Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic

Online food delivery services: cross-sectional... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/10-972" }, "headline": "Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after...", "datePublished": "2021-09-27T11:20:50", "dateModified": "2026-03-03T09:56:37", "author": [ { "@type": "Person", "name": "Sin Yin Tan" }, { "@type": "Person", "name": "Su Yin Lim" }, { "@type": "Person", "name": "Sook Fern Yeo" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background During the COVID-19 pandemic, Malaysian consumers were more likely to purchase food online and have it delivered to their doorstep. To stay afloat, many restaurants were pushed to provide online food delivery services (OFDS), and this sector has grown tremendously. However, will the trend persist after the pandemic? This study aims to look into how consumers’ perceptions of OFDS affect their attitude towards them. It investigates the relationship between convenience motivation, perceived ease of use, time-saving orientation and price-saving orientation in terms of future intent to use OFDS. Method Primary data was collected from 307 respondents in Malaysia using convenience sampling method through an online survey. Respondents’ demographic background was presented statistically in cross tabulation tables to study the ratio comparison implicitly. Consistent Partial Least Square approach and bootstrapping techniques with 5,000 subsamples was employed, with the aid of SmartPLS.V3 software, to identify the significant factors influencing consumers’ continuance intention after the pandemic. Result Perceived ease of use does not contribute significantly to continuance intention as most consumers have prior online purchase experience. Nevertheless, time-saving orientation has a positive correlation with perceived ease of use due to the simplicity of placing an order with just a click. It is also found that price-saving orientation is related to convenience motivation, particularly when prices can be compared on the websites or online ordering platforms. Consumers’ intention to continue using OFDS even after the COVID-19 pandemic is positively influenced by all the parameters studied, except for perceived ease of use. Conclusion Limited work has been done on the continuance intention to use OFDS beyond the pandemic. This study provides insight for food retailers on how to enhance their business and retain their customers with the support of technology, even after the COVID-19 pandemic. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/10-972/v2", "name": "Online food delivery services: cross-sectional study of consumers’..." } } ] } Home Browse Online food delivery services: cross-sectional study of consumers’... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Tan SY, Lim SY and Yeo SF. Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.12688/f1000research.73014.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] Sin Yin Tan https://orcid.org/0000-0001-8606-3827 1 , Su Yin Lim https://orcid.org/0000-0001-5625-7022 2 , Sook Fern Yeo https://orcid.org/0000-0002-8060-5872 2 Sin Yin Tan https://orcid.org/0000-0001-8606-3827 1 , Su Yin Lim https://orcid.org/0000-0001-5625-7022 2 , Sook Fern Yeo https://orcid.org/0000-0002-8060-5872 2 PUBLISHED 08 May 2024 Author details Author details 1 Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 2 Faculty of Business, Multimedia University, Melaka, 75450, Malaysia Sin Yin Tan Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Su Yin Lim Roles: Conceptualization, Data Curation, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Sook Fern Yeo Roles: Methodology, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Research Synergy Foundation gateway. Abstract Background During the COVID-19 pandemic, Malaysian consumers were more likely to purchase food online and have it delivered to their doorstep. To stay afloat, many restaurants were pushed to provide online food delivery services (OFDS), and this sector has grown tremendously. However, will the trend persist after the pandemic? This study aims to look into how consumers’ perceptions of OFDS affect their attitude towards them. It investigates the relationship between convenience motivation, perceived ease of use, time-saving orientation and price-saving orientation in terms of future intent to use OFDS. Method Primary data was collected from 307 respondents in Malaysia using convenience sampling method through an online survey. Respondents’ demographic background was presented statistically in cross tabulation tables to study the ratio comparison implicitly. Consistent Partial Least Square approach and bootstrapping techniques with 5,000 subsamples was employed, with the aid of SmartPLS.V3 software, to identify the significant factors influencing consumers’ continuance intention after the pandemic. Result Perceived ease of use does not contribute significantly to continuance intention as most consumers have prior online purchase experience. Nevertheless, time-saving orientation has a positive correlation with perceived ease of use due to the simplicity of placing an order with just a click. It is also found that price-saving orientation is related to convenience motivation, particularly when prices can be compared on the websites or online ordering platforms. Consumers’ intention to