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Support and Resistance: Fully Explained

By Leo Smigel

Updated on October 13, 2023

Support and resistance lines are technical analysis tools predicting where an asset’s price will tend to stop and reverse. Without breaking through, multiple touches of the resistance area, often accompanied by high volume, denote these levels.

The concept of support and resistance is a significant element in technical analysis. While the basic idea of support and resistance is simple, it’s essential to go deeper.

This post will break down the many support and resistance elements straightforwardly.

What Are Support and Resistance?

Let’s begin by defining both  support and resistance  in more detail. After that, we’ll then review charts to help visualize support and resistance in action.

Price support occurs when a surplus of buying activity occurs when an asset’s price drops to a particular area. This buying activity causes the price to move back up and away from the support level.

Resistance is the opposite of support. Resistance levels are areas where prices fall due to overwhelming selling pressure.

Support and resistance levels occur due to large institutions buying and selling securities at their target buy and sell levels.

Breakthrough, Breakdown, and Rejection

Two things can happen when an asset’s price reaches a support or resistance level.

The first is that the price bounces off, or rejects from, the support or resistance area. Sometimes the price bounces almost exactly off of support or resistance lines, while other times, the price may enter a support or resistance zone and then reject.

The second is that the price breaks through the support or resistance level. A break of resistance is called a breakthrough, and a breaking of support is known as a breakdown.

Part of what makes  support and resistance  such a complex concept is that it doesn’t always look the same. There are different ways support and resistance may manifest on a price chart. To see what we mean by this, let’s look at a few examples.

The above shows support and resistance as a straight line in blue. You’ll see that the asset price on this chart often falls to the support level but then bounces back up. But eventually, the price does break through the support line.

Notice that it struggles to break through again as the price increases – repeatedly bouncing off the line, which now acts as a resistance level. We’ll discuss how and why a support level may become a resistance level shortly, but the key takeaway here is that it’s possible for the same line on a price chart to be both a support level and resistance level.

But, as we see in the chart below, it’s also possible for support and resistance to present itself at multiple levels.

You’ll see the support and resistance levels creating a  ranging  trading channel in the chart above. When this is the case, the resistance level makes the upper level of the trading channel, and the support level creates the bottom level.

The asset’s price oscillates between these two levels, typically bouncing off but occasionally breaking through support and resistance. The critical thing to recognize is that a price channel contains price action between two parallel lines.

Prices can also trend in a channel . In this case, support and resistance are moving up (uptrend) or down (downtrend) in parallel while rejecting from support and resistance.

These three examples are not the only ways you’ll see support and resistance manifest in a chart. Still, they are some of the most common and should give you an intuition on what to look for when analyzing charts for support and resistance.

Why Support and Resistance Levels Exist

Institutional investors and traders determine support and resistance levels for most securities. Let’s back up this assertion.

Roughly five million shares of Amazon exchange hands each day. At 3,250 per share, that’s 16.25 billion per day. Institutions hold a tremendous amount of shares.

These aren’t retail numbers.

And while support and resistance may manifest themselves on a chart in many ways, they only exist for two: fundamental and psychological.

Fundamental Causes of Support & Resistance

Large institutions don’t buy securities without doing a lot of research beforehand — and you shouldn’t either. Institutions value companies. They have target buy and sell prices for every security they hold and on their buy list.

When a stock on their buy list hits their buy price, they buy; when a position hits their sell target, they sell. Easy enough, right?

Not exactly — The transaction volume institutions require is massive. The size of their positions leads to a unique challenge:

Institutions must buy or sell large volumes of shares without moving the market too much, causing  slippage  or tipping the market off and being front-run.

To remedy these two challenges, institutions buy and sell shares over many weeks or months at their target levels. Understanding this makes it easy to see why there are support and resistance at these price levels.

This is also why the stock market goes up like an elevator and down like an escalator. Institutional buying is a slow and steady process, but selling due to de-risking and deleveraging is not.

The above fear-driven sell-off also brings us to the second reason support and resistance levels exist.

Psychological Reasons for Support & Resistance

An Amazon.com search lists 908 books on “Trading Psychology”. My favorite book on the matter is  Thinking Fast and Slow by Daniel Kahneman .

I’m afraid I disagree with most reasons touted for  the psychology behind support and resistance  for larger stocks; however, these emotions are genuine from the lens of an individual trader.

As I’ve said previously, the institutional trader at the margin determines most securities’ prices and the support and resistance levels. These institutions have rigorous processes around their buying and selling and only divert from these processes when risk becomes intolerable, such as during the initial phases of the COVID-19 crisis.

While almost all traders suffer from psychological biases such as  loss aversion , most of these don’t occur at the institutional level. But there are instances where psychological factors, such as the  Fundamental Strength and the 52-Week High Anchoring Effect , come into play.

Analysts put prices targets on companies, and those targets are often affected by other analyst valuations and historical price action. These anchoring biases strengthen support and resistance at these levels.

When Support and Resistance Switch

When the price reaches a line of support or resistance, the price can either bounce off the line or break through it. When the price breaks through, the role of the two lines reverses. If a line supported price, it’s now resistance, and if it was resistance, it’s now support.

A great example of this in action is the first price chart shown earlier, displayed again for convenience.

Empowered now with the fundamental understanding of institutional trading and transaction volume, you should now understand why support and resistance flip, and vice versa:

Institutional traders have to transact with other institutions

This leads to resistance (selling activity) turning into support (buying activity) and vice versa.

How to Determine Support and Resistance

There are multiple ways to draw support and resistance lines on a price chart. We’ll cover the most common below.

Support & Resistance Using Pivots

Pivot highs and lows are the most direct potential support and resistance areas to identify. You can draw horizontal rays at pivot highs and lows (using the candle wicks) or let TradingView.com do it by adding the Pivot HL indicator.

And just because these levels are simple to identify doesn’t mean they are ineffective. It’s quite the opposite. The clarity of these support and resistance areas makes them more effective.

Support & Resistance Using Levels

You can use the eyeball method or once again use one of the many TradingView indicators to identify support and resistance levels.

Support & Resistance Using Trendlines

Trendlines act as potential support and resistance areas. To draw a resistance trendline, connect at least two highs without having any highs cross above the resistance trendline. To create a support trendline, connect multiple lows without any low crossing the line. Once again, TradingView comes to the rescue with a trendline indicator.

Support & Resistance Using Moving Averages

Many traders use moving averages as potential support and resistance areas.

Lower-timeframe traders use the 200-day simple moving average (SMA) to determine the market regime. Paul Tudor Jones says the  200-day moving average of closing prices is his critical indicator .

Shorter-term traders frequently use the 12/26 period exponential moving averages (EMAs) as potential support and resistance areas. Once again, your charting or trading platform will provide you with these.

Notice how the Shopify hourly chart respects the 12-period EMA on multiple instances.

Keep in mind that humans are pattern recognition machines. We often see patterns where none exist — in other words, be careful when playing off potential support and resistance areas. Your eyes may be playing tricks on you.

Other Support & Resistance Elements

Many factors come into play when determining the strength of a support or resistance level. Here are five other factors to consider when analyzing potential support and resistance zones.

As with almost any technical analysis tool, time plays an important role. The more time has passed since establishing a support or resistance level, the more likely it’s no longer relevant.

If an institution was accumulating shares at a particular price area finds a better place to put their money to work, that price area will no longer act as support.

2. Number of Touches

Another popular signal traders look for when identifying support and resistance levels is the number of touches. The common wisdom is that the more times the price has bounced off instead of broken through a support or resistance level, the stronger that level is believed to be

I’m afraid I have to disagree with this.

If an institution is accumulating a significant position, after multiple touches, its position will fill. Instead, I look for two tests with solid rejection, and I get nervous after four or more touches.

3. Preceding Price Movement

In general, support and resistance levels are considered more significant after a steep advance or decline. This is because there are more enthusiasm and momentum behind steep increases or decreases in price. Therefore, the support or resistance level must be reasonably healthy for the price to bounce back.

I think of it this way. When stocks are volatile, it’s the price discovery process in action. Prominent players are trying to agree on the value of an asset. If there are extreme moves, it’s due to uncertainty — and as new information becomes available, these analysts typically become more confident over time instead of less.

The final signal of support and resistance strength we’ll look at is volume. Volume works similarly to preceding price movement as a signal since it also helps convey the momentum behind a trend, but there’s another reason volume is a valuable signal. Higher volume levels mean more buying and selling occurs, leading to potentially better areas of support and resistance.

Let me provide an example. Imagine there’s a ton of short-sellers shorting Apple at 100. The price moves to 95 and then starts to push back on the shorts. They’ll likely cover around the $100 area to not take a loss. More short positions equal more short covering, and more covering leads to higher price rejection at the level.

5. Round Numbers

Once you begin spending more time with support and resistance levels, you’ll likely notice that levels end up at round numbers a disproportionate amount of the time. For example, around 50 or 100. Why is this?

It all goes back to the psychology behind support and resistance. If people were rational, there would be no correlation between the support and resistance and round numbers, but we’re not!

Savvy traders know this and even call it out directly when discussing support levels such as “potential support at $100 psychological”.

Advanced Support & Resistance Techniques

There are also a few lesser-known but valuable ways to use support and resistance when technical trading.

Pivot Points

Pivot points originating from floor traders in the pits are a leading technical indicator that attempts to estimate future support and resistance levels based on past and current prices.

While there are multiple flavors of  pivot points , the standard calculation uses the average of the high, low, and previous day’s closing price.

Beyond the pivot point itself, the pivot point indicator includes multiple support and resistance levels. Usually, at least two support and resistance levels are displayed, known as S1/S2 and R1/R2, respectively.

Again, your charting platform will come to the rescue. You can see Pivot Points charted below.

I find that pivot points have some predictive capability and help determine bias for market direction. While shown daily for display purposes, I use pivot points in a few of my algorithmic trading strategies .

Fibonacci Retracements

Another popular support and resistance indicator is  Fibonacci Retracement .

A retracement is a short-term price correction during a larger upward or downward trend that does not indicate a reversal of the more significant trend. The goal of retracements is to get you into a trade before continuing the move.

Fibonacci retracement shows how much a move corrects from its extremes. To chart fib retracements, select the lowest low in an uptrend, and connect it to the highest high. You would connect the highest high to the lowest low in a downtrend. Those new to this indicator think of it as the amount the price pulls back before likely continuing the move.

Most charting software includes the following support and resistance levels in their Fibonacci retracement tool:

These eight levels often act as support and resistance for the asset’s price.

In the above chart, the dashed line is the uptrend line between the two extremes (the low and the high). You’ll see that there are eight lines (including the gray line at the bottom). You’ll also see that price discovery occurs at 23.6% and the 38.2% retracement levels before continuing with the uptrend.

Also, between the 61.8% and the 65% fib retracement level is called the Fibonacci Golden Pocket and is the most respected reversal zone when using retracement analysis.

