations and Future Research
As in many survey-based methods papers we based our findings on static, variance theory (Markus & Robey 1988). However, a more interesting extension is to take a process view, as in study smartphone users' reciprocity over time-how do the same factors affect the journey of reciprocity of users? How so social interactions grease or hinder reciprocity? How do such interactions degrade or increase trust with respect to reciprocity? This article did not assess other omnipresent social factors, such as network externalities, where the sheer lack of alternatives makes users more inertial, harder to switch, or render loyalty and trust moot. For this article, we considered these network effects as a common factor affecting all users, a recognized limitation but also an area for future research.
Within the Philippine cultural context of "debt of gratitude," deeper research can be undertaken on pasaload within the nuclear and extended family network where a dichotomy can be explored between family as insiders and friends and others as outsiders-with the obvious presumption that load sharing would be significantly stronger with the former, which is not yet demonstrated by research. This exploration might also create a dichotomy on "reasons why" that in turn would offer practical suggestions on reciprocity and repurchase intent. We can likewise investigate how other forms of social capital like identification, shared language, and shared vision relate to product stickiness.
Given the Philippine context, one comparative area of research is to explore the impact of debt of gratitude in other Asian countries with a common cultural context. For example, Japan has a similar concept of obligation -"giri"-that could be explored. Moreover, the research can be expanded to other non-Asian countries where the cultural contexts may differ, and more transaction-based economics may be more prevalent. Finally, the original research by Goulding (1960) that demonstrated the universality of the reciprocity concept might be juxtaposed against the research that implies the differentiating impact of culture.

CoNCLUSIoN
The strong cultural sense of kinship (bayanihan) and its corollary of reciprocity (utang na loob) among Filipinos extends beyond the immediate family with telecommunications and mobile phone technology as a driver for Filipinos to build and expand social networks with via social media as the key vehicle. With respect to the research objectives, pasaload exchange reinforces reciprocity, and it appears embedded within the Philippine cultural value system, such that practitioners are already applying it in their strategies. Pasaload acts as a "stickiness" tool to sustain the process of reciprocity. At the same time, pasaload has both intrinsic value and social worth as it is exchanged among friends and other members of the social network.
Our paper allows for the convergence of theoretical and practical considerations. While bearing in mind ethical and privacy issues, software already exists to track and identify "persons of influence" in social networks. Quite apart from tracking "hits "and scoring "likes", pasaload represents a quantifiable measure of exchanges among network members as a means to analyze individual impact on particular networks or clusters of communities. The data, once analyzed and structured, may result in further identifying characteristics and patterns of behavior and action. Research on pasaload data might successfully measure degrees of influence among members, for example, and further enrich the study of reciprocity.
The practical implications show even more promise. Once again keeping in mind ethics and privacy, practitioners can engage in targeted marketing to identified persons of influence, as opposed to simply blanketing a network or cluster. Since pasaload appears to be a form of currency, companies through the telecommunication firms, can offer "bonus" pasaload to reward key individuals in a group and to provide quasi-monetary pasaload incentives that allow these targeted persons to expand their influence. The combination of pasaload with specific products might generate increased revenues and build brand reputations.
Beyond telecommunication products, this paper illustrates how social capital lubricates and enhances trust and loyalty. Social capital raises switching costs and habitual behaviors. All these converge to raise continued use of products. Habitual use of smartphones is well-documented and may have actually reached epidemic rates (see literature on FOMO, or "Fear of Missing Out" in Rosen et al. 2013;Elhai et al. 2016). However social capital may be the extra "glue" that makes general products, not just phones, stickier. For example, while a streaming product like Netflix may have a wide selection and quality content, as well as a crowd-sourced recommending system, Netflix does not inherently have direct social elements such as friends and "likes" in Facebook. Netflix may be therefore more susceptible to customer churn, compared to, a counter example of socially mediated services in Spotify, the Swedish-based music service. One of Spotify's features is for users to collaboratively build and share and/or look at other people's playlists, to augment their listening variety (Gilmour 2018). There is also Israel-based iTero, a relatively new product that uses social trading, "wisdom of crowds" brokerage service, where one can copy stock trades of strangers and acquaintances. While quality content and user interfaces are still paramount for consumer loyalty, we submit that "extra" social capital building elements can help raise switching costs and lock-in users just about any product.