The Influence of Perceived Market Uncertainty on Consumer Decision-Making: A Focus on Price,Income,and Exchange Rate Dynamics

Uncertainty in the Global Markets Context

The current global economy is witnessing intensified volatility, with many fluctuations occurring, which have adverse effects on individual expenditures, savings, and financial behaviors. Various global events, for instance, the COVID-19 pandemic, as well as political conflict situations such as the Israeli-Palestinian and Ukrainian-Russian conflicts, are resulting in high uncertainty levels.

Previous studies targeting developed countries prove that economic uncertainty has adverse effects on individual expenditures. For example, studies by Coibion et al. (2024) and Nam et al. (2021) prove that when financial and macroeconomic uncertainty indices rise, individual household expenditures decline substantially and consistently.

Objective versus Subjective Uncertainty

The traditional approach to economic analysis has mainly concerned with uncertainty using objective data, focusing on macro data such as dispersion in forecasts for GDP, inflation rates, and stock market volatility, as shown in studies by Altig et al. (2020), Fortin et al. (2023), and Nam et al. (2021). More recent studies in the field covered by Han et al. (2022), Zhao et al. (2022), and Walters et al. (2023) argue that perceived uncertainty must also be recognized.

Perceived uncertainty relates to how people read changes in their environment and how such readings influence what they anticipate about income, prices, and economic stability in general. Drawing on this perspective, Ben-David et al. (2018), Chirumbolo et al. (2021), and Zhao et al. (2022) indicated that people who are uncertain about their future earnings, work, and personal life, tend to save more and spend less. These studies in addition to other studies, indicate that perceived uncertainty, shaping consumer behavior, is a function of subjective states than of objective conditions within the external world.

Dimensions of Perceived Uncertainty

This study focuses on the way in which the perceptions of changes in prices, exchange rates, and incomes are significant elements that influence the perceptions of uncertainty. The three elements were selected because, among others, they have an obvious direct effects on consumers confidence and behaviors. Changes in prices affect incomes through ripple effects, while exchange rates may affect imports and consequently purchasing power (Fortin et al., 2023; Nam et al., 2021). Changes in incomes serve as determinants of perceived incomes; these changes show how vulnerable consumers can be to any form of economic challenges, such as reduced incomes due to reduced wages and loss of jobs (Ben-David et al., 2018; Han et al., 2022).

In extreme socio-economic fluctuations in countries such as Palestine, which falls under developing and conflict economies, consumers do not have direct experiences involving macroeconomic elements such as financial market instability and uncertainties (Chirumbolo et al., 2021). Hence, to articulate with theory, the current study emphasizes these three factors.

The Palestinian Market Context

The Palestinian market is defined by what is termed a “conflict economy.” This economy suffers from significant instability, both economic and political. The market has struggled for over 70 years with issues such as trade barriers and lack of control over borders and resources. Fluctuations in incomes, prices, and exchange rates have thus become part of life for Palestinians (AbdAlrahman, 2020; Fraihat, 2022).

The Palestinian economy operates with three interchangeable currencies which are the Israeli shekel (ILS), the U.S. dollar (USD) and the Jordanian dinar (JOD). These currencies are used for paying salaries, savings, and commercial transactions (Merrino, 2021). This multi-currency system subjects consumers to ongoing exchange rate fluctuations and making it difficult to compare prices accurately or assess true purchasing power.

The Palestinian economy is also highly dependent on imported goods and services. About 56% of imports come from Israel, while the rest come from Turkey, China, the Arab region, and Europe. This, in turn, makes the market prices highly affected by international price changes and currency rates (Palestinian Central Bureau of Statistics, 2025b).

Moreover, for more than 3 years, the public-sector employees have faced partial salary payments of about 60% of their due wages, resulting from withheld clearance revenues and fiscal restrictions by the Israeli government.

After October 7th, 2023, the unemployment rate in Gaza is about 69% by the end of 2024. The effect of war was less severe in the West Bank, where the unemployment rate was 31% in late 2024 (International Labor Organization, 2025).

All of these factors reveal a severe perceived uncertainty across three crucial fields of price levels, currency values, and income, which makes Palestine a perfect setting for studying how consumers adjust their consumption behavior in the face of underlying uncertainty.

Research Gap and Objectives

Although uncertainty has a globally significant impact, there is still limited empirical research from developing and politically unstable countries. Most studies focus on stable developed countries with reliable monetary policies and social safety nets. In Palestine, where uncertainty is complex and structural no research has yet explored how consumers perceive and react to fluctuations in prices, income, or currency values. While extensive research provides insight into the behavior of consumers in uncertain situations, yet there still a few studies that understand the behavior of consumers who adapt their buying decisions during times of continued instability.

this study fills that gap by aiming to analyze consumer reactions to changes in prices, exchange-rate, and income unpredictability in the Palestinian market. Drawing on micro-level behavioral theories, the study explores how perceived uncertainty influences buying decisions in a setting characterized by continuous conflict rather than brief shocks which offers a valuable insight into consumer behavior in a developing economy confronting ongoing uncertainty.