continue using OFDS even after the COVID-19 pandemic is positively influenced by all the parameters studied, except for perceived ease of use. Conclusion Limited work has been done on the continuance intention to use OFDS beyond the pandemic. This study provides insight for food retailers on how to enhance their business and retain their customers with the support of technology, even after the COVID-19 pandemic. READ ALL READ LESS Keywords Online food delivery services, Continuance intention, Attitude, Behavioural intention, Convenience motivation, Perceived ease of use, Time-saving orientation, Price-saving orientation Corresponding Author(s) Sin Yin Tan ( [email protected] ) Close Corresponding author: Sin Yin Tan Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Tan SY et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Tan SY, Lim SY and Yeo SF. Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.12688/f1000research.73014.2 ) First published: 27 Sep 2021, 10 :972 ( https://doi.org/10.12688/f1000research.73014.1 ) Latest published: 03 Mar 2026, 10 :972 ( https://doi.org/10.12688/f1000research.73014.3 ) Revised Amendments from Version 1 This revised version incorporates feedback from the reviewer. The introduction has been substantially enhanced by addressing poverty eradication and the research gap, supported by additional citations. The literature review section now includes the theoretical background, which is further elaborated to support the proposed research model. Furthermore, the collected data has been reanalysed after excluding respondents who have no experience in using online food delivery services (OFDS). The data analysis has also been refined by adding the f2 and Q2 values to assess the effect size and predictive relevance. As a result, there are slight modifications in the statistical results, as illustrated in Figures 2 and 3 and Tables 2 to 9. Finally, the discussion section has been improved by explaining how the constructs of this research contribute to the existing theoretical frameworks based on the proposed research model. This revised version incorporates feedback from the reviewer. The introduction has been substantially enhanced by addressing poverty eradication and the research gap, supported by additional citations. The literature review section now includes the theoretical background, which is further elaborated to support the proposed research model. Furthermore, the collected data has been reanalysed after excluding respondents who have no experience in using online food delivery services (OFDS). The data analysis has also been refined by adding the f2 and Q2 values to assess the effect size and predictive relevance. As a result, there are slight modifications in the statistical results, as illustrated in Figures 2 and 3 and Tables 2 to 9. Finally, the discussion section has been improved by explaining how the constructs of this research contribute to the existing theoretical frameworks based on the proposed research model. See the authors' detailed response to the review by Hyun-Woo Joung See the authors' detailed response to the review by Lau Teck Chai See the authors' detailed response to the review by Bui Thanh Khoa READ REVIEWER RESPONSES  There is a newer version of this article available. Suppress this message for one day. Introduction The COVID-19 pandemic, which swept the world in 2020, caused an unprecedented gripping death toll, affecting the public health, food systems and workplace. Tens of millions of people face the looming threat of extreme poverty, while millions of businesses are on the brink of closure. Nearly half of the world's workplace, totaling 3.3 billion people, are at risk of unemployment 1 . In Malaysia, the pandemic had a profound impact on the nation’s economy, labour market, and social dynamics. The unemployment rate rose from 1.2% to 4.5% in 2020, the highest in nearly three decades. Many people have lost their jobs, sources of income, and even businesses as a result of this situation 2 . This state of affairs is extremely concerning and may jeopardize the achievement of the sustainable development goals (SDGs) established by the United Nations in 2015. In particular, SDG 1 targets the eradication of extreme poverty in all forms everywhere by 2030. Among others, the outcome goals are to lift individuals living on less than US$1.90 per day out of poverty and to reduce all poverty by half. Even though global poverty has been steadily declining for the last 20 years, research by the UNU World Institute for Development Economics Research cautioned that the COVID-19 pandemic might raise it to 8% of the world’s population in just a few months into the pandemic 3 . Everyone must do their part to overcome the challenges of COVID-19, including the government, the commercial sector, and the general public. If businesses, especially, could modify their business models to cater to the population at the bottom of the pyramid, they could play a significant role in alleviating poverty while still profiting. Businesses may reach spectacular new markets made up of billions of people at the lower end of the income spectrum thanks to the web and e-commerce, which are made possible by the widespread use of mobile devices to access the internet in this digital age 4 . Unsurprisingly, many businesses have turned to e-commerce to stay competitive. In Malaysia, 