Trading Using Support and Resistance Levels

Now that we’ve covered much of the theoretical aspect of support and resistance, we can now look at how support and resistance can inform trading decisions. As with any indicator, there are many different ways to use support and resistance, but we’ll stick with the three basic ways support and resistance can inform trading.

The first way to use support and resistance is to enter into a position when you think a reversal will occur. For example, the stock price has dropped, and it has now reached a resistance area. The indication is that the price will bounce off the resistance level and begin increasing.

Another option is a breakout. We’ll give an example of a breakdown when a stock breaks to the downside. If there is little to no support past the support area, and the support level was touched multiple times, soaking up the institutional buy volume, shorting a breakdown may be a good play.

Finally, you might use support and resistance lines to place stop-losses. In the above instances, if you’re wrong, a stop loss near the support area will prevent the trade from going too far in the wrong direction if your thesis is incorrect.

What Timeframe Is Best for Support and Resistance?

Support and resistance levels work on all timeframes. Like trends, support and resistance on lower timeframes are stronger than support and resistance on higher timeframes. This is due to the fundamentals driving longer-term levels and psychological factors causing short-term support and resistance.

Think about the low of the Covid-19 crash; that level is much more significant than the low of last week.

The Bottom Line

Support and resistance are critical elements of technical analysis. Support and resistance levels are caused by fundamental and technical reasons, usually due to institutional activity. There are multiple ways to draw support and resistance areas and trade using them.

Understanding support and resistance levels can help increase your returns and limit your downside, so it’s essential to understand them fully.

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The Psychology of Support and Resistance Zones

Technical analysts use support and resistance levels to identify price points on a chart where the probabilities favor a pause, or reversal , of a prevailing trend. Support occurs where a downtrend is expected to pause, due to a concentration of demand. Resistance occurs where an uptrend is expected to pause temporarily, due to a concentration of supply.

These levels, while they may appear arbitrary at first sight, are based on market sentiment and anchoring . Here, we examine how support and resistance zones are largely shaped by human emotion and psychology.

Key Takeaways

• The concepts of trading level support and resistance important price levels on charts that tend to act as barriers, preventing the price of an asset from getting pushed in a certain direction.
• Market psychology plays a major role as traders and investors remember the past and react to changing conditions to anticipate future market movement.
• Shifting zones of resistance and support can be revealed by understanding the sentiment and emotion of individual market actors and how that aggregates up to the market.

The Psychology Behind Support and Resistance

In a given financial market, there are typically three types of participants at any given price level:

• Those who are long and waiting for the price to rise
• Those who are short and hoping the price will fall
• Those who have not yet decided which way to trade and remain on the sidelines

As the price rises from a support level, the traders who are long are happy and may consider adding to their positions if the price drops back down to the same support level. The traders who are short in this situation are beginning to question their positions and may buy to cover (exit the position) to get out at, or near, breakeven if the price reaches the support level again. The traders who did not enter the market previously at this price level may be ready to pounce and go long if the price comes back down to the support level. In essence, a large number of traders may be eagerly waiting to buy at this level, adding to its strength as an area of support. If all these participants do buy at this level, the price will likely rebound from the support once again.

Price can, however, fall right through the support level. As price continues to drop, traders will quickly realize that the support level is not holding. The long traders may wait for the price to climb back up to the previous support level, which will now act as resistance, to exit their trades in the hopes of limiting their losses. The short traders are now happy and may consider adding to their positions if the price revisits the price level. Lastly, the traders who did not enter the market yet may go short  if the price comes back to the previous support level, in anticipation of prices dropping further. Again, a large number of traders may be ready to make a move at this level, but now instead of buying, they will be selling. This same behavior can be witnessed in reverse with traders' reactions to resistance levels.

These examples illustrate an important technical analysis principle: That which previously acted as support will eventually become resistant. Conversely, levels that formed resistance will act as support, once the price breaks above the resistance level. This can be seen on any chart or any time frame. Though investors commonly refer to daily charts to determine areas of support and resistance, smaller time frames are also used, especially by short-term traders, to establish these areas.

The chart below, for example, indicates the weekly candlestick price chart of Montreal Trucking Company. As the blue horizontal line shows, there is resistance at $15, preventing the price from rising above that level. There also appears to be support at $7.

Image by Julie Bang © Investopedia 2019

Support and resistance zones are not only seen at particular prices; they can vary along with upward or downward trendlines .

Fear, greed , and herd instinct are terms that crop up when discussing the psychology behind financial markets. This is because human emotions play a part in the price action observed in markets. A price chart, then, can be thought of as a timeline of optimism and pessimism. Price charts illustrate how market participants react to changing future expectations.

Fear and greed, for example, are seen in the market participants' behavior outlined above. As price falls back to a support level, the traders who are already long will add to positions to make more money. Meanwhile, the traders who are short will buy to cover, because they are afraid of losing money. Herd instinct is also demonstrated in this example as traders tend to congregate near these support and resistance levels, further strengthening them.

Traders can also collectively experience a conditioned response, of sorts due to what is known as anchoring . Anchoring is a heuristic revealed by behavioral finance that describes the subconscious use of irrelevant information, such as the purchase price of a security, as a fixed reference point (or anchor) for making subsequent decisions about that security.  Therefore, if a resistance or support level has been established in the past, it can create a shared anchor where those same levels will be met with resistance or support in the future. The figure below shows a daily chart of pharmaceutical maker Eli Lilly (NYSE: LLY ), indicating two previous price peaks formed resistance that reflects a significant uptrend. Indeed, we see support again and again when the price drops down near $33.50, since that price level provided strong support on at least three previous occasions.

Image by Sabrina Jiang © Investopedia 2021

Other support and resistance levels that are influenced by human emotion include round numbers (since they are easy to remember), 52-week highs and lows, and historic events such as new market highs. Traders and investors tend to gravitate to these psychological price levels for several reasons. One is that these prices have been significant in the past and traders know they are likely to be again. Market participants often gauge future expectations based on what has happened in the past; if a support level worked in the past, the trader may assume that it will provide solid support again.

Another reason that emotional price levels are significant is they attract a lot of attention and create anticipation, which can lead to increased volume as more traders get ready to respond. New market highs, for example, create a buzz of excitement as traders imagine price going higher, with no previous resistance levels to slow it down. As the bulls take charge, the euphoria can result in a significant push above the previous high, typically with increased market participation, until the enthusiasm wanes and a new resistance level is established.

What are support and resistance levels and how are they formed?

A support level can be thought of as the floor and a resistance level a ceiling for prices in a market. Prices fall and test the support level, which will either "hold," and the price will bounce back up, or the support level will be violated, and the price will drop through the support and likely continue lower to the next support level. These levels are formed, in part, due to market psychology that establishes bullish sentiment at the support and bearish at the resistance.

Why are these levels important for technical traders?

Traders use support and resistance levels to plan entry and exit points for trades. If the price action on a chart breaches the support levels, it is seen as an opportunity to buy in or take a short position, depending on what the trader sees from other indicators. If the breach occurs on an uptrend, it may even be a sign of a  reversal .

How does anchoring play into support & resistance levels?

Anchoring takes an arbitrary value and assigns meaning to it for traders. A previously established level of support or resistance may therefore become an anchor at which points future resistance or support will be observed - even though these points may not reflect any fundamentals. Likewise, round numbers such as $1,000 or $25,000 may serve as support or resistance levels, not because they are fundamentally-driven, but are symbolically meaningful as psychological anchors. As these levels are breached, traders may adjust their anchors accordingly.

Support and resistance zones are utilized by technical analysts to study past prices and predict future market moves. These zones can be drawn using simple technical analysis tools, like horizontal lines or up/down trendlines, or by applying more advanced indicators, such as Fibonacci retracements . Market psychology plays a major role in a given instrument's price movement as traders and investors remember the past, react to changing conditions and anticipate future market movement. Knowing what the market is thinking is the best way to determine what it will do next.

Federal Reserve Bank of St. Louis. " The Anchoring Effect ."

• Behavioral Finance: Biases, Emotions and Financial Behavior 1 of 27
• An Introduction to Behavioral Finance 2 of 27
• Understanding Investor Behavior 3 of 27
• Market Psychology: What Is It and Predictions 4 of 27
• How the Power of the Masses Drives the Market 5 of 27
• How to Read the Psychological State of the Market with Technical Indicators 6 of 27
• Herd Instinct: Definition, Stock Market Examples, & How to Avoid 7 of 27
• Financial Markets: When Fear and Greed Take Over 8 of 27
• 4 Behavioral Biases and How to Avoid Them 9 of 27
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• Let Your Profits Run: Overview, History, Example 17 of 27
• The Art of Cutting Your Losses 18 of 27
• Positive Feedback: What it is, How it Works 19 of 27
• Loss Aversion: Definition, Risks in Trading, and How to Minimize 20 of 27
• Psychological Coping Strategies for Handling Losses 21 of 27
• Regret Avoidance: Meaning, Prevention, Market Crashes 22 of 27
• Technical Analysis That Indicates Market Psychology 23 of 27
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Title: evidence and behaviour of support and resistance levels in financial time series.

Abstract: This paper investigates the phenomenon of support and resistance levels (SR levels) in financial time series, which act as temporary price barriers that reverses price trends. We develop a heuristic discovery algorithm for this purpose, to discover and evaluate SR levels for intraday price series. Our simple approach discovers SR levels which are able to reverse price trends statistically significantly. Asset price entering SR levels with higher number of price bounces before are more likely to bounce on such SR levels again. We also show that the decay aspect of the discovered SR levels as decreasing probability of price bounce over time. We conclude SR levels are features in financial time series are not explained simply by AR(1) processes, stationary or otherwise; and that they contribute to the temporary predictability and stationarity of the investigated price series.

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The Psychology of Resistance to Change: The Antidotal Effect of Organizational Justice, Support and Leader-Member Exchange

Nabeel rehman.

1 School of Accountancy & Finance, The University of Lahore, Lahore, Pakistan

Asif Mahmood

2 Department of Business Studies, Namal Institute, Mianwali, Pakistan

Muhammad Ibtasam

3 Institute of Business & Management, University of Engineering and Technology, Lahore, Pakistan

Shah Ali Murtaza

4 Institute of Management and Organizational Sciences, University of Debrecen, Debrecen, Hungary

Naveed Iqbal

5 Department of Business Administration, University of the Punjab, Lahore, Pakistan

Edina Molnár

Associated data.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

In today’s business environment, the survival and sustenance of any organization depend upon its ability to introduce a successful change. However, in implementing a change, one of the biggest problems an organization faces is resistance from its employees. The current paper addresses this problem by examining the role of organizational justice dimensions in coping with the resistance to change through the intervening role of perceived organizational support (POS), leader-member exchange (LMX), and readiness for change (RFC) in a sequential framework. Data of 372 employees have been collected from the banking industry of Pakistan. The results obtained through the Partial Least Squares- Structural Equation Modeling (PLS-SEM) approach using SmartPLS suggest that distributive justice, procedural justice, and interactional justice play a critical role in lowering the resistance to change through POS, LMX, and RFC, contributing significantly to the theory and practice. Furthermore, this study also discusses recommendations for future research and limitations associated with this research work.