Theoretical and Practical Contributions

The study makes three important contributions. First of all, it will expand the field of behavioral economy theory by conceptualizing perceived uncertainty as a multiple-faceted subjective construct, rather than depending exclusively on accumulated indices. Secondly, it offers the very first empirical evidence from Palestine, an economy marked by intersecting political, monetary, and income-related uncertainties.

Lastly, the research results will provide useful information for legislators and market players about how to rebuild consumer trust and come up with flexible pricing and communication plans in times of uncertainty. the goal of these contributions is to help us better understand how people make decisions when there is a lot of uncertainty and it lasts for a long time.

Palestinian Economic Context

The Palestinian economy is deeply dependent on the Israeli government policies. hence, it suffers from structural constraints leading to instability in income, prices, and exchange rates. The Paris economic protocol in 1994 created a strong customs and monetary union between Palestine and Israel. This agreement designates Israel as responsible for issues concerning boundaries, trade, and taxation. This agreement facilitates the collection of VAT and import duties by the Israeli government, which are subsequently transferred to the Palestinian authority (PA) via the fiscal clearance revenue system (Benstead, 2023; Fraihat, 2022).

In the recent years, Israeli government has repeatedly withheld payments from the budget designated for the Palestinian Authority without any justifications, resulting in payment defaults and financial difficulties (Alazzeh & Uddin, 2025). Therefore, personnel in the public service have been receiving only 50% to 60% of their monthly salaries due to payment defaults (Reuters, 2024; World Bank, 2025).

Consumption Decision

Consumer behavior is concerned with studying every step that individuals and households undergo when making choices regarding their purchases to fulfill their wants and needs. Lejiw and Fauzi (2023) define consumer buying behavior as “the behavior consumers exhibit in seeking, purchasing, using, evaluating, and disposing of products and services they expect to satisfy their needs”; Jungang (2023) defines it as “a behavior where consumers have a desire to buy or choose a product, based on experience in choosing, using and consuming or even wanting a product.” However, according to Mehta et al. (2020), in the consumption process, a consumer represents “a person who identifies a need or desire, makes a purchase, and then disposes of the product.”

A sum of internal and external factors could affect consumer decisions and cause variations in consumer behavior and choices. These include personal demographics, the level of income, as well as other social, cultural, political, and economic factors (Mehta et al., 2020; Sharma & Sonwalkar, 2013) . In the analysis of consumer behavior during the purchasing process, there are three major methods used. These are based on psychology, sociology, and economics. The approach based on psychology focuses on the role that mental events have on the behavior of consumers, the approach based on sociology focuses on the perceptions of social groups, and the approach based on economics utilizes the micro-economic framework (Mehta et al., 2020).

Economic downturns are marked by increased uncertainty and emerging trends of new behavior among consumers (Mehta et al., 2020; Sharma & Sonwalkar, 2013). During economic downturns, risk behavior and perceptions of consumers significantly influence their purchase behavior. Risk attitude measures how averse to risk a set of consumers is to particular risks, while risk perception is what they believe about their susceptibility to possible risks (Mehta et al., 2020).

In economic crisis times, consumers experience high levels of uncertainty, and their capacity to foresee future economic conditions is exceedingly challenging. Under these circumstances, their behavior becomes more cautious, and decisions frequently become irrational since they are formed according to irrational expectations and a lack of accurate knowledge, which is a situation generated by the uncertain conditions they experience. In such settings, consumers normally choose to cut their consumption, only buy necessities, increase their savings, compromise quality, and focus more on choosing cheaper brands. Before making any decision, consumers tend to gather more information. Therefore, their routine decisions will be transformed into more complex decisions (Sharma & Sonwalkar, 2013).

Prospect theory can be applied here, which says that risk behaviors are dependent on a certain reference point or benchmark related to risk. When the outcome is better than the benchmark, it is viewed as a good situation, causing a person to resist risk by opting for a smaller but certain gain over a bigger but uncertain one. When the outcome is lower than the benchmark, it is viewed as an unfavorable situation. This causes a person to feel that they are losing something, so they opt for a bigger but uncertain loss over a smaller gain. This is the opposite of the idea of economic utility, which says that humans tend to prefer the option that is likely to provide a better gain over one that is likely to provide a smaller gain, or that they tend to prefer one that is likely to provide a small loss over one that is likely to provide a bigger loss (Aljazzazen & Balawi, 2022; Inggarsono et al., 2018).

