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Vally, Kyiv (2026)

20/05/2022

What consumers truly value can be difficult to pin down and psychologically complicated. But universal building blocks of value do exist, creating opportunities for companies to improve their performance in existing markets or break into new markets. In the right combinations, the authors’ analysi...

19/05/2022

Do you always worry that you didn’t do a good job? Do you always question your work and your actions? Are you afraid of admitting your mistakes? Does rejection make you feel like s**t?

If so, you’re in great danger.

I’m not a perfectionist myself. At least, that’s what I try to tell myself. I bet that you try to tell yourself that as well. In fact, the people who don’t admit it are the worst.

But here’s the thing: If you’re a perfectionist, you’re just a procrastinator with a mask. It’s no different from someone who’s lazy and does nothing at all.

Don’t believe me? Let’s take a look. A perfectionist…

Always waits for the right moment.
Never makes mistakes.
Always needs more time.
But at the end of the day, life and work is about outcomes. Results matter.

And if you’re a perfectionist you might get the outcomes some day. But the question is: When? And, at what cost?

Research specifically shows that perfectionism is closely related to depression and low self-esteem.

“Perfectionists are their own devils.” —Jack Kirby

Is the price of perfectionism really worth it?
I’ve found that perfectionism is just another form of procrastination. When you constantly worry about making mistakes, doubt creeps in your mind. And that causes indecision.

There are two types of perfectionists:

The one that never starts. You want to achieve something, but you immediately start doubting yourself. You think: “I don’t think I can do it.” So you never start.
The one that starts but has too high standards. You set a goal. You work hard (maybe too hard). But you’ve set your goals so high, that you’re always failing yourself.
Both scenarios can cause the following: Anxiety, worry, depression, and Type A behavior.

These are things that we rather avoid. Joachim Stöber and Jutta Joormann, who studied Worry, Procrastination, and Perfectionism, write:

“The combination of concern over mistakes and procrastination may be a crucial factor in the maintenance of worry. On the one hand, it may prolong existing threats because no steps are taken to cope. On the other hand, it may increase existing threats or even produce additional threats because initially solvable problems will pile up, thus creating an overload of problems that may finally be insoluble.”

And that feeling of being helpless is the biggest pitfall for us. Because what do we do when we feel helpless? Exactly—we give up. Just look at the studies about Learned Helplessness.

However, perfectionism is not always bad. In fact, some studies suggest perfectionism is related to greater achievement. But that’s not the question here.

Of course, when you set higher goals and if you have higher standards; you achieve more. Without a doubt, perfectionistic tendencies can be a good thing.

But as we all know, achieving goals is not the only thing in life. It’s more about HOW we reach our goals and aspirations.

“How can we beat the nasty side of procrastination and perfectionism?”
So we’ve talked about how procrastination and perfectionism are related, and why it can be bad. But what’s the solution?

I’ve found an interesting study by Gordon L. Flett and his colleagues; they talk about the role of learned resourcefulness to perfectionism. They suggest that learned resourcefulness can play a mediator role.

So I started looking into learned resourcefulness. And this is what I’ve found from an article by Michael Rosenbaum:

“Learned resourcefulness refers to the behavioral repertoire necessary for both regressive self-control and reformative self-control. This repertoire includes self-regulating one’s emotional and cognitive responses during stressful situations, using problem-solving skills, and delaying immediate gratification for the sake of more meaningful rewards in the future.”

Learned resourcefulness is the skill that you need to stop sabotaging yourself.

Finding a balance.
IMG_0059
Let’s look at the opposite of a perfectionist: A slacker.

If you’re a slacker, you don’t care about much. Good enough is your motto. And you have no ambition at all.

An attitude like that doesn’t bring you anywhere. The American novelist Cormac McCarthy put it best:

“It’s like a lot of things, said the smith. Do the least part of it wrong and ye’d just as well to do it all wrong.”

Slacking is an attitude of “I don’t care.” But if you want to make things happen in your life, you have to care.