Introduction

Since today’s business environment frequently confronts changing market trends, globalization, and technological advancements, firms need to continuously revisit their processes, strategies, and culture ( Cummings and Worley, 2009 ; Petrou et al., 2018 ). Over time, a review of change management has acknowledged the importance of organizational change ( Burnes and Jackson, 2011 ). Consistency in introducing change has arguably become a key to survival ( McKinsey Company, 2008 ; Burnes, 2009 ). Therefore, organizations are under constant pressure to initiate and execute organizational change ( Shah, 2011 ). In developing countries like Pakistan, the financial services industry also faces competitive challenges for their survival and sustenance. Several banks have gone through enormous changes like mergers and acquisitions, adopting new technology, reforms in business operations, and changes in human resource policies ( Osei-Bonsu, 2014 ). Hence, the financial services institutions must consider introducing change from time to time to stay in business, meet market standards, and maintain a competitive edge.

However, change processes are pretty challenging, and most organizations struggle to execute change strategies ( Burnes, 2009 ). The literature suggests that more than two-thirds of change implementation efforts fail ( Beer and Nohria, 2000 ; Meaney and Pung, 2008 ). One of the most critical failures to change is employees’ attitudes toward change ( Ahmad and Cheng, 2018 ). Unaware of the potential benefits associated with the organizational change, employees often develop a sense of fear, and perceive the introduction of change as an unfair act ( Ford and Ford, 2010 ). Therefore, they develop negative attitudes and exhibit adverse reactions toward change—a phenomenon known as resistance to change (RTC) ( Folger and Konovsky, 1989 ). Thus, shaping the employees’ resistive attitudes is considered vital for success in implementing change. A recent study by Banguntopo (2018) has drawn our attention to the factors influencing RTC and suggested that the employees’ readiness for change (RFC) greatly influences RTC by transforming their attitudes in favor of the change. Some early research has established that the employees’ beliefs, attitudes, and intentions toward organizational change determine their state of RFC ( Armenakis et al., 1993 ), furthermore, the RFC depends upon the employees’ behaviors and emotions toward change ( Oreg, 2003 ).

Based on the above discussion, it can be argued that the success of change execution effort largely depends on shaping employee attitudes toward change, i.e., coping with RTC by making the employees ready for change. Currently, considerable research work acknowledges the importance of change management in employee response, and the factors influencing those responses. Some recent studies have highlighted the importance of organizational justice practices in shaping employee response toward change ( Soenen and Melkonian, 2017 ). It is believed that the perception of organizational justice greatly influences the beliefs, attitudes, intentions, behaviors, and emotions of employees. For instance, if employees perceive that the management does justice in outcomes, rewards distribution (distributive justice), is honest in its procedures and policies (procedural justice) regarding outcomes and rewards, and is fair in the communication process regarding distributions and procedures (interactional justice), they are more likely to show the RFC. In addition, if employees perceive that their organization is supportive, they reciprocate their support in response—a phenomenon known as perceived organizational support (POS), thereby developing a positive attitude in the context of organizational change ( Ciliana and Mansoer, 2008 ). Accordingly, if an employee perceives that his boss treats him well, he will likely be well-motivated, committed, and willing to accept whatever his organization entrusts him—known as Leader-Member Exchange (LMX). Such social exchange relationships can derive change implementation process toward success ( Niehoff et al., 2001 ).

Thus, employee’s justice perception, POS and LMX hold sheer importance in the context of RFC and RTC, however, a review of the literature suggests that there is a dearth of knowledge in this area. First, the previous studies have focused on general change antecedents such as employee commitment, employee beliefs, and job satisfaction ( Madsen et al., 2005 ; Bernerth et al., 2007 ). Second, most researchers have considered organizational justice as a whole, but less attention was given to the dimensions of justice in the context of organizational change ( Arnéguy et al., 2018 ). Third, the underlying mechanism in a justice-change relationship involving social exchange links is yet to be explored ( Nova and Hadiyan, 2017 ). In fact, very limited studies have examined the justice-change relationship comprehensively ( Shah, 2011 ; Arnéguy et al., 2018 ; Mangundjaya, 2020 ). Moreover, no research has explored the intervening roles of POS, LMX, and RFC between the relationship of justice dimensions and RTC in sequential order.

In this backdrop, the present study contributes by developing the underlying mechanisms that examine the impact of justice dimensions (distributive justice, procedural justice, and interactional justice) on resistance to change (RTC) by empirically analyzing the intervening role of POS, LMX, and RFC in sequence. Secondly, this study contributes to the literature by discussing the results from a theoretical as well as a practical viewpoint to broaden the knowledge base of management and future researchers.

Background and Hypotheses Development

Resistance to change: why is it of concern.

Studies have shown that the success of organizational change primarily relies on the attitude and response of their employees toward change ( Ahmad and Cheng, 2018 ). As a matter of fact, appropriate transformation in employees’ behavior toward change determines its long-term success. As early response and intention toward change are crucial ( Bayiz Ahmad et al., 2020 ), a large body of research supports the role of employees’ positive attitudes in the success of change ( Herold et al., 2008 ). In contrast, employees’ negative attitudes and responses may prove harmful. The phenomenon of resistance to change (RTC) reflects the negative attitudes and behaviors expressed by the employees during times of organizational change. During the change execution process, the biggest challenge faced by the organizations is how to manage that change, especially to cope with the resistance posed by the employees. The employees either try to slow down the change process or terminate the change effort entirely ( Hughes, 2006 ). Hence, resistance is a leading obstacle in the way of an organization’s efforts for improvement, survival, or adoption of new processes and technology. But most of the time, management does not consider employees’ perception about stress or uncertainty associated with the change process, which becomes a major cause of resistance, and may lead the change implementation effort to failure ( Ahmad and Cheng, 2018 ). Hence, to make change process a success, the management must not see resistance as a mere obstacle but an opportunity to learn and subsequently reduce it ( Strebel, 1996 ).

Similarly, it has been found that despite circumstances push for a change, the employees are likely to show resistance by sticking to the notion that they do not need the change ( Robins et al., 2011 ). Robbins and Galperin (2010) state that resisting organizational change is in the nature of employees because they often find it uncomfortable to leave their comfort zone. Employees generally get stressed out due to the fear of the unknown. Yue (2008) argued that the greatest challenge that an organization faces during the change process is to deal with the resistive reaction of employees. For many years, it has been believed that the resistance to change is a counterproductive element that reflects employees’ individual and collective negative responses ( Collinson, 1994 ; King and Anderson, 1995 ; Waddell and Sohal, 1998 ; Trader-Leigh, 2002 ). Since employee resistance is a factor that significantly contributes to the failure of a change ( Sirkin et al., 2005 ), serious research efforts have been undertaken to identify predictors of the resistance, individual and collective perceptions about change, their influence on the resistance, benefits, and threats associated with change ( Erwin and Garman, 2010 ; Colquitt et al., 2013 ).

The positive perception of justice is among the coping mechanisms of resistance, as it has been argued that the distribution of resources, processes, and procedures influence the employees’ attitude and behavior in the context of change ( Ford et al., 2008 ). In this regard, our study extends the literature by highlighting the role of organizational justice in coping with employee resistance through the lens of social exchange relationships, i.e., POS and LMX. Here, it is argued that if management observes fairness and justice in distribution procedures and processes, a message of fairness would be delivered throughout the organization, which will shape the employees’ perception in enforcing openness for the change. The following section hypothesizes the relationships between dimensions of organizational justice, POS, LMX, employee RFC, and resistance to change.

Distributive Justice and Perceived Organizational Support

Perceived Organizational Support (POS) refers to the perception of employees about how their organization appraises their efforts, and takes care of their welfare, social needs, and career development. Generally, POS is about how an organization extends its support to its employees, and this organization-oriented support enhances their commitment level in return ( Baran et al., 2012 ). POS draws its roots from social exchange theory which suggests that it is a mutual relationship between an organization and its employees, for instance, if an employee perceives that his organization supports him, he will formulate a strong connection with his organization, and participate in extra-role activities to realize the organizational goals. POS can be enhanced through organizational justice, growth opportunities, and support from supervisors and coworkers ( Fu and Lihua, 2012 ; Cheung, 2013 ; Jacobs et al., 2014 ).

Previously, there has been a rise in studies focusing on the dimensions of organizational justice from a social exchange perspective. The scholars have suggested its impact in determining the quality of social exchange relationships ( Colquitt et al., 2013 ). Among dimensions of organizational justice, distributive justice derives its roots from equity theory ( Adams, 1965 ), and refers to an employee’s perception of the distribution of organizational rewards and outcomes ( Niehoff and Moorman, 1993 ). The employees who believe that their employer does justice in the distribution of outcomes are motivated, committed, and loyal toward their organization. On the contrary, if employees perceive that their employer distributes injustice, they are likely to change their attitude, lower their morale, and may not participate in job activities as desired ( Greenberg, 1993 ). In a study, Fasolo (1995) supported this argument, and another study explicitly demonstrated that distributive justice is related to POS ( Shore and Shore, 1995 ; Kurtessis et al., 2017 ). The extent to which an organization takes care of its employees determines employee perception about the organizational support ( Loi et al., 2006 ; Fu and Lihua, 2012 ). Therefore, it is argued that the employees who perceive their organization has been fair in distributing pay-offs are more likely to contribute toward their organization effectively. The employees with the perception of fair distributive justice show more commitment to their organization, and support it in achieving strategic goals. Based on these arguments, it is hypothesized that:

H 1a : Distributive Justice positively affects Perceived Organizational Support.

Distributive Justice and Leader-Member Exchange

Leader-member exchange (LMX) refers to the “exchange outcomes” realized from relationships between an employee and manager, follower and leader, or worker and supervisor ( Liden et al., 1993 ; Scandura, 1999 ). Here, the word “exchange” indicates that this is a two-way relationship with mutual outcomes. The quality of the relationship between a manager and his employee, and the length of the period of such relationship determine the interpersonal understanding of both parties. If the quality of such a relationship is high, there will be more trust, respect, mutual understanding, and information exchange between the parties. While on the other hand, a low-quality relationship results in a decreased trust level, formality in employee-manager relations, and one-sided influence and manipulation ( Bauer and Green, 1996 ).

While going through the literature, we find that there has been a focus on studies about relationships between dimensions of organizational justice and LMX ( Scandura, 1999 ; Brown et al., 2005 ). Drawing upon the level of organizational justice, leaders may develop high-quality relationships with some employees, and low-quality relationships with other employees within the organization. The employees who receive better outcomes, rewards and social benefits from their leaders may develop a high-quality LMX relationship ( Lee et al., 2010 ). Therefore, it is argued that distributive justice affects the quality of the LMX relationship: if there is a positive perception of distributive justice among the employees, there will be a strong LMX relationship. Here, it is proposed that:

H 1b : Distributive Justice Positively affects Leader-Member Exchange.