84.2% of the population uses the internet, 88.3% of them use a shopping app each month and in particular, 6.86 million people used online food delivery services (OFDS) to order take-away food in 2020 5 . The COVID-19 outbreak lockdown, enacted to minimise physical contact, has forced consumers to adjust their preferences, increasingly turning to digital services for various needs, including food purchases 6 . As such, restaurants were eager to collaborate with online delivery platforms in order to stay in business 7 This avenue not only ensures their continuity but also provides a platform for small and medium-sized enterprises (SMEs) seeking to extend their reach in the online sphere. GrabFood’s deliveries increased vastly by 30%, with 8,000 new merchants whose online revenues increased by 25% 6 . Malaysia’s OFDS market undoubtedly, increased tremendously in 2020, by 45.9% from 2019, and is expected to reach US$370 million in revenue over the next four years 5 , 8 . Apart from preventing business closures, e-commerce also plays an important role in creating job opportunities, especially for those who have lost their source of income as a result of the pandemic. While approximately 25% of GrabFood’s deliveries were made by GrabCar drivers, who were hampered by the limited movement, Foodpanda reported a 7.5% rise in new riders during the lockdown. Over 10,000 people joined Grab as drivers and delivery partners, in reality, opening up employment chances for those in need 6 . The OFDS industry has demonstrated remarkable growth potential 9 , a trend notably accentuated by the events of 2020. Therefore, it is imperative to explore the determinants influencing consumers’ inclination to order food online on a regular basis, particularly in the aftermath of a pandemic. Amid the global pandemic, the lockdown, which leaves consumers with no choice but to prepare their own meals or order them online has resulted in an unprecedented surge in the OFDS business in 2020. A pertinent question arises as to whether this surge was a temporary phenomenon or if it will lead to a sustained growth in the long term. Research must explore the factors influencing consumers’ willingness to embrace online food ordering as a routine practice, even as pandemic restrictions subside. This would assist food retailers in positioning their products and services to capitalise on this emerging market. Previous research has primarily focused on consumers’ attitude towards online services in general, with only a few researchers focusing on consumer experiences with OFDS 10 , 11 . Despite the fact that online food delivery is an emerging trend, the majority of the studies in this domain examined consumers’ intention and initial adoption of OFDS 9 , 12 , 13 . Some researchers investigated factors such as customer satisfaction 11 , 12 , 14 – 16 , convenience 10 , 11 , 13 , 17 , 18 , perceived ease of use 13 , 17 , 18 , price-saving 10 , 11 , 17 , 19 – 21 , customer experience 11 , 13 , product information quality 17 , 21 , 22 , prior online purchase experience 10 , 20 , perceived usefulness 18 , 20 and perceived trust 14 , 18 in using OFDS. However, very little research has been conducted to investigate the continuance intention of OFDS in this unprecedented pandemic state that may intensify usage 14 , 16 . Will consumers continue to order food online once the restrictions on movement are lifted? Therefore, to bridge this gap, this study aims to further investigate the critical factors that consumers believe are important in motivating them to continue using OFDS after the COVID-19 epidemic. Literature review and hypotheses development Theoretical background This study aims to examine the essential factors perceived by consumers as influential in their decision to persist in using OFDS following the COVID-19 outbreak. Previous studies have frequently combined the Theory of Planned Behaviour (TPB) and Technology Acceptance Model (TAM) to explain why people engage in a specific behaviour. For example, a study conducted in China used TPB, TAM and three patient-centered factors to examine the elements affecting patients’ acceptance of mobile medical platforms 23 , while another study conducted in Italy combined TPB and TAM to analyse the main drivers of users’ intention to use foods delivery apps 18 . Other examples include the examination of continuance intention to utilise mobile banking in Jordan, achieved through the integration of UTAUT, TPB, TAM and service quality with machine learning methods 24 , and understanding library user behavioral utilisation intention of physical book as compared to e-book format in Malaysia by combining TAM, TPB and Theory of Self-Regulation (TSR) 25 . This study proposes a similar approach to form an integrative theoretical research model adapted from the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB). Expanding upon this foundation, the research model incorporates additional factors such as convenience motivation, price-saving orientation and time-saving orientation. TAM, conceptualised by Davis in 1989, put forward that users’ attitudes toward a technology are shaped by their perception of its ease of use and usefulness. These attitudes subsequently influence users’ behavioural intentions to adopt and continue using the technology 26 . In the context of OFDS, perceived ease of use relates to how easy consumers