And what you want is to find a middle ground where your perfectionistic tendencies drive you, but you have the calm of a slacker, and you combine that with learned resourcefulness.

So that’s why I found a balance between perfectionism and slacking. It looks like this:

perfectionism
Do great work like a perfectionist, but don’t give too much attention to your goals like a slacker.

And finally, combine it with this:

Resourcefulness — Goals can work well, but they can also be counterproductive. That’s why you want to rely on systems. And when s**t hits the fan; use your problem-solving skills to figure things out.
To me, that’s the sweet spot: Instead of beating yourself up when you make a mistake or if you fail yourself, you just adjust or solve the problem.

Avoid the perfectionist’s favorite sentence: “OMG, this is the worst thing ever!”
Also avoid the slacker’s favorite sentence: “I don’t care.”
But instead, you say: “I’ve got this.”
So what’s your current challenge? Actually, I don’t even have to ask: You’ve got this.

19/05/2022

We'll go first: It's never too late to start adopting good money habits.

19/05/2022

The conglomerate will likely face stiff competition in the sector from existing global giants such as Unilever, as well as Indian tycoon Mukesh Ambanis Reliance Industries Ltd., which plans to acquire up to 60 small grocery and household consumer goods brands within six months, according to Reuters.

19/05/2022

Abstract
Although we are accustomed to thinking about technology as involving things—objects and processes—derived from scientific discoveries, science also creates a technology of ideas, ways of thinking both about the world and about human beings. And unlike “thing technology,” “idea technology” can have powerful effects even when the ideas are false. This paper discusses false idea technology, or ideology, and suggests mechanisms by which it can have effects on both individuals and societies. It discusses neuroscience as the “next frontier” of ideology that may change our conceptions of human nature.

We are, in sum, incomplete or unfinished animals who complete or finish ourselves through culture—and not through culture in general but through highly particular forms of it.

—Clifford Geertz (1973:50)

“His brain made him do it.”

—Hon. Jed S. Rakoff, Senior United States District Judge of the United States District Court for the Southern District of New York, in a lecture at Swarthmore College, 2011.

Introduction
On the campus where I used to teach, every time a new building was built or an old one was substantially renovated, an issue arose about where to locate the asphalt walkways that would go between that building and other campus locations. One school of thought suggested that the placement of walkways should be part of the building plan. But a second school, no doubt having observed many asphalt paths that lay unused near trails of dirt where once there had been grass, had the view that you build the building, watch where people walk, and put the asphalt where the grass has been worn thin. Proponents of the first view are folks we might call “theory driven.” Guided by some sense of efficient movement, esthetics, or both, they are inclined to do the “ideal” thing and have people conform to it. Proponents of the second view are folks we might call “data driven.” They let the users of the space tell them, with their behavior, what the “ideal” thing is.

When done right, all of science is an ongoing conversation between theory and data. The point of theories in science is to organize and explain the facts. Facts without organizing theories are close to useless. But theories must ultimately be accountable to the facts. And new facts force us to modify or discard inadequate theories.

That is the ideal. But in real life, things do not always work out this way. At least in the social sciences, theories, rather than being beholden to facts, can shape facts in a way that strengthens the theories. You build that path and then force people to walk on it, perhaps by roping off the grass.

This can be true even in the pristine world of the laboratory. An investigator publishes a paper that reports a significant finding using novel research methods. The paper attracts a good deal of attention. Others develop and extend the initial findings using the same methods. Quickly, a research tradition develops in which these particular methods are used to study this particular phenomenon. We now have a well-supported theory confirmed by a large set of empirical facts. But, alas, it turns out that the empirical methods on which this theoretical edifice is built are deeply flawed. There are confounds in the experimental designs, or questionable methods used in data gathering and analysis. Redone more carefully, new experiments fail to replicate the findings that created this edifice in the first place (see Simmons, Nelson, and Simensohn 2011). Thus, science may eventually and ultimately correct its mistakes, but disciplinary norms for conducting and analyzing research can perpetuate those mistakes for quite some time, making questionable results and incorrect theories seem self-evident to participants in a field until the mistakes are uncovered. Scientists operating at their best know (better than non-scientists) that all theories are false, i.e., provisional. Their hope is that their current mistaken efforts will at least be useful, rather than complete blind alleys (see Sterman 2002).