Procedural Justice and Perceived Organizational Support

An organization provides benefits or outcomes to its employees such as career counseling, promotions, training and other social benefits to win their loyalty. But unfortunately, situations may arise that even all the provided perks may not be enough to induce the desired impact on the behavior of the employees. This may be the case when an organization does not pay attention to the effectiveness of procedures adopted for the distribution. Hence, procedural justice holds equal importance as distributive justice ( DeConinck, 2010 ). Therefore, it is contended that procedural justice has an impact on POS. Several studies have supported the argument ( Loi et al., 2006 ; Stinglhamber et al., 2006 ; Peelle, 2007 ; Fu and Lihua, 2012 ). Thus, if an organization is fair in procedures and policies adopted for the distribution of outcomes, it will create a positive perception among its employees ( Loi et al., 2006 ). Therefore, drawing upon the literature, it is proposed that as fair distributive procedures preserve the rights of employees in terms of organizational justice, it tends to influence employees’ POS positively. Here, it is hypothesized that:

H 2a : Procedural Justice positively affects the Perceived Organizational Support.

Procedural Justice and Leader-Member Exchange

As discussed earlier, an employee with a positive perception of distributive justice tends to form a high-quality LMX relationship. Similarly, his perception of procedures adopted by the organization also matters in determining the quality of LMX. If an employee perceives that the distributive procedures adopted by the organization are justified, he is likely to form a perception that his organization is fair to him. It would help build trust and confidence in his management, ensuing a high-quality LMX. Lee et al. (2010) argued that LMX is related to the dimensions of justice: procedural justice and distributive justice. In some studies, LMX has also been observed to contribute as a moderator in the relationship between procedural justice and distributive justice with specific organizational outcomes ( Piccolo et al., 2008 ). Therefore, deriving from the literature, it is reasoned that procedural justice positively affects the quality of the supervisor-subordinate relationship. Hence, we propose that:

H 2b : Procedural Justice positively affects the Leader-Member Exchange.

Interactional Justice and Perceived Organizational Support

Interactional justice is the third dimension of organizational justice that augments the earlier discussed dimensions of justice. It reflects how an organization treats, interacts, and communicates with its employees during the execution process of procedures and distributions ( Bies and Moag, 1986 ). According to organizational support theory, when employees receive recognition for their contributions, they become more loyal to their organization. So, interactional justice imparts a sense of being influential among the employees, which increases their trust in the management and supervisors. Subsequently, it will arguably enhance the perception of organizational support. Organizations with a strong focus on interactional justice will have improved POS relationships than those without it. Despite its importance, the literature indicates that interactional justice has been mostly ignored in the past ( Cheung and Law, 2008 ; DeConinck, 2010 ). However, in a recent meta-analysis, interactional justice has been found to be positively related to POS ( Kurtessis et al., 2017 ). Hence, based on the literature, it is postulated that employees will find themselves well aware and well communicated with their organization, and show their support for it if interactional justice persists. Thus, it is hypothesized that:

H 3a : Interactional Justice positively affects the Perceived Organizational Support.

Interactional Justice and Leader-Member Exchange

The researchers have determined a positive relationship between overall justice and LMX ( Brown et al., 2005 ). However, few studies have examined the dimensional role of interactional justice in ascertaining the quality of a manager-employee relationship. Interactional justice describes the communication side of organizational justice. As most of the communication between management and employees happens through their immediate bosses, it is argued that the leader’s fair treatment and communication will ultimately strengthen the manager-employee relationship ( Piccolo et al., 2008 ). If a manager is fair to his employees, a strong social exchange relationship is developed between them. It supports the fact that interactional justice has a connection with LMX ( Wayne et al., 2002 ). Therefore, it is argued that if interactional justice exists, there are chances for the development of high-level LMX relationships. Hence it is suggested that:

H 3b : Interactional Justice positively affects Leader-Member Exchange positively.

Perceived Organizational Support and Leader-Member Exchange

POS and LMX are the two leading indicators of social exchange in an organization. POS is organization-oriented whereas, LMX is the leader-oriented approach. In some early studies, researchers suggested that organizational support is seen as help from immediate leaders ( Shore et al., 1994 ). Liden et al. (1997) argued that organizational support augments the supervisor-subordinate relationship. Masterson et al. (2000) suggested that the perception of good organizational support promotes a social exchange relationship between supervisors and subordinates. Later on, it was also corroborated by Kurtessis et al. (2017) . Based on the evidence, it is asserted that if an employee perceives that his organization supports him, he would likely to build trust in management that will promote and strengthen the social exchange relationship. Support from the organization will be seen as support from the senior management. Therefore, we put forward:

H 4 : Perceived Organizational Support affects Leader-Member Exchange positively.

Perceived Organizational Support and Readiness for Change

When employees of an organization feel that their organization treats them fairly, and supports them well, they develop a positive perception ( Eisenberger et al., 1986 ). Therefore, employees with a positive perception of organizational support are more likely to welcome any job-related task assigned to them by their employer. In other words, it is here argued that they will tend to develop a sense of readiness for a change. As the change process involves day to day enforcement of actions, organizational support plays a vital role in imparting change readiness ( Gigliotti et al., 2019 ). According to Eisenberger and Stinglhamber (2011) , the employees will be more loyal to their organization who feel that they have been supported well by their organization, and are more committed to achieving their organizational goals ( Shore and Shore, 1995 ). POS imparts a sense of responsibility in employees for the organization ( Kurtessis et al., 2017 ). It translates into developing a positive attitude and behavior that might be considered necessary for RFC. Positive perception of organizational support encourages the employees to prepare for the change implementation process ( Eby et al., 2000 ; Mitchell et al., 2012 ). Thus, it is propounded here that:

H 5a : Perceived Organizational Support positively affects Readiness for Change.

Perceived Organizational Support and Resistance to Change

An organization’s support for its employees enhances their commitment level ( Rhoades and Eisenberger, 2002 ). Employees offer their services and, in return, expect incentives, rewards, and social benefits ( Armeli et al., 1998 ). If they are provided with the same, their commitment and loyalty level rise. Employees with more POS are more likely to give their best for the organization, and they will find themselves ready for change initiatives. Therefore, it is asserted that if employees are supported well by their organization, they will be less likely to resist the change process. The Employees of any organization willingly participate in the change process when they conceive that change will prove valuable ( Shapiro and Kirkman, 1999 ). Therefore, drawing from the literature, it is argued that the employees are more likely to lower the resistance toward change when they perceive a strong organizational support ( Cummings and Worley, 2009 ). Hence, we advocate that:

H 5b : Perceived Organizational Support is negatively related to Resistance for Change.

Leader-Member Exchange and Readiness for Change

During the change implementation process, the management and employees have to interact daily. So, the quality of subordinate-employee relationships matters a lot in carving the employee’s attitude toward change. Therefore, it can be said that the quality of LMX determines the employee’s intention toward a change initiative. A high-quality LMX relationship among the employees of any organization imparts a sense of loyalty, liking and respect for the leaders because employees in such a relationship are frequently admired for work by their leaders ( Brower et al., 2000 ). The potential rewards of positive behavior development, commitment and trust are associated with a high-level LMX relationship ( Karriker and Williams, 2009 ). Thus, it is argued that LMX supports employee’s RFC. High-quality LMX suggests that the support and trust from the management positively influence employees’ behavioral reactions. Therefore, there are chances that the employees in high-quality LMX relationships develop a positive attitude toward accepting the change ( Eby et al., 2000 ). Therefore, it is inferred that the LMX is strongly related to employee RFC. Consequently, we suggest that:

H 6a : Leader-member exchange positively affects readiness for change.

Leader-Member Exchange and Resistance to Change

While reviewing the literature regarding the social exchange and resistance to change, it can be observed that there is an inverse relationship between high-quality LMX and resistance to change. The employees with a high level of LMX relationship are more optimistic toward change-related outcomes. Therefore, they tend to participate in change-related activities instead of posing a resistance ( Lee et al., 2010 ). Biao and Cheng (2014) have highlighted the importance of the leader-employee relationship in the context of resistance to change. If management practices are directed purposefully to make leader-member relationships better, it will significantly help the organization cope with the resistance during the change process ( Georgalis et al., 2015 ). Therefore, it is argued that mutually beneficial supervisor-subordinate relationships facilitate coping with the resistance to change. Hence, it is hypothesized that:

H 6b : Leader-Member Exchange affects Resistance to Change negatively.

Readiness for Change and Resistance to Change

If employees of an organization exhibit positive attitudes, beliefs, actions and intentions toward implementing the change, they reflect RFC ( French et al., 2005 ). RFC is very facilitating as it holds primary importance in implementing change. The more the employees are ready for an organizational change, the more they believe in positive change outcomes, consequently increasing the chances of success ( Rafferty et al., 2013 ). Simply, if RFC exists, employees are more likely to accept change rather than resisting it. Therefore, it can be said that their level of resistance to change is reduced to the minimum ( Armenakis et al., 1993 ). So, it turns out that the RFC is an effective predictor of lowering the resistance to change. Therefore, it can be hypothesized that:

H 7 : Readiness for change negatively affects the Resistance to change.

Mediating Roles of Perceived Organizational Support and Leader-Member Exchange

It has been suggested in the literature that the quality of the relationship between management and employees is essential in dealing with the resistance to change ( Ford et al., 2008 ). Therefore, organizations must concentrate on the factors predicting such high-quality relationships. The social exchange theory ( Blau, 1964 ) explains the organization-employee relationships, and emphasizes how these relationships can be strengthened. In the organizational context, social exchange is a concept of a mutually beneficial relationship between two parties. Therefore, it was advocated that this theory resides on reciprocity norms ( Aselage and Eisenberger, 2003 ). Based on this theory, it was suggested that employee’s perception about the organizational support (POS) establishes a mutual relationship between employee and his organization ( Mowday et al., 1982 ; Mathieu and Zajac, 1990 ; Meyer and Allen, 1997 ; Rhoades and Eisenberger, 2002 ). The researchers have highlighted the importance of POS as it enhances the employee commitment, strengthen his bond with the organization and provide value, in return of the support they receive from their organization ( Mowday et al., 1982 ; Meyer and Allen, 1997 ; Eisenberger et al., 2001 ). Here, it is asserted that the employees with POS are anticipated more ready for change and, accordingly, will shape their responses in favor of change instead of resisting it.