believe ordering food online is. A user-friendly interface, straightforward navigation, and intuitive app design all contribute to a high level of perceived ease of use. Consumers who find the service easy to use are more likely to form positive attitudes toward the service and their intention to use it. TPB, developed by Ajzen in 1991 extends the understanding of user behaviour by incorporating attitudes, subjective norms and perceived behavioural control as determinants of behavioural intentions, which in turn impact actual behaviour 27 . This framework holds significant recognition within psychology and social science disciplines, aiming to elucidate and predict human behaviour. Attitude plays a pivotal role in influencing an individual’s perception and inclination toward a specific behaviour. In the context of OFDS, consumers’ attitudes are primarily shaped by two key factors: their perceptions of the benefits, which encompass aspects such as convenience, cost-effectiveness, and time efficiency; and their perceptions of the ease of using the service 28 . When consumers hold favourable beliefs regarding these factors, it leads to the development of positive attitudes, which in turn, significantly enhances the likelihood of consumers forming a positive intention to engage in OFDS. In essence, attitude acts as a critical determinant in the decision-making process regarding the adoption and utilisation of OFDS among consumers. Both TAM and TPB emphasise the importance of behavioural intentions. While TAM believes that perceived ease of use and usefulness lead to behavioural intentions, TPB directly incorporates behavioural intentions as a key component. Behavioural intention represents the user’s intention or willingness to engage in a specific behaviour. If consumers find OFDS easy to use, they are more likely to use them regularly. This intention is driven by the notion that the service provides convenience and efficiency in food ordering. TAM is particularly useful for understanding continuance intentions. It implies that users’ initial attitudes and behavioural intentions influence their continued use of technology or services. Likewise, while TPB is traditionally applied to assess initial intentions, it can be adapted to consider continuance intention by exploring whether user’s attitudes and behavioural intentions formed during the pandemic persist as the situation changes. Consumers with positive attitudes and strong initial intentions may also have strong continuance intentions. In this study, the research model was expanded to include convenience motivation, time-saving orientation and price-saving orientation. Consumers are motivated by the desire to simplify their lives and save time and effort. Convenience motivation is consistent with this concept as it reflects consumers’ drive to seek convenience in their choices. Convenience motivation also aligns with attitudes and behavioural intentions in TPB. If consumers perceive OFDS as highly convenient and complementary to their lifestyle, it positively influences their attitudes and intentions towards ordering food online. Aside from convenience, consumers frequently seek ways to make simple and expedite daily activities. TPB’s perceived behavioural control accounts for this time-saving approach. If consumers believe that OFDS save them time and effort compared to traditional dining options, this perception positively influences their attitudes and behavioural intentions, making them more inclined to use the service to save time. Furthermore, today’s consumers seek not only time-saving and ease but also economic advantages. Both TAM and TPB indirectly consider cost-related factors. TAM can account for cost-related benefits through perceived usefulness, whereas TPB can account for external factors such as subjective norms related to cost savings. If consumers believe OFDS as cost-effective because of promotions, discounts, or reduced transportation costs, it can positively impact their attitudes and intentions to use the service. This comprehensive framework combines elements from the TAM and the TPB, augmenting them with additional variables. By doing so, it encompasses not only the technological adoption aspects (TAM) but also the broader socio-psychological factors (TPB) that shape consumer attitudes and behavioural intentions in the context of OFDS. This integrated approach facilitates a holistic examination of the various variables and their intricate interconnections, thereby yielding a more nuanced understanding of consumer behaviour within the realm of OFDS. Convenience motivation Convenience is defined as the perceived time, value and effort required to facilitate the use of OFDS. Consumers now have the freedom to choose from a wide range of food providers listed on the internet at any time and from anywhere. As a result of its convenience, consumers will be motivated to use OFDS on a regular basis 29 , 30 . A total of 47% of e-commerce users in Southeast Asia shopped online to save time and energy, and 87% agreed on the usefulness of internet services during the COVID-19 outbreak 31 . Malaysians also prefer online shopping when they have a hectic schedule 32 . The ease of comparing prices across different online platforms and a wide variety of items are all motivating factors that drive consumers to shop online. Convenience was also cited as