“If you build it, they will come.” This is the mantra that the main character in the movie Field of Dreams keeps hearing as he turns his farmland into a baseball park in the middle of nowhere. He builds it, and they do come. In this paper, I will try to show that at least sometimes, when social scientists build theories, the people come. That is, the people are nudged into behaving in ways that support the theories. This paper, then, is an attempt to resolve a conversation between metaphors. The “watch where they walk, then pave it” metaphor argues that the empirical data shape the theories people develop. The “if you build it, they will come” metaphor argues that theories shape data. I will attempt to defend the second metaphor.

The debate here is one that has been going on in more familiar territory for years. Does the market cater to consumer desires or does it create consumer desires? Do the media cater to people’s tastes in news and entertainment or do the media create those tastes? We are all accustomed to the difficulties surrounding discussion of these issues in modern society, and we may all have fairly strong opinions about the cater/create debate. Questions of just this sort are all around us.

In a sense, the distinction I am making is between discovery and invention. Discoveries tell us things about how the world works. Inventions use those discoveries to create objects or processes that make the world work differently. The discovery of pathogens leads to the invention of antibiotics. The discovery of nuclear energy leads to bombs, power plants, and medical procedures. The discovery of the genome leads, or will lead, to untold changes in almost every part of our lives. Of course, discoveries also change the world, by changing how we understand it and live in it, but they rarely change the world by themselves.

The distinction between discovery and invention is crucial. When a scientist, or anyone else, discovers something, it does not occur to us to ask whether that discovery should exist. In other words, although discoveries often have moral implications, they do not, by themselves, have moral dimensions. If someone were to suggest that the Higgs boson should not exist, we would wonder what mind-altering substance he had ingested. Inventions, in contrast, are a whole other story. Inventions characteristically have moral dimensions. We routinely ask whether they should exist. We wonder what is good (life improving) about them, and what the drawbacks are. We debate whether their wide distribution should go forward, and if so, with what kind of regulation.

So, is a theory about human nature a discovery, or is it an invention? I believe that often, it is more invention than discovery. For example, consider this, from Adam Smith:

It is in the inherent interest of every man to live as much at his ease as he can; and if his emoluments are to be precisely the same whether he does or does not perform some very laborious duty, to perform it in as careless and slovenly a manner that authority will permit. (1937:221)

I think that ideas like this from Smith, about what motivates people to work, have shaped the nature of the workplace (see Schwartz 2015). As noted by distinguished economist John Maynard Keynes:

The ideas of economists and political philosophers, both when they are right and when they are wrong, are more powerful than is commonly understood. Indeed the world is ruled by little else. Practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist. (1965:386)

The ideas that Keynes is talking about are ideas about human nature—about what people care about, and what they aspire to. And like fish that do not know they live in water, we live with such ideas about human nature that are so pervasive that we do not even realize there is another way to look at ourselves. Where once our ideas about ourselves may have come from our parents, our community leaders, and our religious texts, these days, they come mostly from science—specifically from social science. Social science has created a “technology” of ideas about human nature.

Adam Smith’s ideas about human laziness helped give shape to the form the industrial revolution took. Ideas about racial differences in intelligence, self-discipline, and motivation helped give shape to the form that racial oppression took. Ideas about gender differences helped give shape to the form that gender discrimination took. And now, in the “age of the brain,” ideas about how neural processes determine our desires, thoughts, and actions promise to give rise to changes in how we think about human agency, autonomy, and responsibility (see Monterosso, Royzman, and Schwartz 2005; Monterosso and Schwartz 2020). Of all the technologies that humanity has produced over its history, the technology of ideas may be the most powerful.