Furthermore, it is added that during the change implementation process or otherwise, the employee and his supervisor or boss interact regularly. Therefore, the quality of their mutual relationship matters in achieving the desired organizational outcomes. As discussed earlier, LMX is a phenomenon that refers to the exchange relationship between a supervisor and subordinate ( Niehoff et al., 2001 ). If an employee finds his immediate boss, supervisor, or manager as supportive, he will enthusiastically perform the assigned tasks. It is maintained that a high-quality LMX relationship enables the employee to embrace the organizational change, thereby reducing their resistive attitude toward change. Therefore, organizations need to focus on the factors influencing the quality of LMX. In the context of this study, the literature suggests that employees see their immediate supervisors’ support through the lens of their organization’s support ( Shore et al., 1994 ). Suppose the employees perceive that the organization supports them and realize their efforts. In that case, they will likely build strong exchange relationships with their bosses because they interact daily, and any communication regarding rewards, incentives, on-job training and career counseling mostly happens through immediate bosses. So, employees mostly see the manager’s support as their organization’s support ( Kurtessis et al., 2017 ). Here it can be argued that POS augments LMX, which further makes the employees ready for change, ultimately reducing the resistance to change.

One of the legit reasons behind the failure of a change strategy is the fear of employees about the uncertainty of the future events associated with change. Employees’ confidence, emotions, and behaviors need to be shaped to make them ready for change. Soenen and Melkonian (2017) suggested that justice perceptions greatly influence employee responses in the context of change. Breaking it down to the dimensional level, the perceptions about outcome and rewards distributions (Distributive Justice), procedures adopted for such distributions (Procedural Justice), and how well these distributions and procedures are communicated throughout the organizations (Interactional Justice) influence employee attitude toward change. In this study, it has been formulated that the justice-change relationship is indirect, and there are underlying mechanisms that are mediated by POS and LMX. Moreover, distributive, procedural and interactional justice influence employee’s perception of organizational support (POS) ( DeConinck, 2010 ; Fu and Lihua, 2012 ). Hence, based on these arguments, it is stated that if an employee perceives justice in the distribution of outcomes, procedures, and management interactions, he will develop positive perceptions about the organization’s support, which will strengthen leader-member relationships. They would build confidence about change, and, finally, resistance to change would reduce considerably. Similarly, distributive, procedural, and interactional justice influence the quality of leader-member relationships ( Brown et al., 2005 ; Lee et al., 2010 ). Positive the perception about justice dimensions, higher the quality of LMX. Thus, it can be argued that justice dimensions positively impact LMX, which further leads to lowering the resistance to change through employees’ improved state of RFC. So, we propose:

H 8 (a,b,c,d,e,f): Perceived organizational support mediates the relationships between the dimensions of organizational justice and readiness for change and resistance to change.

H 9 (a,b,c,d,e,f): Leader-member exchange mediates the relationships between the dimensions of organizational justice and readiness for change and resistance to change.

H 1 0 : Leader-member exchange and readiness for change sequentially mediate the relationship between perceived organizational support and resistance to change.

H 11 (a,b,c): Perceived organizational support and Leader-Member Exchange sequentially mediate the relationships between the dimensions of organizational justice and readiness for change and resistance to change.

Grounded on the proposed hypotheses, we suggest a theoretical research framework chalked out as Figure 1 .

The theoretical model.

Methodology

Sample and procedure.

We selected the five largest private commercial banks (based on the number of branches) with a total of 60,311 employees across Pakistan. Furthermore, in these banks, 37,996 employees worked in 3,793 branches across Punjab (the most populated province of Pakistan). The Punjab province was chosen because the majority of bank branches are located in this province. We developed a list of all branches and their employees by obtaining information from the State Bank of Pakistan, and related head offices of banks. The sample was adequately representative of the private banking sector because the top five commercial banks represent all banks in this region, and the data were collected employing a random sampling technique to ensure representativeness. The primary informants for this research were lower and middle managers because they are the frontline workers of banks who deal with implementing and complying with any policy or reform received from top management.

The structured questionnaires were distributed to collect the data. A total of 1,200 questionnaires were distributed across 600 branches, chosen based on a random sampling technique. Out of 1,200 distributed questionnaires, we received 410 questionnaires achieving a response rate of 34.16%. Thirty-eight questionnaires were discarded due to incomplete information, and, hence, the remaining 372 responses were considered for further analysis. The results show that the mean values (μ) of all variables are higher than the corresponding standard deviations (σ). The low values of μ/σ (CV = coefficient of variation) implies that all the variables in our study are under dispersed.

Due to the cross-sectional nature of the study, the findings might be likely to suffer from common method bias due to common method variance (CMV) ( Harman, 1976 ). It is one cause of the correlational error, which arises “when individual responses vary consistently to different degrees over and above true differences in the construct being measured; that is, it is a result of different individuals responding in consistently different ways over and above true differences in the construct” ( Viswanathan, 2005 , p. 108). This is opposed to random errors of measurement, which are presumed to be independent across the measures of the same or different constructs ( Baumgartner et al., 2021 ). Both ex-ante and ex-post approaches were used to restrict CMV. The following remedies were adopted during the research design stage as an ex-ante approach: (a): Assuring the secrecy and anonymity, the respondents were stressed upon providing fair responses disregarding them right or wrong ( Podsakoff et al., 2003 ); (b): the items of all constructs (independent, dependent and mediators) were shuffled to prevent a biased pattern of ticking the anchors in “creating” the correlation ( Murray et al., 2005 ); (c): the construction of the complex model in anticipation to avoid the mental model of interactions ( Harrison et al., 1996 ).

After that, statistical analyses were conducted to assess CMV as an ex-post approach. First of all, the most reported post hoc test, Harman’s single factor, was conducted without rotating the factor. The test resulted in a 29% variance explained by the single factor, which is less than the prevailing threshold value of 50%. It means no single factor emerged, and hence, there was no existence of CMV in the data. However, Podsakoff et al. (2003 , 2012) explain that the test has a low sensitivity in detecting CMV because it is implausible that a single-factor model would fit the complete data (notably, in the absence of some useful threshold). Due to the shortcoming of Harman’s single factor test, Podsakoff et al. (2012) recommend testing the measurement model (CFA) with and without a single latent factor, called a common latent factor (CLF). A CLF is a latent factor showing direct links with all the indicators (items). Hence, CFA was run with and without CLF, and both the measurement models achieved good fits. Now, in order to detect CMV, the standardized loadings of the two models were compared. The difference between these loadings was found to be less than 0.2, implying that CMV did not significantly inflate the estimates of the model CLF was not specified ( Devonish, 2018 ). Thus, the presence of CMV was disregard in this study.

The attributes of the study sample have been described in Table 1 . It reveals that the majority of the respondents were male. The dominant group of respondents was lower management with ages between 31 and 40 years and experience of more than 10 years.

Demographic characteristics of the respondents.

A seven-point Likert scale with a range from 1 = strongly disagree to 7 = strongly agree was used. The respondents were asked to rate the degree to which they agree or disagree with a particular statement. The organizational justice scale developed by Niehoff and Moorman (1993) was used, which contains five items for distributive justice, six items for procedural justice, and nine items for interactional justice. A seven-item scale developed by Scandura and Graen (1984) , also known as LMX-7, was used to measure LMX. An eight-item scale developed by Rhoades and Eisenberger (2002) was used to collect POS data. Furthermore, a nine-item scale by Bouckenooghe et al. (2009) was used for RFC. Similarly, an eighteen-item scale developed by Oreg (2003) was used to collect responses for RTC. All the items used in the study have been placed in Supplementary Appendix for reference.

The Measurement Model (Confirmatory Factor Analysis)

A partial least squares structural equation modeling (PLS-SEM) technique has been employed through SmartPLS 3.2.6 to analyze the research framework due to the non-normality of the sample collected. PLS-SEM proposes maximizing the dependent variables’ predictive accuracy while allowing the constructs to retain more items. This method was preferred because (1) data normal distribution is not necessary; (2) PLS-SEM is acceptable for the predictive purpose; and (3) it deals with the complexity of the model in terms of hypothesized relations and variables ( Albort-Morant and Ribeiro-Soriano, 2016 ).

The internal consistency of the constructs was measured by the values of Cronbach’s alpha and composite reliability (CR), as shown in Table 2 . The reliability was established because the values were above the acceptable threshold of 0.7 ( Gefen et al., 2000 ).

The measurement model.

Similarly, convergent validity and discriminant validity are the two ways that assess construct validity ( O’Leary-Kelly and Vokurka, 1998 ). The convergent validity was analyzed by investigating factor loadings and Average Variance Extracted (AVE) values of the measures. AVE, explained by a latent construct, shows the complete variance of indicators ( Fornell and Larcker, 1981 ). The values of loadings and AVE were above the threshold levels of 0.7 and 0.5, respectively ( Gliem and Gliem, 2003 ), as shown in Figure 2 and Table 2 .

Factor loadings.

Then discriminant validity, which assesses the degree of variation of one construct from others, was measured by ascertaining that the square roots of AVE values must be higher than the correlation between constructs ( Fornell and Larcker, 1981 ). Table 3 demonstrates that square roots of AVE values of constructs (shown in the diagonal) are greater than the inter-construct correlations (off-diagonal elements), establishing the discriminant validity. The significant values of correlations have also been reported here. Further, Table 3 displays the mean and standard deviation values, which show a narrow spread of the data.

Discriminant validity.

Structural Model

After acceptable and appropriate results of the measurement model, the study analyzed the research hypotheses through the PLS-SEM approach. The empirical results of the structural model have been presented in Figure 3 .

Structural model.

The structural model presents direct relationships related to12 research hypotheses, as reported in Table 4 . POS is significantly influenced by DJ (β = 0.239, p < 0.000), PJ (β = 0.308, p < 0.000), and IJ (β = 0.185, p < 0.001), which support the hypotheses H 1a , H 2a , and H 3 a , respectively. Hence, the results show that the dimensions of organizational justice have a significant positive effect on POS. However, IJ has the least positive significant relationship with POS. Similarly, LMX is significantly influenced by DJ (β = 0.166, p < 0.006), PJ (β = 0.150, p < 0.008), and IJ (β = 0.135, p < 0.007), which support hypotheses H 2a , H 2b , and H 3 c , respectively. The results demote that the dimensions of organizational justice have a significant positive influence on LMX. The results for H 4 show that POS positively affects LMX (β = 0.375, p < 0.000). In addition, H 5a , H 6a , H 5b , and H 6b have also been supported, which show that RFC is positively influenced by POS (β = 0.397, p < 0.000) and LMX (β = 0.332, p < 0.000). Furthermore, RTC is significantly and negatively influenced by POS (β = −0.145, p < 0.032) and LMX (β = −0.253, p < 0.000). In the end, H 7 has also been supported (β = 0.245, p < 0.001), representing that RFC has a significant negative impact on RTC.

Direct path coefficient.

Similarly, this study has utilized the Hayes (2018) process to analyze mediation because it does not strictly assume distribution ( Hair et al., 2013 ). Hayes (2018) process utilizes the bootstrapping technique in two steps. First of all, the significance level of a direct relationship is checked by employing bootstrapping, in which the mediating variable is not present in the model. Afterward, the significance of indirect effect and associated t -values are checked when the mediator is included in the model. The results of the mediation analysis have been presented in Table 5 .