Communicating uncertainty is important because even accurate estimates if miscommunicated, can result in poor decision-making. Erroneous and biased decisions resulting from a misunderstanding of uncertainty can erode trust and confidence in those making assessments and those acting on them (Dhami & Mandel, 2022).

Uncertainty

Modern life is more complex than ever, and everything is connected, so what happens in one part of the world affects another (Al-Thaqeb & Algharabali, 2019). Globalization and technological advancements have changed every aspect of our lives, making uncertainty more important than ever. Political division, polarization, and increased government spending in the economy have contributed to recent uncertainty. Still, relatively few studies have been conducted on uncertainty and its effect on corporations and consumers until recently.

Whether in a basic or complicated context, decisions in turbulent dynamic environments are always made at a higher uncertainty level. Decisions made in an uncertain state are difficult to determine and involve a great deal of uncertainty and ambiguity about how to arrive at a decision (Welch & Lawrence, 2019).

Uncertainty is a state of ignorance that emphasizes decision-making based on insufficient information (Aljazzazen & Balawi, 2022; Dhami & Mandel, 2022; Lu & Chen, 2021) or where the future is identifiable but not calculable, with unforeseen consequences (Aljazzazen & Balawi, 2022; King & Kay, 2020; Welch & Lawrence, 2019).

Some define uncertainty as the unpredictability of fiscal, regulatory, and monetary policies, which contribute to market volatility. More specifically, economic uncertainty may be characterized as unanticipated developments that affect the economic ecosystems, as well as how changes in fiscal or monetary policies or any other government policies affect firms. Furthermore, the issue of policy uncertainty, often known as the economic risk associated with unclear future government policies and governing structures, raises the possibility that both enterprises and people would postpone their expenditures and monetary investments owing to market uncertainty (Al-Thaqeb & Algharabali, 2019).

Feeling uncertain can be caused by other factors, such as predicted changes in consumers’ income, consumption, spending patterns, liquidity limitations, and precautionary savings, which will lead to unrealistic expectations that stimulate short-sighted consumption and make consumers select between spending and savings.

The Palestinian market is regarded as having one of the world’s most unstable economies. It has suffered dramatically over the past 30 years, and there is very little prospect of a revival anytime soon. The Palestinian economy has certain peculiar characteristics that raise the level of uncertainty, such as the usage of four foreign currencies rather than only using its Palestinian currency. When it comes to trade, the Palestinians import the majority of their imports from or through Israel and export the majority of their exports to or through Israel. This economic connection and dependence have existed since the beginning of the Israeli occupation in 1948 (AbdAlrahman, 2020; Gal & Bader, 2018).

In the Palestinian marketplace which will be the subject of this research, the uncertainty will be studied in the context of market instability caused by the unstable economic and political situations in the market that created an unstable economic environment that negatively affects household income and creates fluctuations in various product prices and currency exchange rates, and therefore, affects their consumption patterns. Following that, in this research, we will look at how Palestinian consumers perceive continual swings in product price, changes in currency exchange rates, and household income as indicators of market uncertainty that affect consumers’ consumption decisions.

Decision Making Under Uncertainty

Decision-making under uncertainty is a central focus of behavioral economics and consumer psychology (Han et al., 2022; Walters et al., 2023). Classical economic theory asserts that customers act based on rational assessments of anticipated value about probabilistically specified outcomes (Von Neumann et al., 1944). However, research evidence suggests that decision-making is not necessarily a rational process, especially in developing or conflict-affected economies that face uncertain conditions with information asymmetry (Arthur, 2021; Chen et al., 2020; Dibb et al., 2021). In such economies, subjective uncertainty is more significant for consumer behaviors than objective uncertainty.

A special type of uncertainty is found in the Palestinian environment, more precisely in the West Bank, with income uncertainty that is exposed to more than one currency as well as politically driven price variability. Such environments are also found in other nations such as Lebanon, Sri Lanka, or Ukraine (Iqtait, 2022; Khawaldah & Alzboun, 2022), where consumer attitudes toward uncertainty contribute to economic conservatism.

In behavioral studies, consumer environmental information is conveyed by expressions of subjective risk attitudes and emotional expectations, focusing on uncertainty without using objective probability information (Aven, 2025; Omar et al., 2021; Walters et al., 2023). This approach takes into account subjective uncertainty.

Expected Utility Theory (EUT)

Expected Utility Theory (EUT) is currently the primary decision-theoretic framework employed by economists studying decision-making under uncertainty. This formalism was developed by von Neumann and Morgenstern in 1944, who assumed that maximizing expected utility represents the dominant objective of a rational decision maker using a utility function that is determined by certain probabilities. This is more true in a laboratory situation with controlled risk preferences than in real-world markets that are unpredictable.

