Research Tips with Dr. Juliana

  Hotel sustainability initiatives including solar energy, LED lighting and recycling A hotel can fit every guest room with energy-efficient LED bulbs, install solar water heaters, place recycling bins in the lobby, and ask guests to reuse their towels—and still not know whether its environmental footprint actually improved. That is the gap between doing something green and being able to demonstrate measurable sustainability performance . A hotel may genuinely invest money, time and staff effort in environmental initiatives. These actions matter. But if management cannot track energy, water, waste or greenhouse-gas emissions consistently, it becomes difficult to determine whether those initiatives are producing meaningful operational improvements. In sustainable hospitality, the question is therefore not simply, “What green initiatives do you have?” A better question is: “What does your data show?” Looking Green vs. Being Measurably Sustainable Visible sustainability initi...

You run your reliability analysis in SPSS, and there it is: Cronbach's alpha = .68. Now you are wondering: “Is my questionnaire unreliable? Do I need to throw away the scale? Will my supervisor or journal reviewer reject my study?” Not so fast. A Cronbach's alpha below .70 deserves attention, but .70 is not a magical pass-or-fail line . The meaning of an alpha coefficient depends on the number of items, how closely the items are related, the nature of the construct, the purpose of the research, and the evidence that the scale is measuring a coherent construct. In this article, we will look at what Cronbach's alpha actually tells you, what α = .70 means, what to do when you obtain something like α = .68 , and the mistakes you should avoid when reporting reliability in a thesis or journal article. Quick answer: An alpha of .70 is commonly used as a rule-of-thumb benchmark for acceptable internal consistency, but it should not be treated as a universal cutoff. A...

Research results and discussion section comparison on a laptop Every academic writer has experienced this moment: you spend months conducting research, analysing data, or synthesising qualitative evidence. You prepare your tables and figures, write your findings, complete the manuscript—and finally click Submit . Then, months later, the decision letter arrives: “The manuscript presents clear data, but the Discussion section largely restates the Results. The authors should contextualise their findings and articulate their broader implications.” This is a common type of criticism in academic publishing. One reason is that researchers sometimes treat the Discussion as another opportunity to report their findings rather than as a space to interpret and contextualise those findings. The distinction is simple but important: RESULTS = What did you find? DISCUSSION = What do your findings mean? ...

    Sustainable hospitality business identifying hidden costs from energy, water, food and resource waste. Introduction Ask most business owners what sustainability means, and you'll usually get some version of the same answer: recycling, solar panels, maybe a "green" certification on the website. It sounds virtuous, a little expensive, and mostly optional, something you get to once the "real" business priorities are sorted. That framing misses the point entirely, and it costs businesses money. Sustainability, at its core, is about using resources well — energy, water, food, materials, and people. And when a business uses resources poorly, it doesn't just harm the environment. It quietly drains profits. A dripping tap, an oversized energy bill, a walk-in fridge full of food that never made it to a plate, a revolving door of staff who leave within months of being trained — none of these show up as a single dramatic loss. They show up as a thousand sm...

  SPSS regression banner explaining why R² should not be reported alone If you are using SPSS for regression analysis , there is one common mistake you should avoid: reporting the R² value alone and concluding that your regression model is statistically significant. For example, you may obtain: R² = .25 A researcher might write: “The regression model was significant and explained 25% of the variance in the dependent variable.” The first part of that statement cannot be concluded from R² alone. An R² of .25 means that the predictors in the regression model explain 25% of the variance in the dependent variable . However, you need additional statistics from the SPSS output to determine whether the overall regression model is statistically significant. This distinction is important if you want your research findings to be statistically accurate. What Does R² Mean in SPSS? R², or R-squared, is the coefficient of determination. It tells you the proportion of variance in the dependent va...