The indirect effects.

The analysis results show that intervening variables mediate most of the relationships between organizational justice dimensions and resistance to change, as explained subsequently. This study analyzed the sequential role of POS and LMX between justice dimensions (DJ, PJ, and IJ) and RFC. The results show that the paths from justice dimensions (DJ, PJ, and IJ) to POS → LMX → RFC are significant. Each dimension of organizational justice reaches to RFC significantly through POS and LMX (β = 0.030, t = 3.035, p = 0.003), (β = 0.038, t = 3.155, p = 0.002), and (β = 0.023, t = 2.316, p = 0.021). This represents that hypotheses: H 8a , H 8b , and H 8c are supported. Then the study examined the sequential role of POS and LMX between justice dimensions (DJ, PJ, and IJ) and RTC. The results show that each dimension of organizational justice reaches to RTC significantly through POS and LMX (β = −0.023, t = 2.464, p = 0.014), (β = −0.029, t = 2.585, p = 0.010), and (β = −0.018, t = 2.054, p = 0.040). This represents that hypotheses: H 8d , H 8e , and H 8f of our study are supported. Furthermore, the paths from justice dimensions DJ, PJ, and IJ to RTC through LMX and RFC (DJ, PJ, and IJ) to LMX → RFC → RTC have been analyzed. The results indicated that the paths from DJ, PJ do not reach significantly to RTC (β = −0.013, t = 1.859, p = 0.064), (β = −0.012, t = 1.859, p = 0.064), whereas the path: IJ → LMX → RFC → RTC is significant but with very low significance level (β = −0.011, t = 1.949, p = 0.052). This suggests that hypotheses H 9a and H 9b are not supported, whereas H 9c finds moderate support. Furthermore, hypotheses for relationships (DJ, PJ, IJ) to POS → RFC → RTC for hypotheses H 9d , H 9e , and H 9f are supported. H 10 has also been tested as significant (β = −0.030, t = 2.328, p = 0.020). Finally, the study has analyzed the final three paths that reach from DJ, PJ, and IJ to RTC with the sequential role of POS, LMX and RFC. The paths are from justice dimensions (DJ, PJ, and IJ) to POS → LMX → RFC → RTC. The results have indicated that POS, LMX and RFC sequentially mediate the relationship of DJ, PJ with RTC (β = −0.007, t = 2.045, p = 0.041), (β = −0.009, t = 2.145, p = 0.032), supporting the hypotheses H 1 1a and H 1 1b . For IJ—the third dimension of organizational justice, the path does not reach RTC significantly through POS, LMX, and RFC (β = s−0.006, t = 1.789, p = 0.074), representing that the last hypothesis of our study, H 1 1c , is not supported.

Discussion and Conclusion

This study aims to extend the literature by demonstrating how different paths can lower the resistance to change in organizational justice and social exchange relationships within an organization. The study contributes to the literature by considering data from various branches of multiple banks from Pakistan. It has provided us with a broader view and clear assessment of antecedents and their effects on dependent constructs of the study ( Oreg et al., 2011 ). In previous organizational change-related studies, data were collected mainly from a single organization. Furthermore, there is a lack of research where the data were gathered from different organizations or organizations with multiple branches ( Fedor et al., 2006 ; Banguntopo, 2018 ). This study mitigates the shortcomings and contributes to the theory by investigating the dimensions of organizational justice and their role in determining the most suitable path in thwarting the employee resistance to change through POS, LMX, and RFC.

The results of our study have presented that all of the direct hypotheses are supported. It has been established that the level of organizational justice being practiced by the organization determines the quality of social exchange relationships ( Bauer and Green, 1996 ) between employees and management as well as between employees and their organization. Organizational justice not only impacts employee productivity but also influences several important organizational outcomes ( Fiaz et al., 2021 ). The results of the current study have supported that the dimensions of organizational justice (distributive justice, procedural justice, and interactional justice) positively impact the quality of POS and LMX. The fair process of distribution of rewards and adoption of equitable procedures enhances POS and LMX. These findings are aligned with previous studies ( DeConinck, 2010 ; Lee et al., 2010 ; Fu and Lihua, 2012 ).

Similarly, the results have shown that interactional justice strongly affects POS and LMX. An organization’s fair communications and interactions with the employees positively influence superior-subordinate relationships ( Piccolo et al., 2008 ). Then, the result of hypothesis H 4 represents that POS positively influences LMX. As suggested by the previous literature, employees perceive their organization’s support as their manager’s support, and, as a result, they form high-quality social exchange relationships with their boss ( Shore et al., 1994 ). The social exchange relationships (POS and LMX) induce positive perceptions regarding change outcomes, resulting in RFC among the employees. The findings are convergent with the literature ( Eby et al., 2000 ). Moreover, the study has observed that the more the employees are ready for change, the less chance they show negative responses, and less will be the change-resistant. This is also in line with the established literature on change ( Armenakis et al., 1993 ; Rafferty et al., 2013 ).

Likewise, the findings have also demonstrated a significant impact of dimensions of organizational justice (distributive justice, procedural justice, and interactional justice) on resistance to change (RTC) through mediators. Furthermore, the results represent that this effect is mediated sequentially through POS, LMX, and RFC. When dimensions of organizational justice are discussed individually, they significantly affect RTC when moving through the path from POS, LMX, and RFC. One path has been resulted completely insignificant, that is, IJ - > POS - > LMX - > RFC - > RTC. Furthermore, three other paths were found relatively less significant: 1. DJ - > LMX - > RFC - > RTC, 2. IJ - > LMX - > RFC - > RTC, and 3. PJ - > LMX - > RFC - > RTC due to longer paths. All the other paths are significant, ranging from moderate to very good level of significance, as displayed by p values representing the strength of indirect relationships (as represented in Table 4 ). This finding provides us with important insights regarding the positioning of justice dimensions as antecedents of RTC in an indirect framework through POS, LMX and RFC.

Since most employees fear that change will not bring any good for them or the organization itself ( Arnéguy et al., 2018 ), managers are concerned about lowering such a resistive attitude. Hence, the introduction of a comprehensive, sequential framework is a dire need of the current times. As a conclusion of this research, it has been put forward that employee resistance can be dealt with by making them ready for change. Acceptance for change becomes more promising when employees receive considerable support from their organization (POS) as well as from their managers (LMX). When the earlier discussed relationships are present, they will positively impact the state of an employee’s readiness for an organizational change, which will then be translated into “minimization of change resistance.” That will significantly help in the successful implementation of change.

Practical Implications

Change management has become one of the most important business priorities because organizations need to stay up-to-date to compete in today’s business environment. Organizations need to understand how to make their employees ready for the change to successfully implement a change strategy ( Ferlie et al., 2005 ). This study has provided us with a bird’s-eye view about the dimensional role of organizational justice in minimizing the resistance to change, indirectly through POS, LMX, and RFC. Suppose an organization intends to devise a change management strategy, as suggested by the empirical evidence of this study. In that case, it needs to focus on how and their distribution and procedures bring can justice among the employees because it is a building block of employee’s perception of organizational support and superior-subordinate social exchange relations ( Colquitt et al., 2013 ; Georgalis et al., 2015 ). Thus, employees will participate in change-oriented assignments willingly if they realize that change is beneficial for them. It is suggested that such a sense of RFC is better derived by organizational justice. Organizations may incorporate the underlying mechanisms discussed in this study as a tool for policymaking. For instance, an organization’s focus on distributive justice and procedural justice will catalyze organizational support, which will lead to a high-level LMX relationship. Subsequently, it will lead to minimizing the resistance to change through RFC. Undermining the role of organizational support, subordinate-employee relationships, or a sense of readiness for unimagined change may cost the organization a lot. If employee resistance to change is compromised, an organization may fail in its change implementation effort ( Meaney and Pung, 2008 ), leaving it with substantial financial losses.

The research framework tested and discussed in this study would prove to be a valuable tool for the management in understanding and designing a system to help them deal with the resistance to change. The success of any organization mainly depends on human capital because the employees are often treated as assets by successful organizations. In this regard, the studies relating to the employee’s behaviors and attitudes hold distinctive importance. Therefore, in terms of organizational change, the POS and quality of leader-member relationships play a vital role in determining the decisive approach of employees.

Limitations and Future Recommendations

Like other studies, our study is prone to several limitations that point to future directions for the researchers. First, our research has adopted a cross-sectional framework; however, a longitudinal approach for data collection will significantly contribute to the literature of change and organizational justice by providing insights into cause-and-effect relationships. Since this study is related to human resources and organizational behavior, data collected over time will significantly contribute significantly to the change management literature. Moreover, data were collected from the banking industry only. Change management is a big concern for other sectors as well, such as the manufacturing industry. Therefore, it will be valuable to use the small and large manufacturing industries as the sampling frame. Furthermore, since the current study extends recent change literature by exploring the justice dimensions, it is suggested that the effect of these dimensions on resistance to change may further be studied through other indirect variables like organizational identification, organization citizenship behavior. In this way, the researchers may determine a more favorable and robust framework with reference to antecedents and outcomes of change management.

Data Availability Statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

NR, AM, and SAM contributed to the conception and design of the study. NI organized the database. MI and EM performed the statistical analysis. NR wrote the first draft of the manuscript. AM, EM, MI, NI, and SAM wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Funding. The publication of this study was supported by the EU-funded Hungarian grant EFOP-3.6.3.-VEKOP-16-2017-00007, for the project entitled “From Talent to Young Researchers”—Supporting the Career-developing Activities of Researchers in Higher Education.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.678952/full#supplementary-material

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On Resistance: A Primer for Further Research

Andrew Maher and Martijn Kitzen | 09.08.22

Authors’ note: This primer reflects the discussion of an expert panel on resistance as a deliberate strategy featuring Major General Patrick Roberson , Dr. Ulrica Pettersson , Dr. Byron Harper, and Dr. James Kiras .

The war in Ukraine has produced many acts of notable resistance. Ukrainian civilians have defied Russian occupation forces and, through thousands of seemingly minor actions, sabotaged Russia’s efforts from behind enemy lines. Simply spray-painting traffic signs in the early days of the war, for example, denied easy navigation to Russian soldiers and introduced a source of friction. More recently, Ukrainians collaborating with Russia have been violently targeted . The war has sparked renewed interest in concepts and activities related to resistance, but what exactly is resistance and what can we learn from Ukraine?

What is Resistance?

Dr. Pettersson introduced the concept of resistance, which the Resistance Operating Concept as

a nation’s organized, whole-of-society effort, encompassing the full range of activities from nonviolent to violent, led by a legally established government (potentially exiled/displaced or shadow) to reestablish independence and autonomy within its sovereign territory that has been wholly or partially occupied by a foreign power.