Recent empirical research questions the relevance of Expected Utility Theory related to structural shocks between various nations (Yaman et al., 2023). Research by Immordino et al. (2024) demonstrates that income and price expectations significantly influence consumption behaviors: if people believe income growth is likely, their consumption estimates increase, while income risk can generate more expenditures for wealthy people. Research by Seo et al. (2025) integrated sentiment statistics of CPI news into models of predicting consumer price indexes, improving their predictability. This is another example of local story impacts on consumer cost-of-living beliefs. Moreover, for consumers living in the West Bank, their perceptions about incomes and prices in the foreseeable future is highly uncertain. Hence, even if EUT is the standard test for sound decision-making, it disregards decision-making predisposition biases caused by cognitive distortions, institutions, and uncertainty beliefs in less stable economies.

Prospect Theory (PT)

Prospect Theory, developed by Kahneman and Tversky in 1979, has been further developed in other mor recent studies. The prospect theory corrects the flaws in Expected Utility Theory by recognizing that individuals evaluate outcomes relative to a reference point, such as their existing income, and prioritize losses above gains. Loss aversion and reference-dependent behavior are two things that might assist explain why customers don’t respond or overreact to risk. Prospect theory posits that decision-making in uncertainty creates asymmetry, as individuals exhibit a heightened response to potential loss compared to potential gain (Beißner & Werner, 2023; Ferro et al., 2021).

Recent studies on macroeconomic uncertainty have revealed new areas of insight into consumption behavior. Han et al. (2022) state that “loss-preventive consumption” patterns will occur due to unpredictable income or price variation. Walters et al. (2023) identify two types of uncertainty - epistemic and aleatory - and describe how the two differ. The authors discuss how the lack of knowledge and randomness contained within the two types of uncertainty result; cause consumers to engage in defensive behavior, specifically in terms of increasing savings rates and cautious purchasing patterns.

Empirical research on unstable economies has confirmed the explanative power of the PT model. For instance, after the crisis in the Lebanese economy, the threat of shocks in prices caused consumers in Lebanese society to change their pattern of consumer expenditure from luxury to necessity goods (Bahn et al., 2025). Similarly, even when income remains stable, the anticipation of inflation volatility in Jordan was observed to reduce consumer spending (Aljaloudi, 2023).

In the context of Palestine, the application of the Prospect Theory indicates that relative changes in prices, exchange rates, and income influence the reference point of the consumers. This causes the volatility of the market to be viewed more as a potential loss rather than a gain. Hence, when financial crises or pay disruptions occur, West Bank consumers demonstrate risk-aversion as regards the consumption of durables.

Uncertainty Reduction Theory and Adaptive Consumer Behavior

While behavioral economics targets the cognitive biases and emotional reactions related to the resolution of uncertainty, the Uncertainty Reduction Theory (URT) targets the adaptational behavior of individuals when dealing with their uncertain context. Uncertainty Reduction Theory was first conceptualized by Berger and Calabrese (1975) but was later utilized in other forms of psychology and consumer behavior studies. The goal of these consumers was to reduce uncertainty as they strove to obtain more certain information as a means of avoiding risk (Fermand et al., 2024; He & Rucker, 2022). Reducing uncertainty would become a means of survival when market uncertainty occurs rather than merely a rational effort (Campbell et al., 2020).

Evidence from fragile markets supports the significance of URT. This has been reflected in consumer behavior after income shock, where they resort to precautionary saving, involving borrowing from social ties, and show brand preference to hedge against risk and uncertainty (Dunska et al., 2024; Han et al., 2022). This adjustment pattern signifies consumers acting with URT.

In Palestine, the same attitudes toward fiscal hesitation, income cuts, and currency volatility demonstrate adjustment behavior not only in curbing economic outlay but also in changing the pattern of consumption to overcome volatility. During periods of unfavorable exchange rates, consumers tend to increase their total amount of purchases to stockpile or shift their consumption preference to imported products. These behavioral tendencies are inspired by the uncertainty-reduction mechanism suggested by URT, which provides sufficient justification to include perceived uncertainty as a multidimensional construct for this study.

Perceived Uncertainty and Consumption

Based on previous theoretical work, the correlation between perceived uncertainty and consumption behavior can be accounted for using three major approaches, each of which focuses on a different aspect of uncertainty that is relevant to the West Bank economy. Those three major approaches are:

Moderating Effect

The behavioral effects of perceived uncertainty vary and depend on socioeconomic stability and employment structure. Two key factors of income level and work sector account for this variation.

The income level determines the effect of uncertainty, as it affects the ability of consumers to withstand shocks. Individuals from higher income levels have more liquidity and savings to fall back on and can afford any expenditure behavior fluctuations. This view has been confirmed among the emerging economies as individuals from wealthy backgrounds remain less susceptible to price or income-related perceptions of uncertainty, whereas other income classes remain highly susceptible as their effects get aggravated due to behavior triggered due to uncertainty (Carroll & Samwick, 1997; Han et al., 2022).