14/05/2022

Crypto data websites Etherscan, CoinGecko, DeFi Pulse and others reported such phishing attacks, reports CoinDesk. The phishing attack appears to come from a domain displaying the Bored Ape Yacht Club logo.A

14/05/2022

Credibility
Guba and Lincoln (1989) claimed that the credibility of a study is determined when coresearchers or readers are confronted with the experience, they can recognize it. Credibility addresses the “fit” between respondents’ views and the researcher’s representation of them (Tobin & Begley, 2004). Lincoln and Guba (1985) suggested a number of techniques to address credibility including activities such as prolonged engagement, persistent observation, data collection triangulation, and researcher triangulation. They also recommended peer debriefing to provide an external check on the research process, which may therefore increase credibility, as well as examining referential adequacy as a means to check preliminary findings and interpretations against the raw data. Credibility can also be operationalized through the process of member checking to test the findings and interpretations with the participants (Lincoln & Guba, 1985).

Transferability
Transferability refers to the generalizability of inquiry. In qualitative research, this concerns only to case-to-case transfer (Tobin & Begley, 2004). The researcher cannot know the sites that may wish to transfer the findings; however, the researcher is responsible for providing thick descriptions, so that those who seek to transfer the findings to their own site can judge transferability (Lincoln & Guba, 1985).

Dependability
To achieve dependability, researchers can ensure the research process is logical, traceable, and clearly documented (Tobin & Begley, 2004). When readers are able to examine the research process, they are better able to judge the dependability of the research (Lincoln & Guba, 1985). One way that a research study may demonstrate dependability is for its process to be audited (Koch, 1994), which will be discussed in further detail below.

Confirmability
Confirmability is concerned with establishing that the researcher’s interpretations and findings are clearly derived from the data, requiring the researcher to demonstrate how conclusions and interpretations have been reached (Tobin & Begley, 2004). According to Guba and Lincoln (1989), confirmability is established when credibility, transferability, and dependability are all achieved. Koch (1994) recommended researchers include markers such as the reasons for theoretical, methodological, and analytical choices throughout the entire study, so that others can understand how and why decisions were made.

Audit Trails
An audit trail provides readers with evidence of the decisions and choices made by the researcher regarding theoretical and methodological issues throughout the study, which requires a clear rationale for such decisions (Koch, 1994). Sandelowski (1986) stated that a study and its findings are auditable when another researcher can clearly follow the decision trail. Furthermore, Koch (1994) argued that another researcher with the same data, perspective, and situation could arrive at the same or comparable, but not contradictory, conclusions. Keeping records of the raw data, field notes, transcripts, and a reflexive journal can help researchers systemize, relate, and cross reference data, as well as ease the reporting of the research process are all means of creating a clear audit trail (Halpren, 1983).

Reflexivity Is Central to the Audit Trail
Researchers are encouraged to keep a self-critical account of the research process, including their internal and external dialogue (Tobin & Begley, 2004). A reflexive journal can be used by researchers to record to document the daily logistics of the research, methodological decisions, and rationales and to record the researcher’s personal reflections of their values, interests, and insights information about self (the human instrument; Lincoln & Guba, 1985).

Toward a Step-by-Step Approach for Conducting a Trustworthy Thematic Analysis
From a thorough examination of our experiences with qualitative analysis, we have attempted to outline a practical and effective procedure for conducting thematic analysis that aims to meet the trustworthiness criteria outlined by Lincoln and Guba (1985). In qualitative research, the process of data collection, data analysis, and report writing is not always distinct steps; they are often interrelated and occur simultaneously throughout the research process (Creswell, 2007). Because data collection and data analysis may happen concurrently, it is important to identify that the data analysis process may not be entirely distinguishable from the actual data (Thorne, 2000). Although thematic analysis as documented by Braun and Clarke (2006) will be presented here as a linear, six-phased method, it is actually an iterative and reflective process that develops over time and involves a constant moving back and forward between phases. Table 1 highlights how researchers may address Lincoln and Guba’s (1985) criteria for trustworthiness during each phase of thematic analysis.