the top reason for shopping online in Q4 2020, and remained the top three reasons in Q1 2021 33 . Perceived ease of use Perceived ease of use (PEOU) refers to a person's perception of how hassle-free it is to use a system. The quality of a system is defined as the ease with which pages can be navigated, the presence of a clear and uncomplicated layout, and the system's dependability 34 . It is critical for businesses to ensure that their online platform is simple to use because bad designs or a complicated process will deter consumers from continuing with the online purchase. The amount of effort required to use a system will serve as a critical predictor of its adoption and subsequent usefulness 17 , 26 . It was discovered that if it is relatively effortless to use a system, consumers are more likely to order food online 13 . Time-saving orientation In today's fast-paced world, where consumers’ busy schedules mean time is in short supply, time-saving orientation (TSO) has become a critical factor in easing daily tasks while fully utilising time. Many office workers could not afford the time and trouble of going out to eat, including driving and queuing up to place order. Thus, using OFDS is the quickest way to get food and the time saved can be used to complete other tasks. Higher-income consumers value time because of the opportunity costs. As such, they find online shopping appealing because it allows them to make better use of their time 19 . A study discovered that timesaving is the key determinant of consumers' motivation to use technology-based self-service 35 . When consumers are able to save time, their perception turns positive and as a result, their attitude towards OFDS also becomes favourable 10 , 20 , 29 . Price-saving orientation Price can be defined as the value (monetary or non-monetary) an individual must put forth in an exchange for a product or service 36 , 37 . One of the key factors influencing customer satisfaction is price-saving orientation (PSO), which includes offers and discounts provided by sellers 11 . 82.9% of Malaysians purchased a product online in the past month 5 . The internet makes it easier to compare prices among different online sellers, which has proven to be advantageous for consumers to purchase at a lower price, which in turn has a significant effect on their behavioural intention to shop online 17 , 38 . OFDS provide additional perks such as not having to pay for service charge imposed by the restaurants, as well as getting free delivery and discount coupons. Additionally, consumers do not need to expend energy or effort to visit a physical store or restaurant. Thus, consumers will be more satisfied with their online food ordering experience and will be more likely to use these services in the future 12 , 20 . Attitude, behavioural intention and continuance intention Attitude (ATT) can be defined as a consumer's overall reaction when using a specific device or technology 27 . It refers to a person's reaction, whether positive or negative, to a given object 39 . When consumers believe that online food ordering is useful and capable of easing their daily lives, they are more likely to develop a positive attitude which will lead to continuance intention (CI) of using it. Thus, attitude is positively related to behavioural intention 10 , 18 , 40 . Behavioural intention (BI) is defined as a person's proclivity to act in a certain way 41 . The intent to use OFDS denotes a consumer's desire to purchase food and beverages through online delivery platforms 10 . Many studies have established that the factors used to measure BI include positive word-of-mouth, willingness to recommend a product or service to others and also repurchase intention 42 . Consumers who are pleased and content with their online purchase experience are expected to continue doing so 12 . The main objective of this study is to identify the factors that may influence consumers’ attitude and behaviour towards continuance intention in using OFDS post pandemic, as illustrated in the proposed research model in Figure 1 . The hypotheses are proposed as follows: H1: Convenience motivation positively influences consumers’ attitude towards online food delivery services. H2: Perceived ease of use positively influences consumers’ attitude towards online food delivery services. H3: Time-saving orientation positively influences consumers’ attitude towards online food delivery services. H4: Price-saving orientation positively influences consumers’ attitude towards online food delivery services. H5: Attitude positively influences consumers’ behavioural intention towards online food delivery services. H6: Behavioural intention positively influences consumers’ continuance intention towards online food delivery services. Figure 1. Research model. Methods Ethics Research ethics approval was obtained from Multimedia University, Malaysia (EA1422021) and the respondents gave their written informed consent when filling out the Google Form. Questionnaire development An online survey with close-ended questions was designed using Google Form to examine the research hypotheses. It consisted of two parts: demographic information of respondents and 25 measurement items which indicated seven variables, namely, CM, PEOU, TSO, PSO, ATT, BI and CI towards OFDS, which were adopted from previous studies 10 , 12 , 14 , 18 , 22 , 43 – 45 