Key within this definition is the idea of a “whole-of-society” effort. Some nations have used the term “ total defense ” to describe their resistance strategy, as it draws attention to a comprehensive military and societal resilience effort led by a whole-of-government approach. Dr. Harper noted that a mere 2 percent of society is part of the government, with an inherent obligation to participate in defense. The remaining 98 percent could be an ambivalent majority, as David Galula might say, and the rebel’s dilemma is how to mobilize collective action in the face of potentially high costs. What defines resistance, then, is understanding how the 2 percent might motivate, support, and mobilize segments of the 98 percent to engage in nonviolent and violent actions in defense of the nation.

Resistance also includes a broad range of methods. In fact, nonviolent approaches are often more effective, particularly against authoritarian regimes. As a result, resistance—whether in Paris in 1944 or Kyiv in 2022—is not just a military task, but a societal task that requires organization and leadership. Resistance is most effective when prepared in peacetime and underpinned by operations that emphasize resilience, so that society can cope with foreign-imposed crises.

Historical Examples of Resistance

Throughout the panel discussion, participants highlighted examples of resistance in World War II, the Cold War, and beyond. The World War II cases focused primarily on the European front, including the Baltic region’s “ Forest Brothers ,” the French Resistance , Norway, Denmark, and elsewhere. These cases were presented with the context of supporting efforts from the British Special Operations Executive . In the Cold War era, Sweden and Switzerland were key examples of nations that adopted a resistance strategy, yet these remained planned and hypothetical efforts that were never tested against an invader, so analysts cannot be confident in their assessments of how effective they might have been. In the modern era, the panel noted that the Russian invasion of Crimea in 2014 sparked a renaissance of interest in resistance, building upon the lessons of earlier eras. Major General Roberson offered lessons from both the Kurdish resistance to Saddam Hussein and efforts to foment resistance to the Islamic State in Iraq and Syria .

Participants also discussed the unique moral and ethical questions associated with resistance operations, citing a number of case studies including the Dutch Resistance , the heroic efforts of the Polish Home Army , and Operation Anthropoid in Czechoslovakia. These cases all highlight the ethical concerns inherent in supporting resistance movements against regimes that might conduct reprisals against local populations and violate the laws of armed conflict.

Analyzing case studies from these historical eras demonstrates the importance of understanding the nature of the occupying regime, its capacity to operate as a police state , and the capacity for local resistance. These lessons, alongside other vignettes, were noted in the scholarly efforts of Will Irwin, including Support to Resistance , Decision-Making Considerations in Support to Resistance , and How Civil Resistance Works . The panelists also noted the efforts of Otto Heilbrunn , Gene Sharp , Erica Chenoweth , and Richard Shultz .

What is Today’s Model of Resistance?

Estonia , Sweden, Finland , and other states have made resistance preparations based on the model of the “ indigestible hedgehog ”: the hedgehog that displays its defenses to both deter attacks from predators and demonstrate the difficulty of digesting it. Likewise, Resistance strategies aim to deter through denial and then impose costs —both moral and material—if deterrence fails. Moreover, successful resistance relies on a preplanned strategy, rather than an emergent response to a foreign invasion. This is because grassroots movements do not have comparable levels of organization, legitimacy, and resilience in the face of repression.

In addition, resistance movements often benefit from external state support, but foreign powers typically have less skin in the game, leading to lower levels of commitment. This problem holds especially true when resistance movements face an authoritarian opponent willing to employ indiscriminate violence. For example, Russian tactics of terrorism and long-term attrition may weaken Ukrainian resistance and diminish NATO’s support. Maintaining internal and external resolve is therefore critical, and it requires a shared understanding among Ukraine’s supporters. The panel noted that a community of interest has formed in response to Russia, which integrates lessons from today’s conflicts. This “intellectual interoperability” strengthens resistance concepts and practice.

Resistance in Ukraine and Future Research

Much of Ukraine’s unlikely success against a much larger and stronger invader flows from its diligent preparations starting in 2014, following the Russian annexation of Crimea. The panel cited three factors that have led to the successful use of resistance. First, Ukraine leveraged wide-scale participation in national defense through legislation passed in July 2021. Second, external allies exhibited impressive unity of effort in coordinating support to Ukraine. Third, foreign special operations forces advising Ukrainian counterparts were invested in understanding the Ukrainian perspective ; as Dr. Harper noted, they had gone “49 percent native” which allowed them to masterfully understand how to support Ukraine.

Despite the success of resistance in Ukraine, many questions remain unanswered and provide avenues for future research. First, why was the Ukrainian resistance strategy unable to deter a Russian invasion? Moreover, how can a state know if it is deterring a threat? Resolving these questions may help analysts and policymakers measure and define success in peacetime.

Another outstanding question will be the implications of substantial, overt international assistance. The international community has significantly contributed to the Ukrainian government’s ability to enhance, support, and maintain resistance. How well do international interests overlap with local interests, and how does this impact internal resolve to resist and external resolve to support resistance?

Finally, analysts do not fully understand the impacts of modern technology on resistance. Everyone with a smartphone is a potential resistance fighter. This lowers the barrier to entry for participation in armed conflict and enables a host of functions like intelligence reporting, fire coordination, and rapid information sharing.

The topics covered in this discussion and outlined above offer an avenue for research to expand the existing body of knowledge on resistance—a topic that, given the contemporary security environment, demands further investigation by practitioners and scholars alike.

Dr. Martijn Kitzen is a senior nonresident fellow with the Irregular Warfare Initiative and a professor at the Netherlands Defence Academy where he holds the chair of irregular warfare and special operations.

Andrew Maher is the engagements director for the Irregular Warfare Initiative, an Australian Army officer, and a lecturer with the University of New South Wales Canberra on irregular warfare.

The views expressed are those of the authors and do not reflect the official position of the United States Military Academy, Department of the Army, or Department of Defense.

Image credit: President of Ukraine

Let's consider "resistance" from a different point of view; this being, from the point of view of those states and societies — and those individuals and groups — who believe that:

a. Such political, economic, social and/or value changes,

b. As the U.S./the West has sought to achieve both here at home and there abroad post-the Old Cold War,

c. These such political, economic, social and/or value changes threaten their ways of life, their ways of governance and their values, attitudes and beliefs. (A basis for "resistance" — if their ever was one?)

In this regard, let's start with (from 1993) then-National Security Advisor Anthony Lake’s “From Containment to Enlargement" document; which served as a precursor/an introduction to President Bill Clinton’s signature “Engagement and Enlargement” national security strategy. (Herein, note the U.S.'s — very straight forward — grand "change" initiative re: other countries.)

“Throughout the Cold War, we contained a global threat to market democracies; now we should seek to enlarge their reach, particularly in places of special significance to us. The successor to a doctrine of containment must be a strategy of enlargement — enlargement of the world’s free community of market democracies. During the Cold War, even children understood America’s security mission; as they looked at those maps on their schoolroom walls, they knew we were trying to contain the creeping expansion of that big, red blob. Today, at great risk of oversimplification, we might visualize our security mission as promoting the enlargement of the ‘blue areas’ of market democracies. The difference, of course, is that we do not seek to expand the reach of our institutions by force, subversion or repression.”

Next, let's look at how both Russia and China have (a) embraced such things as traditional social values, beliefs and institutions and indeed has (b) "weaponized" same; this, (c) in their "resistance to unwanted change" efforts:

Russia in resistance mode:

“In his annual appeal to the Federal Assembly in December 2013, Putin formulated this ‘independent path’ ideology by contrasting Russia’s ‘traditional values’ with the liberal values of the West. He said: ‘We know that there are more and more people in the world who support our position on defending traditional values that have made up the spiritual and moral foundation of civilization in every nation for thousands of years: the values of traditional families, real human life, including religious life, not just material existence but also spirituality, the values of humanism and global diversity.’ He proclaimed that Russia would defend and advance these traditional values in order to ‘prevent movement backward and downward, into chaotic darkness and a return to a primitive state.’ …

As Putin passes his 20th year as Russia’s president, his domestic and foreign policy appears intended to contrast his country’s ‘independent path’ with the liberal and decadent regimes in the West. The invented battle of Western values versus Russia’s ‘traditional values’ is part of a Kremlin effort to justify its broader actions in the eyes of Russian citizens, placing them in the context of a global struggle in which Russia is the target of aggression. Ignoring and violating the provisions of international organizations to which it is a party thus becomes a demonstration of defending its conservative values from European liberalism."

(See the Wilson Center publication “Kennan Cable No. 53” and, therein, the article “Russia’s Traditional Values and Domestic Violence,” by Olimpiada Usanova, dated 1 June 2020.).

China in resistance mode:

“This may, in fact, be the missing explanatory element. Ideologies regularly define themselves against a perceived ‘other,’ and in this case there was quite plausibly a common and powerful ‘other’ (to wit: Western liberalism) that both (Chinese) cultural conservatism and (Chinese) political leftism defined themselves against. This also explains why leftists have, since the 1990s, become considerably more tolerant, even accepting, of cultural conservatism than they were for virtually the entire 20th century. The need to accumulate additional ideological resources to combat a perceived Western liberal ‘other’ is a powerful one, and it seems perfectly possible that this could have overridden whatever historical antagonism, or even substantive disagreement, existed between the two positions.” (Items in parenthesis above are mine.)

(See the April 24, 2015 Foreign Policy article “What it Means to Be ‘Liberal’ or ‘Conservative’ in China: Putting the Country's Most Significant Political Divide in Context" by Taisu Zhang.)

Last, let's see how the U.S./the West's "change" initiatives have caused individuals and groups — even here in the U.S./the West — to go into "resistance" mode — this extending, in this case, even to conservative elements in the U.S./the West now seeing Russia as their "natural ally" today:

"Liberal democratic societies have, in the past few decades, undergone a series of revolutionary changes in their social and political life, which are not to the taste of all their citizens. For many of those, who might be called social conservatives, Russia has become a more agreeable society, at least in principle, than those they live in. Communist Westerners used to speak of the Soviet Union as the pioneer society of a brighter future for all. Now, the rightwing nationalists of Europe and North America admire Russia and its leader for cleaving to the past."

(See "The American Interest" article "The Reality of Russian Soft Power" by John Lloyd and Daria Litinova.)

Bottom Line Thought — Based on the Above:

In the Old Cold War of yesterday, the Soviets/the communist sought to achieve "revolutionary" political, economic, social and value changes; this, both in their own home countries and throughout the world. This (a) fostered a worldwide "resistance to change" effort, which, (b) leaned heavily on such things as traditional social values, beliefs and institutions.

In the New/Reverse Cold War of today, it has been the U.S./the West that has sought to achieve "revolutionary" political, economic, social and value changes — both in our own home countries and throughout the world. This such effort, likewise, has (a) fostered a worldwide "resistance to change" effort which, once again, has (b) leaned heavily on such things as traditional social values, beliefs and institutions.

Accordingly, should not "resistance" — today — be viewed from this such perspective also?