Therefore, we proposed the following sup-hypotheses:

H1.1: Consumer income level moderates the relationship between the perception of product price change and consumption decisions.

H2.1: Consumer income level moderates the relationship between consumer income uncertainty and consumption decisions.

H3.1: Consumer income level moderates the relationship between perceived currency exchange and consumption decisions.

The work sector (public vs. private) has a great influence on the level of perceptions of uncertainty related to employment. In Palestine, for example, the salaries of the people working in the public sector tend to be more stable, although sometimes the payment can be delayed. On the other hand, people working in the private sector tend to encounter instability related to job loss due to the fluctuations of the market.

Together, these pathways constitute the main framework of the current model: perceived uncertainty—regarding price, income, and exchange rates—directly influences consumption decisions. This relationship is moderated by income level and work sector.

Therefore, we proposed the following sup-hypotheses:

H1.2: The consumer working sector moderates the relationship between the perception of product price change and consumption decisions.

H2.2: The consumer work sector moderates the relationship between consumer income uncertainty and consumption decisions.

H3.2: The consumer work sector moderates the relationship between perceived currency exchange and consumption decisions.

Figure 1 shows the research model that displays the main relationships among the research variables and the supposed hypotheses.

OPEN IN VIEWER

Figure 1.

Research model.

Sample

The study was designed as a quantitative, analytical (theoretical) inference that aims to study the relationship between uncertainty and consumers’ behavior, and for theoretical testing, and not aiming to generalize the data to the whole Palestinian population, a convenience sample technique was chosen and applied to gather data from Palestinian consumers. Three screening criteria were applied to make sure that respondents are within the scope of the study: each respondent should be 18 years old or older, live in the West Bank Region, and be responsible for buying their own or their family’s requirements.

Consumers were invited to voluntarily participate in the survey. A total of 226 respondents participated in the survey. F. Hair et al. (2018) suggested that the sample-to-variable ratio necessitates a minimum observation-to-variable ratio of 5:1. Nonetheless, ratios of 15:1 or 20:1 are preferred. Thus, the 226 participants constituted a sufficient sample size for data analysis with a ratio of 56:1, given that the study has four main constructs. The profiles of the respondents are presented in Table 1.

OPEN IN VIEWER

Table 1.

The Demographic Profile of the Respondents. (N = 226).

Description	Category	(%)	Description	Category		(%)
Gender	Male	52.7	Income (US dollar)	Low income	Less than 500	20.8
	Female	47.3			501–1,000	28.3
Age (years)	18–28	20.8		Mid income	1,001–1,500	17.7
	29–39	28.8			1,501–2,000	13.3
	40–50	28.8		High income	2,001–2,500	10.2
	Above 50	21.6			More than 2,500	9.7
Marital status	Single	21.9	Work sector		Private sector	46.5
	Married	68.1			Public sector	36.3
	Divorced	6.0			Unemployed	5.3
	Widow	4.0			Working in the 48 region	11.9
Education level	High school or less	6.2
	Diploma	8.0	Wage currency		NIS	69.5
	Bachelor	52.2			JOD	19.4
	Master	26.1			USD	11.1
	PhD	7.5

The majority of the sample was male (52.7%), and almost half of the respondents were under the age of 40. 68.1% of respondents are married. The respondents are well educated, where 52.2% holding a bachelor’s degree and 33.6% having a postgraduate degree. The income levels of the sample ranged from less than $1,000 (49.1%) to more than $2,000 (19.9%). Respondents who worked in the private sector represent 46.5% of the sample, while those in the public sector represent 36.3%. Most of the respondents are paid by Shekeel (69.5%), while others are paid in other foreign currencies. The sample structure is fairly similar to the actual composition of the West Bank market.

Data Collection Method

Data collected between June and August of 2024. A five-point Likert scale was employed in the investigation, which had constructs such as Perception of Price Fluctuation (PPF), Perceived Income Change (PEC), Perceived Currency Exchange Rate (PCE), and Consumption Decision (CD) that were employed from the works of Anderloni et al. (2012), Claveria et al. (2020), and Hampson et al. (2021). The items were adapted to serve the study purpose without changing the main scope of the items.

The first part of the survey captures demographic variables. This step is followed by six questions that consider consumer perception of price changes and five questions that evaluate consumer perception of income changes. Then, there are six questions that examine consumer perception of exchange rate changes, followed by five questions on consumption decisions.

The questionnaire was initially framed in English, and then the Arabic version of the study was employed, which matches the language of the respondents. An interview was then conducted with three academicians to check the relevance of the questions, and the questionnaire was modified on the basis of the views obtained.