Table
Table 1. Establishing Trustworthiness During Each Phase of Thematic Analysis.

Table 1. Establishing Trustworthiness During Each Phase of Thematic Analysis.

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Exemplar Study
In 2014, we began Phase 1 of a 5-year mixed methods case study of nine SCNs in Alberta, Canada. SCNs connect stakeholders across health systems—including patients and families, health-care professionals, researchers, the government, and professional organizations—to identify health and system needs and to develop plans to address those needs using quality improvement initiatives with best evidence. In collaboration with our knowledge users and decision makers, we aimed to understand what made these networks effective, including how networks engaged their stakeholders and what knowledge translation and engagement looked like across their initiatives.

This study was approved by the University of Calgary Conjoint Health Research Ethics Board REB13-0783/0781. Interviewees provided both written and verbal consent to participate. Our study built on a smaller pilot study and guiding conceptual framework that included a modified input–process–output team effectiveness model (Mathieu, Maynard, Rapp, & Gilson, 2008), knowledge translation (Graham et al., 2006), and stakeholder engagement (see Figure 1). The qualitative data in Phase 1 consisted of 71 documents, 117 interview transcripts from exploratory interviews, and 15 observation field notes. Initial codes were generated deductively based on our pilot study, prior research, and conceptual framework. Codes were first fit into a preexisting coding framework to provide detailed analysis of aspects of the data we were most interested in exploring. This variable-oriented strategy (Miles, Huberman, & Saldana, 2014) also facilitated cross-case analysis of the data during later stages of analysis. Phase 1 has been completed (Norris, Hecker, Rabatach, Noseworthy, & White, 2017; Norris, White, Nowell, Mrklas, & Stelfox, 2017). Phase 2 data are currently undergoing analysis, while data collection for Phase 3 has begun.

figure

Figure 1. Study conceptual framework.

Phase 1: Familiarizing Yourself With Your Data
Description
Qualitative data come in various forms including recorded observations, focus groups, texts, documents, multimedia, public domain sources, policy manuals, and photographs (Thorne, 2000). Textual data may also include field notes from participant observations, reflexive journal entries, and stories and narratives (Crabtree & Miller, 1999). Qualitative researchers may triangulate different data collection modes to increase the probability that the research findings and interpretations will be found credible (Lincoln & Guba, 1985). Regardless of the form of data collection, archiving all records of the raw data provides an audit trail and a benchmark against which later data analysis and interpretations can be tested for adequacy (Halpren, 1983; Lincoln & Guba, 1985).

If data were collected through interactive means, researchers will come to the analysis with some prior knowledge of the data and possibly some initial analytic interests or thoughts. Documenting these thoughts during data collection may mark the beginning of data analysis, as researchers may note initial analysis thoughts, interpretations, and questions (Tuckett, 2005). Regardless of who collected the data, it is vital that researchers immerse themselves with the data to familiarize themselves with the depth and breadth of the content (Braun & Clarke, 2006).

The volume, complexity, and varied formats of qualitative data (e.g., audio recordings, transcriptions, documents, and field notes) often lack consistent structure; however, all are useful and imperative for conducting a comprehensive analysis (Dey, 1993). To become immersed in the data involves the repeated reading of the data in an active way searching for meanings and patterns. Braun and Clarke (2006) recommended that researchers read through the entire data set at least once before beginning coding, as ideas and identification of possible patterns may be shaped as researchers become familiar with all aspects of their data.

Researchers are encouraged to engage with the analysis as a faithful witness to the accounts in the data, being honest and vigilant about their own perspectives, preexisting thoughts and beliefs, and developing theories (Starks & Trinidad, 2007). Researchers can document their theoretical and reflective thoughts that develop through immersion in the data, including their values, interests, and growing insights about the research topic (Lincoln & Guba, 1985; Sandelowski, 1995). During this phase, researchers may also make notes about ideas for coding that can be returned to in subsequent phases (Lincoln & Guba, 1985).

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