and recorded in Table 1 . All items were measured based on a five-point Likert-type 46 , 47 . Table 1. Measurement items of the study. Constructs Indicators Sources Convenience motivation CM1: Online food ordering would allow me to order food at any time. Brewer and Sebby (2021) Cho et al . (2019) Ganesh et al . (2010) Troise et al . (2021) CM2: Online food ordering would allow me to order food at any place. CM3: Online food ordering would make my daily life easier. CM4: I like the comfort of ordering food without leaving home. Perceived ease of use PEOU1: I would find that it is easy to use OFDS. Liébana-Cabanillas et al . (2017) Troise et al . (2021) PEOU2: I would find that using OFDS requires minimum effort. PEOU3: I would find that learning how to order food online is easy for me. PEOU4: I would find that it is easy to navigate through the online food ordering platform. Time-saving orientation TSO1: I believe that I can save time by using OFDS to order food. Yeo et al . (2017) TSO2: Using OFDS shortens the time spent to select my meal. TSO3: Using OFDS shortens the time spent to get my meal ready. TSO4: It is important for me to purchase food as quickly as possible by using OFDS. Price-saving orientation PSO1: I can save money by checking and comparing the price of different OFDS before purchase. Yeo et al . (2017) PSO2: Online discount coupons help me to save a lot, compared to purchasing at shop/restaurant. PSO3: I can search for cheaper food deals in different websites or online platforms. PSO4: Online food retailers offer better value for my money spent on food. Attitude ATT1: Purchasing food through OFDS is a wise action. Yeo et al . (2017) ATT2: Purchasing food through OFDS is a good idea. ATT3: Purchasing food through OFDS is a sensible thing to do. Behavioural intention BI1: I plan to use OFDS to order food in the future. Cho et al . (2019) Troise et al . (2021) BI2: I am willing to use OFDS to order food whenever possible. BI3: I am likely to keep using OFDS to order food. Continuance intention CI1: I intend to use OFDS continuingly after COVID-19. Alalwan (2020) Cho et al . (2019) Zhao and Bacao (2020) CI2: If I have the opportunity, I will continuingly order food through OFDS after COVID-19. CI3: I am willing to use OFDS continuingly in future. Data collection In this study, purposive sampling method was applied 48 – 50 because the selected samples are more representative of the population. It is commonly used by researchers for similar studies, such as a recent study on the intention to use OFDS among consumers in Malaysia, which gathered 224 samples for data analysis 10 . Questionnaire was sent to potential respondents who were close contacts (relatives, friends and students) of the authors of this study, and they were invited through email, Facebook and WhatsApp, between 22 March 2021 and 18 April 2021. A primary dataset of 256 respondents was gathered, in order to examine consumers’ perception and attitude towards OFDS during the pandemic, which is critical to the future growth of the OFDS industry. The minimum sample size of 191 is determined according to the guideline of Hair et al. 51 , with a maximum of 4 arrows pointing at a latent variable and minimum R 2 of 0.10. Hypotheses approach Demographic background of respondents is presented descriptively and graphically. Consistent Partial Least Square (PLSc) approach 51 – 53 was applied to study the reflective and formative factors in this study and SmartPLS.v3 software was the main tool used (a free version is available for 30 days). Reliability and validity were tested in factor analysis and bootstrapping of 5,000 subsamples was used to estimate PLSc path model 54 . Results Profile of survey respondents Table 2 shows the demographic profile of 256 respondents 55 . All of them has experienced using OFDS and mostly are young adults between the age of 18 to 25 years old (40.63%). 68.75% preferred to eat at home, compared to at a restaurant. Figure 2 depicts the distribution of respondents who ordered food via third-party mobile apps, social media, or the company’s own website or mobile apps. Foodpanda (76.56%) and GrabFood (70.70%) are the most popular in Malaysia because consumers deemed that the platforms are user-friendly 15 . However, social media platforms such as Instagram are more suitable for promoting food rather than ordering 56 . Table 2. Frequency and percentage distribution of demographic profile. Characteristics n = 256 % Age Under 18 4 1.56 18 ~ 25 104 40.63 26 ~ 30 40 15.63 31 ~ 40 71 27.73 41 ~ 50 26 10.16 51 ~ 60 10 3.90 60 and above 1 0.39 State Malacca 122 47.66 Johor 65 25.39 Selangor 38 14.85 Negeri Sembilan 10 3.91 Kelantan 5 1.95 Perak 5 1.95 Sarawak 4 1.56 Kedah 3 1.17 Pahang 2 0.78 Penang 2 0.78 Dining preference Outside 80 31.25 At home 176 68.75 Figure 2. Distribution of online food delivery services platform. Table 3 recorded the feedback of the respondents whereby the mode for all measurement items is “Agree”, which contributes to the left-skewed distribution except PSO4. The average and standard deviation of variables are recorded in Table 4 and each average is close to “4” (Agree) except PSO. Table 3. Feedback of the respondents. Strongly disagree Disagree Neutral Agree Strongly agree CM1 5 9 46 126 70 CM2 5 19 53 112 67 CM3 5 5 35 135 76 CM4 3 6 45 122 80 PEOU1 5 3 45 151 52 PEOU2 4 5 62 137 48 PEOU3 5 3 38 