Perhaps I missed it in the hot links but there appears to be no loinage to the ARIS Project at USASOC. (Assessing Revolutionary and Insurgent Strategies)

Here: https://www.soc.mil/ARIS/ARIS.html. and here: https://www.soc.mil/ARIS/books/arisbooks.html

There are a wide range of resources and references at the project thanks to the hard work and vision of Paul Tompkins in partnership with the Johns Hopkins Applied Physics Lab National Security Division.

Resistance is the foundation of Unconventional Warfare, irregular warfare, political warfare, revolution, and insurgency.

"Activities to enable a resistance or insurgency to coerce, disrupt or overthrow a government or occupying power through and with an underground auxiliary, or guerrilla force in a denied area."

Scholars, analysts, officers, critics, and students of the Ukraine War history should note that Ukraine is a "special case war." Very few nations in the world are "gifted" high-tech arms that are free and functional to a level of trust that NATO is willing to commit to. Also, Ukraine is often considered one race…would resistance work in the USA with its diversity and many foreign Green Card workers?

Ukraine called for all able men to defend the Homeland, essentially a draft. But Ukraine has shown that attacking in the form of ATGMs, light infantry, captured tanks, drones, and artillery is the best form of resistance. One can't resist with just trenches, tank traps, barbed wire and small arms and expect to win with such static and passive forms of defensive resistance—one has to attack. One has to have the means to conduct LRPFs to resist in such a fashion as to make the enemy think again. And that form of resistance with LRPF is what made the difference compared to VBIEDs and IEDs in the GWOT. Close fighting in the GWOT was often the form of resistance, but in Ukraine, the ATGM can reach out to a few miles. The insurgency was a constant form of attacking resistance that wore down on Coalition troops. When mortars and RPGs were used in the GWOT, it made a difference.

Morale, attitude, support, logistics, propaganda, revenge, money, love, time, sweat, blood, sacrifice, charisma, effort, and esprit de corps are all factors in resistance of a nation being invaded, even against overwhelming odds. The chances of survival, combined with luck and lots of prayers, can win the day if the odds tilt in the defenders' favor. Technology has definitely played a role.

But why then did Ukraine resistance seem so surprising and the insurgencies not? In terms of "good vs. bad," I think the battle lines were drawn and the moral and social aspects are clearer compared to the GWOT. The GWOT certainly has other nations contributing on both sides, but one thing is clear is that when destroyed, the tanks and IFVs litter the area. In the GWOT, the Taliban dragged away their wounded and dead. One can SEE the resistance level in the Ukraine War by the burnt-out hunks of AFVs. One CAN'T see the resistance level in the GWOT because like in Vietnam, the enemy hauled away their dead so the body count was uncertain. "Big sticks" are used in Ukraine, but not the GWOT by the insurgency outside of small arms and technical pickups.

When videos circulate of massive explosions and "breaking things and killing people," that lifts the morale of the defenders if the results are shown of the successful defense and attacks. If the news covers mistakes made by the Coalition, then that is a depressing downer. The media can play a huge role in telling the story of resistance. If troops whine that they didn't know what to do, and missions are told of Elmer Fudds bumbling around, then that's no way to organize a resistance. If the stories are such that the defenders are competent and "kick some real good," then that is a success story. Video facts have to back up the claims as video can expose the lies also. The TV is the best weapon sometimes.

Outpourings of support, love, and emotion work in favor of the leader who shows it. A stoic leader will not garner the same support as one who has appeal to and of the masses.

"Resistance is futile." No one likes or cares for the stoic robotic emotionless Borgs in Star Trek compared to the Jedi in Star Wars that were wiped away down to the last Jedi. So does anyone want to be a suffering Jedi armed with just a lightsaber to perhaps be cut down by blasters? –SPOILER–Grogu sure doesn't—SPOILER—he went back to the Mandalorian instead of being a Padawan. The Jedi are a symbol of futile resistance that people cheer and yet they lost whereas the Borg are a symbol of resistance that people fear, hate, and loathe and yet they rise into success and prosper. Borg have high tech weapons and ships and Jedis often do not. Borg are a team community and Jedis are lone rangers of resistance. A tale of two Sci-Fi sagas of resistance told on TV for decades should be an example of Real World resistance, even if fictional.

Once again, the TV and media can be the best weapon in the world. All wars seem to prove this.

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Resistance to Social Influence - Social Support

Last updated 22 Mar 2021

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Asch’s (1951) research demonstrates the power of social influence through conformity and his variations provide an insight into how group size, unanimity and task difficult can increase or decrease the influence of the majority. Milgram (1963) on the other hand, highlights our susceptibility to obeying orders and his variations reveal the different variables that can increase or decrease our willingness to follow orders.

Since Asch and Milgram’s research, psychologists have examined explanations of resistance to social influence, our willingness to conform or obey, including social support and locus of control.

• Social Support

One reason that people can resist the pressure to conform or obey is if they have an ally, someone supporting their point of view. Having an ally can build confidence and allow individuals to remain independent.

Individuals who have support for their point of view no longer fear being ridiculed, allowing them to avoid normative social influence. Furthermore, individuals who have support for their point of view are more likely to disobey orders.

Evidence for this explanation comes from one of Asch’s (1951) variations. In one of the variations, one of the confederates was instructed to give the correct answer throughout. In this variation the rate of conformity dropped to 5%. This demonstrates that if the real participant has support for their belief (social support), then they are likely more likely to resist the pressure to conform.

Furthermore, evidence for this explanation comes from Milgram (1974). In one of Milgram’s variations, the real participant was paired with two additional confederates, who also played the role of teachers. In this variation, the two additional confederates refused to go on and withdrew from the experiment early. In this variation, percentage of real participants who proceeded to the full 450 volts, dropped from 65% (in the original) to 10%. This shows that if the real participant has support for their desire to disobey, then they are more likely to resist the pressure of an authority figure.

Variations from Asch and Milgram suggest that if an individual has social support then they are likely to resist the pressure to conform or obey.

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Single-cell chromatin accessibility profiling of acute myeloid leukemia reveals heterogeneous lineage composition upon therapy-resistance

Affiliations.

• 1 Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
• 2 Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
• 3 Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
• 4 Princess Margaret Cancer Center, University Health Network, Toronto, ON, M5S 1A8, Canada.
• 5 Department of Molecular Genetics, University of Toronto, Toronto, Canada.
• 6 Department of Hematopathology, University of Texas M D Anderson Cancer Center, Houston, TX, USA.
• 7 Department of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
• 8 Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
• 9 Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
• 10 Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
• 11 Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
• 12 Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA. [email protected].
• 13 Department of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, USA. [email protected].
• PMID: 37479893
• PMCID: PMC10362028
• DOI: 10.1038/s42003-023-05120-6

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by high rate of therapy resistance. Since the cell of origin can impact response to therapy, it is crucial to understand the lineage composition of AML cells at time of therapy resistance. Here we leverage single-cell chromatin accessibility profiling of 22 AML bone marrow aspirates from eight patients at time of therapy resistance and following subsequent therapy to characterize their lineage landscape. Our findings reveal a complex lineage architecture of therapy-resistant AML cells that are primed for stem and progenitor lineages and spanning quiescent, activated and late stem cell/progenitor states. Remarkably, therapy-resistant AML cells are also composed of cells primed for differentiated myeloid, erythroid and even lymphoid lineages. The heterogeneous lineage composition persists following subsequent therapy, with early progenitor-driven features marking unfavorable prognosis in The Cancer Genome Atlas AML cohort. Pseudotime analysis further confirms the vast degree of heterogeneity driven by the dynamic changes in chromatin accessibility. Our findings suggest that therapy-resistant AML cells are characterized not only by stem and progenitor states, but also by a continuum of differentiated cellular lineages. The heterogeneity in lineages likely contributes to their therapy resistance by harboring different degrees of lineage-specific susceptibilities to therapy.

© 2023. The Author(s).

Publication types

• Research Support, N.I.H., Extramural
• Research Support, Non-U.S. Gov't
• Cell Differentiation
• Cell Division
• Cell Lineage / genetics
• Chromatin* / genetics
• Leukemia, Myeloid, Acute* / drug therapy
• Leukemia, Myeloid, Acute* / genetics

Grants and funding

• P30 CA016672/CA/NCI NIH HHS/United States
• R01 LM012806/LM/NLM NIH HHS/United States
• S10 OD024977/OD/NIH HHS/United States

Corrosion and Wear Resistance of HVOF-Sprayed Ni-Cr-Co Multi-principal Element Alloy Coating on Copper Plate

• ORIGINAL RESEARCH ARTICLE
• Published: 16 May 2024

Cite this article

• Dongbao Huang 1 ,
• Zhenlin Xu   ORCID: orcid.org/0000-0002-3747-5363 1 , 2 ,
• Yizhu He 1 , 2 ,
• Ming Liu 3 ,
• Xiquan Jia 1 &
• Tingwei Zhou 1  

The development of protective coatings on copper alloy surfaces represents a critical research direction to enable the widespread industrial application of copper alloys. To improve the corrosion resistance and wear resistance of the copper alloy plates, a Ni-Cr-Co-based multi-principal element alloy coating was prepared via high-velocity oxygen fuel (HVOF). Then, the microstructure, corrosion resistance, and wear resistance of the Ni-Cr-Co coating and the electroplated NiCo coating were analyzed comparatively. The research results show that the phases of the Ni-Cr-Co coating contained face-centered cubic (FCC) solid solution, CrB and M 23 C 6 . The NiCo coating exhibited a single-phase FCC solid solution structure. Compared to the NiCo coating, the corrosion current density of the Ni-Cr-Co coating was reduced by 92.1% in NaF solution. A highly protective passive film was formed on the Ni-Cr-Co coating, and its low ΣCSL grain boundary proportion reached as high as 25.7%. Therefore, the Ni-Cr-Co coatings demonstrated superior corrosion resistance. The scratch wear coefficient of the Ni-Cr-Co coating was only 51.9% of that of the NiCo coating, due to the synergistic strengthening of the matrix and hard second phase. This research offers technical support and a theoretical basis foundation for the development of coatings on copper alloys with excellent corrosion resistance and wear resistance.

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This work was supported by the National Natural Science Foundation of China (Grant No. 51971001) and the Key Research and Development Project of Anhui Province (No. 2022a05020017).

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School of Materials Science and Engineering, Anhui University of Technology, Maanshan, 243002, China

Dongbao Huang, Zhenlin Xu, Yizhu He, Xiquan Jia & Tingwei Zhou

Anhui Key Lab of Metal Material and Processing, Maanshan, 243002, China

Zhenlin Xu & Yizhu He

Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing, School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China

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Huang, D., Xu, Z., He, Y. et al. Corrosion and Wear Resistance of HVOF-Sprayed Ni-Cr-Co Multi-principal Element Alloy Coating on Copper Plate. J Therm Spray Tech (2024). https://doi.org/10.1007/s11666-024-01788-2

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Received : 27 December 2023

Revised : 01 May 2024

Accepted : 01 May 2024

Published : 16 May 2024

DOI : https://doi.org/10.1007/s11666-024-01788-2

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