The Effects of Emotional Value, Social Value and Sensory Appeal Value to Mall Repatronage Intention in Kota Kinabalu Context
Bibliographic record
Abstract
The rapid expansion of the Malaysian retail contribution to the GDP could expand to 15 per cent from the current 10 per cent in the next five years, stated by Malaysia Retail Chain Association (MRCA) President Datuk Seri Garry Chua. He mentioned that for the second quarter of 2018, Malaysia’s retail industry reported a growth rate of 2.1 percent in retail sales, compared to the same period in 2017. As a result, some retailers enjoyed a 30 percent increase in business, and others only 10 per cent after the 0 per cent of the Goods and Services Tax (GST). High value-added retail goods, such as luxury items, sporting goods, electrical goods, electronics goods, gadgets and furniture, enjoyed higher sales compared to those of basic necessities, household goods and general fashion items. There is huge potential in the local retail industry, despite concerns of a glut in retail space. Hence, the malls must be interactive and have new dynamics in shopping for consumer’s repatronage (Sharen, 2018). Specifically, Kota Kinabalu, Sabah in East Malaysia, shopping malls have tremendously sprung up nearly in every neighbourhood. In present, there are new malls opened that compete with the old. From just several earliest malls such as Centre Point, Wawasan Plaza, Wisma Merdeka, KK Plaza, and Karamunsing Complex, there are more upcoming operational malls to be built located in Kota Kinabalu city (Daily Express Online, December 30, 2015). The new malls opened differ in sizes, architectural designs and are strategically located and several of it are Imago, Oceanus, Suria Sabah, City Mall, 1Borneo and Grand Merdeka. As the number of malls rise, many consumers in the developing countries have shown loyalty towards certain malls that meet and exceeds their expectations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".