The questionnaire was disseminated in person, electronically using Google Forms, as well as through social media platforms and emails.

Ethical Considerations

The study adopted a non-experimental, survey-based research design in which respondents were asked to report their perceptions regarding the study constructs related to uncertainty and consumer consumption decisions in the Palestinian market. Participants were not exposed to any experimental manipulation, pre-testing procedures, or behavioral interventions. The research design minimized potential risks by avoiding sensitive questions, ensuring anonymity, and analyzing and reporting data exclusively in aggregated form.

The anticipated benefits of the study, including its contribution to understanding consumer behavior under conditions of market uncertainty in a fragile and under-researched context, outweigh the minimal risks associated with participation.

Participation was voluntary, and informed consent was obtained through clear information provided on the questionnaire’s introductory page prior to data collection.

Measurement Method

In analyzing the findings, we utilized the technique of Structural Equation Modeling (SEM) for testing our hypotheses. The analysis was conducted using Smart PLS-4, employing the Partial Least Squares (PLS) technique. The technique is most appropriate for our research due to the small sample size and the nature of our findings, which follow a non-normal distribution, and its ability to test complex study models such as our model (Barghouthi et al., 2024; F. Hair et al., 2019; Henseler, Ringle, & Sarstedt, 2016).

Nonresponse Bias and Common Method Bias

To examine nonresponse bias, a t-test was done to compare the responses of customers who participated early in the survey with those who responded later (Armstrong & Overton, 1977). There were no statistically significant differences between early and late answers.

The one-factor test was suggested by Podsakoff and Organ (1986) as a way to assess the potential threat of common method variance bias. The relevant factor analysis revealed that neither a single factor nor a general factor was developed in the unrotated factor structure.

Furthermore, this study studied the correlation links between the components to analyze common technique bias. Tehseen et al. (2017) stated that common method bias would not be a concern when the correlation between constructs is lower than .900. The correlations between the constructs in this study are less than .900. These findings removed the likelihood of any common method variance bias.

Frequency Analysis of Respondents’ Perception

According to the frequency analysis results (Table 2), respondents perceived the prices of necessary and luxury products to be higher than before. They feel that the prices are continuously increasing and believe they will not go down shortly.

OPEN IN VIEWER

Table 2.

Respondents’ Perception.

Code	Question	Mean	Std. deviation
PPF	Perceived price change	2.36	0.864
PPC1	I think the prices of the necessary products were cheaper than last year’s.	2.00	1.087
PPC2	I think the prices of luxury products were cheaper than last year’s.	2.26	1.102
PPC3	I feel that the price of products in the market is decreasing continuously.	2.19	1.100
PPC4	I believe that when product prices increase, it will go down in the near future.	2.28	0.974
PPC5	I think the product prices now are the lowest ever.	2.27	1.092
PPC6	It is hard for me to predict the changes in product prices in the near future.	3.19	1.056
PIC	Perceived income change	2.23	0.773
PIC1	My income now is lower than it was last year.	2.08	1.093
PIC2	I have had new income sources since last year.	1.96	0.981
PIC3	I am not afraid to lose my job in the near future.	2.30	1.035
PIC4	I am sure that my income will stay the same or it will be better in the near future.	2.50	0.985
PIC5	It is easy for me to predict the changes in my income in the near future.	2.33	1.054
PCE	Perceived currency exchange	2.89	0.634
PCE1	I think it is good to get my salary in Shekels rather than the United States dollar.	2.90	0.995
PCE2	I think it is good to get my salary in Shekels rather than Euros.	2.76	1.001
PCE3	I think it is good to get my salary in Shekels rather than Jordanian Dinar.	2.82	1.019
PCE4	The currency exchange rates in the last year positively affected my income.	2.92	0.935
PCE5	“I believe that the exchange rate of the shekel against other currencies has led to an increase in product prices in the Palestinian market.”	3.18	0.968
PCE6	It is easy for me to predict the changes in currency exchange rates in the near future.	2.78	1.013
CD	Consumption decision	3.935	0.714
CD1	Now, I can buy fewer products than before.	4.10	0.850
CD2	Now, for the same amount of money, I can buy a smaller quantity of the same product than before.	4.04	0.940
CD3	Currently, I cannot afford to buy all my (my family’s) requirements.	3.55	1.158
CD4	I believe that the increase in product prices does not match the increase in my income.	4.27	0.921
CD5	My current buying pattern differs from that of the previous year.	3.70	1.069

Respondents stated that their income has decreased compared to the previous year, with greater chances of losing their jobs in the near future. Additionally, the respondents found that they prefer to get their salaries in foreign currencies such as the US dollar, Jordanian Dinar, or Euro rather than the Shekel.