146 64 PEOU4 4 9 58 134 51 TSO1 6 9 46 126 69 TSO2 9 21 67 116 43 TSO3 6 14 65 127 44 TSO4 6 12 63 119 56 PSO1 17 34 73 96 36 PSO2 11 20 71 103 51 PSO3 6 19 77 117 37 PSO4 14 41 88* 78 35 ATT1 3 6 85 121 41 ATT2 3 6 60 146 41 ATT3 5 7 82 132 30 BI1 5 3 73 132 43 BI2 4 7 63 141 41 BI3 7 6 73 126 44 CI1 4 13 67 126 46 CI2 3 14 59 137 43 CI3 5 12 60 133 46 Table 4. Mean and standard deviation of the variables. Mean SD CM 3.98 0.72 PEOU 3.92 0.71 TSO 3.78 0.79 PSO 3.49 0.91 AI 3.76 0.73 BI 3.79 0.76 CI 3.79 0.82 Table 5 shows the ratio comparison of the dining preference among the OFDS users based on age, gender, marital status and personal income level. As expected, the majority of OFDS users preferred to enjoy their food at home during pandemic especially the elderly or married adults prefer to enjoy their food at home (>80% for age group above 41 years old; married 73%). Although 71.88% of the users were earning a low income, they still preferred to use OFDS and dine at home (71%) compared to higher income respondents. This indicates COVID-19 pandemic has significantly changed people’s lifestyles and has became a new norm. Table 5. Comparison of dining preference among the OFDS users. Characteristic Ratio Number of OFDS users Dining at home Age 60 1 1.00 Gender Female 174 0.73 Male 82 0.60 Marital status Single 179 0.66 Married 73 0.73 Others 4 1.00 Personal income level B40 184 0.71 M40 65 0.63 T20 7 0.57 Measurement of model Reliability and validity Table 6 shows Cronbach’s alpha 57 , 58 and composite reliability (CR) 51 , 59 , 60 for each variable as above 0.8, which indicates good internal consistency of the questionnaire’s questions scale in measuring a similar variable. * indicates CR>0.95 but there are no significant changes after its removal 51 . The average variance extracted (AVE) indices 61 are greater than 0.5 for each variable, indicating no convergent validity problems. Table 6. Cronbach’s alpha, composite reliability and average variance extracted. Cronbach’s alpha Composite reliability AVE Item CM 0.838 0.839 0.566 4 PEOU 0.916 0.916 0.732 4 TSO 0.883 0.883 0.654 4 PSO 0.911 0.911 0.718 4 ATT 0.926 0.927 0.809 3 BI 0.920 0.920 0.793 3 CI 0.964 0.964 * 0.899 3 * indicates CR > 0.95. In Table 7 Fornell-Larcker criterion 61 , 62 , the diagonals represent the square root of AVE and off diagonals represent the coefficient of correlation. One tail t-test is conducted on the coefficient of correlation at 5% level of significance. The results revealed that there is a positive correlation between the variables with p -value of 0. There are no discriminant validity issues with the support of HTMT values, recorded in Table 8 based on HTMT 0.90 criterions 63 . Table 7. Fornell-Larcker criterion. CM PEOU TSO PSO ATT BI CI CM 0.752 PEOU 0.737 0.855 TSO 0.699 0.661 0.809 PSO 0.522 0.534 0.644 0.847 ATT 0.730 0.610 0.677 0.577 0.899 BI 0.758 0.615 0.678 0.542 0.859 0.891 CI 0.607 0.587 0.675 0.565 0.763 0.821 0.948 Table 8. Heterotrait-Monotrait ratio (HTMT). CM PEOU TSO PSO ATT BI CI CM PEOU 0.737 TSO 0.698 0.661 PSO 0.542 0.534 0.646 ATT 0.732 0.609 0.676 0.576 BI 0.757 0.615 0.677 0.542 0.859 CI 0.606 0.587 0.674 0.565 0.763 0.821 Consistent partial least square (PLSc) path modelling approach Six hypotheses were tested using PLSc 53 , a variance-based structural equation modelling technique, with no concerns about distribution or multicollinearity. In the past decade, the use of PLS modelling has gradually increased in order to handle more complex models. Table 9 summarises the result of the hypotheses presented in Figure 3 , which indicates the path coefficient and outer loading of the variable. PEOU is found to be insignificant in influencing consumers’ attitude towards OFDS ( p -value > 0.05). Consumers’ attitude towards using OFDS during and post the COVID-19 pandemic is, however, positively influenced by CM ( p -value < 0.05), TSO ( p -value < 0.05) and PSO ( p -value < 0.05). Furthermore, hypotheses of ATT positively influencing consumers’ BI ( p -value < 0.05) and also BI positively influencing consumers’ CI ( p -value < 0.05) towards OFDS are supported in this study. Thus, H1, H3, H4, H5 and H6 are validated while H2 is rejected. Table 9. Summary of hypotheses testing. Hypothesis Path t-value p -value Decisions f 2 Q 2 H1 CM-->ATT 4.119 0.000 Supported 0.134 0.449 H2 PEOU-->ATT 0.287 0.774 Rejected 0.012 H3 TSO-->ATT 2.187 0.029 Supported 0.055 H4 PSO-->ATT 2.370 0.018 Supported 0.046 H5 ATT-->BI 26.390 0.000 Supported 1.706 0.536 H6 BI-->CI 23.985 0.000 Supported 1.493 0.555 Figure 3. Part coefficient and outer loading. To test the model quality, effect size, f 2 and predictive relevance, Q 2 is measured. All the f 2 values are greater than 0.02 except the path of PEOU→ATT, which indicates no effect toward ATT 64 , 65 . The predictive relevance, Q 2 is used to determine the predictive power of dependent variables. All the Q 2 values are greater than 0.35 66 . This means there is substantial predictive relevance in this model. Discussion Based on the findings of this study, convenience motivation has a significant impact on consumers’ attitude towards OFDS, which is consistent with previous studies 10 , 11 , 18 , 22 , 29 , 31 – 33 . OFDS platforms are very well developed nowadays, enabling consumers to order food online at any time and from any location, without having to leave home. With just a