The responses also show that their buying power had decreased as they could not afford to buy all their basic requirements. Therefore, their current consumption pattern is different from what it was before.

Measurement Model Assessment

The evaluation of the measurement model is the initial stage of PLS-SEM analysis. Consequently, our study paradigm incorporated reflectively evaluated constructs, “composite reliability,”“indicator reliability,”“convergent validity,” and “discriminant validity” (F. Hair et al., 2020).

Indicator outer loadings should be 0.700 or higher (Wong, 2013), and any indicator score value lower than 0.500 should be eliminated from the model. While evaluating the reflective measurement model, the indicators for PCE1, PCE2, PCE3, PPC6, and CD5 were deleted for the reason of low standardized loadings. The loadings were found to be less than the suggested cut-off value of 0.500, which meant that these indicators did not contribute much to the respective constructs (F. Hair et al., 2020). Following the deletion, the internal and convergent consistency of the remaining indicators improved.

Reliability is evaluated using Cronbach’s α (Cronbach, 1951) and composite reliability (CR) statistics. As seen in Table 3, both exceeded the required threshold of .7, suggesting high reliability (Henseler, Hubona, & Ray, 2016). Similarly, Convergent validity was satisfactory because the AVEs were above 0.500 for all constructs (Fornell & Larcker, 1981).

OPEN IN VIEWER

Table 3.

Measurement Model Output.

Construct & items	Loading	t-value	Construct & items	Loading	t-value
“Perceived Currency Exchange Rate (PCE)” “Cronbach’s α = .818; CR = .831; AVE = 0.733”			“Perception of Price Change (PPC)” “Cronbach’s α = .860; CR = .864; AVE = 0.640”
PCE3	0.837	3.826	PPF1	0.760	28.062
PCE4	0.833	3.832	PPF2	0.805	34.660
PCE5	0.898	4.526	PPF3	0.817	27.630
			PPF4	0.795	36.597
			PPF5	0.822	28.062
“Perceived Income Change (PIC)” “Cronbach’s α = .862; CR = .874; AVE = 0.643”			“Consumption Decision (CD)”“Cronbach’s α = .868; CR = .876; AVE= 0.717”
PIC1	0.839	34.881	CD1	0.903	71.732
PIC2	0.782	26.900	CD2	0.792	27.421
PIC3	0.748	19.667	CD3	0.843	44.529
PIC4	0.857	45.482	CD4	0.844	40.442
PIC5	0.779	22.567

According to the recommendations made by Fornell and Larcker (1981), the square root of AVE for each construct should be greater than any correlation estimate to assess the discriminant validity. According to Table 4, each construct’s square root of AVE (shown on the diagonal) was higher than the construct’s correlation with every other construct. Furthermore, according to F. Hair et al. (2020), discriminant validity is ensured if the shared variance between a pair of constructs exceeds their AVE. The results in Table 4 indicate that all shared variance is greater than the individual AVE. Thus, the analysis reveals that each latent variable was separate and measured its own properties.

OPEN IN VIEWER

Table 4.

Fornell- Larcker and the Square Root of AVE.

Construct	CD	PCE	PIC	PPC
CD	0.847
PCE	−0.082	0.856
PIC	−0.433	0.032	0.802
PPC	−0.532	−0.104	0.033	0.800

Multicollinearity was detected using the variance inflation factor (VIF) test. All variables recorded values range from 1.73 to 2.880, which are below the recommended cut-off of 3.0 mentioned by F. Hair et al. (2020). Thus, the investigation passed the multicollinearity assumption.

Structural Model

The structural model assessment in PLS-SEM comprises testing hypotheses and examining the independent variables’ predictive relevance (Q²), effect size (f²), and variance explained (R²) with respect to the dependent constructs. The model explained 47% of the variance in the consumption decision (CD) variable (R² = 0.47), which reflects a moderate level of explanatory power (F. Hair et al., 2020), see Figure 2.

OPEN IN VIEWER

Figure 2.

The structural model.

Predictive relevance (Q²) was assessed using the blindfolding procedure (Henseler et al., 2009). The results showed that the Q² for the dependent variable CD was above zero (Q² = 0.443), confirming that the model has good predictive relevance.

Cohen (2013) categorized f² values of 0.02, 0.15, and 0.35 as small, medium, and large, respectively. The f² values of the independent variables on the dependent variable are low and large (Table 6).

Finally, F. Hair et al. (2020) recommended key model fit indices for PLS-SEM. Table 5 demonstrates that all fit indices within the research model satisfied the established thresholds, indicating that the data appropriately conform to the proposed model.

OPEN IN VIEWER

Table 5.

Indices of Model Fit.

Fitness criteria	Description	Value	Recommended value
d_ULS	Euclidean discrepancy	0.501	<0.95
d_G	Geodesic discrepancy	0.187	<0.95
NFI	Normed fit index	0.911	>0.90
SRMR	Standardized difference between observed & predicted correlations	0.057	<0.80

Hypothesis Testing

Figure 2 and Table 6 present the standardized path coefficients (β), SD, t-values, p-values, and hypothesis support. The results show that Perceived Currency Exchange Rate (β = −.124, p < .05), Perceived Income Change (β = −.412, p < .01), and Perception of Price Change (β = −.531, p < .01) have a significant negative effect on the consumption decision. As a result, H1, H2, and H3 hypotheses are supported.

OPEN IN VIEWER

Table 6.

Hypothesis Testing and Path Coefficients - Direct Effect.

Hypothesis	Relationship	Path coefficient	SD	t-value	p-value	f 2	Result
H1	PPC→ CD	− 0.531	0.045	8.534	.000	0.526	Accepted
H2	PIC → CD	− 0.412	0.049	8.437	.000	0.319	Accepted
H3	PCE → CD	−0.124	0.057	2.186	.029	0.029	Accepted

Moderating Effect

To analyze the moderating role of work sector and income, Multi-Group Analysis (MGA) was used since they are qualitative moderators with two categorical levels (public and private) and three categorical levels (low, middle, and high). The sample was split into two non-overlapping groups for the work sector variable. Three non-overlapping groups were created from the sample for the income variable. As suggested by F. Hair et al. (2020), Henseler’s MGA and permutation tests were used to compare the path coefficients between groups. This is a non-parametric significance. When the difference in path coefficients between groups was statistically significant (p < .05), moderation was deemed significant.

Before conducting the Multi-Group Analysis (MGA), we tested measurement invariance through MICOM method. The findings revealed partial measurement invariance, thereby ascertaining that comparisons across income categories were valid. The number of respondents within each group was adequate for conducting MGA for different income levels (Low: 83, Middle: 77, High: 54), as well as sectors of work (Private: 118, Public: 96), meeting the requirements put forth by F. Hair et al. (2020). While conducting MGA, 5,000 bootstrap samples were used to increase its stability and robustness regarding differences across various groups. Results of the Multi-Group Analysis (MGA) Across Income Levels

To establish whether income moderated the structural relationships, the sample was divided into three non-overlapping categories, including low, middle, and high. The Multi-Group Analysis (MGA) was conducted through the permutation tests in a manner outlined in by F. Hair et al. (2020). Table 7 shows the outcome of the parameter estimations, including the contrasts among the three categories. Each p-value is higher than .050. There are no statistically significant differences in the low, middle, or high categories. More specifically:

OPEN IN VIEWER

Table 7.

Group Analysis Due to Income.

Hypothesis*	Low income (β)	Middle income (β)	High income (β)	Low vs middle (diff)	p-value	Low vs high (diff)	p-value	Middle vs high (diff)	p-value	Supported?
H 1.1 PPC → CD	−.042	−.143	.019	0.101	.178	−0.061	.066	−0.162	.287	No
H 2.1 PIC → CD	−.205	−.175	−.188	−0.030	.616	−0.017	.556	0.013	.438	No
H 3.1 PCE → CD	.601	.658	.578	−0.057	.771	0.023	.834	0.080	.609	No

*

“MGA based on Henseler’s MGA procedure with 5,000 bootstrap samples; significance at p < .05.” (Henseler et al., 2016).

Results of the Multi-Group Analysis (MGA) Across Work Sector Levels

As shown in Table 8, only one relationship is significantly moderated by work sector. Specifically, the effect of PPC on CD was significantly stronger in the private sector (β = .658) than in the public sector group (β = .578), and the difference between the two path coefficients was statistically significant (Diff = 0.080, p = .003). This result supports the corresponding moderation hypothesis, indicating that work sector meaningfully influences the strength of the perceived price change (PPC) and consumption decision (CD) relationship. In contrast, no significant sector-based differences were found for the paths PIC to CD (Diff = 0.012, p = .911) or from PCE to CD (Diff = –0.162, p = .153); therefore, these results indicate that work sector does not moderate the relationship between each of perceived income change (PIC) and perceived currency exchange rate (PCE) and consumption decision (CD).

OPEN IN VIEWER

Table 8.

Group Analysis Due to Work Sector.

Hypothesis	Private sector (β)	Public sector (β)	Private vs public (Diff)	p-value	Supported?
H 1.2: PPC → CD	−.143	.019	−0.162	.153	No
H 2.2: PIC → CD	−.175	−.188	0.012	.911	No
H 3.2: PCE → CD	.658	.578	0.080	.003*	Yes

*

“MGA based on Henseler’s MGA procedure with 5,000 bootstrap samples; significance at p < .05.”