click and via a cashless payment system, food will be ready in a short period of time, providing consumers with a great deal of convenience. However, electronic devices have already been integrated into our daily routines for a long time and people are already familiar with these devices, thus perceived ease of use is not a significant motivator that would influence consumers to continue ordering food online 12 , 17 , 29 , 67 . Time is an important factor that consumers, particularly working adults and students, are concerned about 10 , 20 , 29 . Consumers are eager to use OFDS because they can save a significant amount of time from menu selection to food preparation. Especially during rush hour, OFDS will be their first choice rather than waiting in line at a restaurant. OFDS also saves consumers money, as they can compare the prices offered by different food retailers and budget for a meal. Food retailers must continue to offer competitive price, such as giving attractive discount coupons or free delivery services to influence consumers to revisit 11 . With the assistance of third-party apps, price-saving orientation significantly influences consumers’ attitude towards OFDS continuance intention after the pandemic 10 , but perhaps not for all students 20 . Previous studies conducted in this field of study have focused on the general intention of using OFDS 14 , 18 , 67 . This paper, however, investigates consumers’ attitude and behaviour regarding their continuance intention of using OFDS after the COVID-19 pandemic. The left-skewed distribution of continuance intention’s measurement items significantly indicates that there is a high possibility of consumers using OFDS continuously after COVID-19, and this supports the hypothesis that a positive behavioural intention will lead to continuance of using a service. A satisfying online shopping experience fosters a positive attitude toward using the services and, as a result, always increases the likelihood of future purchase behaviour 21 , 68 , 69 . Furthermore, many previous studies have integrated TPB & TAM, whether it is to investigate mobile banking adoption among Palestinian customers 70 , consumer’s willingness to adopt online food in Italy 18 , university students’ intention to use mobile learning in Ghana and Colombia 71 , 72 , Indian commuters’ willingness to use carsharing app 73 , Indonesians’ intention to use bicycles 74 , changes in behaviour of e-wallet users during the COVID-19 pandemic among Indonesians 75 , or Vietnamese consumers’ online purchase intention 76 to name a few. However, to the best of the authors’ knowledge at the time of writing, very few or no studies have included the convenience factor in the integrated research model. In this study, convenience motivation, perceived ease of use, time-saving orientation and price-saving orientation were added to explore how convenience affects the consumers’ behaviour in incorporating OFDS into their lifestyle. In this day and age, people always strive for simplicity and ease in their lives. They are motivated to minimise discomfort, inconvenience, and hassle. They prefer solutions that reduce stress, inconvenience and the need for complex decision-making. Therefore, consumers are drawn to options, products, or services that make their lives simpler and easier. They often prefer choices that require minimal effort and are straightforward to use or access. Besides that, consumers always look for ways to optimise their resource allocation, whether it is time, money, or effort. They seek solutions that provide value for their investment. In line with this, there is a significant emphasis on timesaving. Consumers value options that help them save time in their daily tasks and activities. In addition, while convenience is a primary motivator, consumers also consider economic factors. They are interested in options that offer cost savings and provide value for their money. Overall, by adding these four variables into the research model, it provides insights into why consumers make particular choices and how they prioritise convenience in various aspects of their lives. It is a valuable framework for understanding consumer behaviour, product design, and service delivery in a wide range of contexts, from OFDS to technology adoption and beyond, especially after the unprecedented pandemic. Limitations This study did not take into account all of the possible factors that might influence the continuance intention of using OFDS after the pandemic. The model could be improved in the future by including more variables, such as, customer satisfaction and social influences. Furthermore, the findings cannot be generalised as a whole due to convenience sampling biasness. In the future, the study could be narrowed down to a specific group; perhaps looking at some larger cities with higher demand and supply for OFDS. Conclusions OFDS is a consumer-focused market which aims to bring comfort to consumers so that they are able to get their favourite food at the best price and convenience without having to leave home. This is consistent with our findings that convenience motivation, time-saving orientation and price-saving orientation were the primary factors influencing consumers’ attitude towards OFDS during and post the COVID-19 pandemic. The findings also revealed that consumers who have a positive attitude and behaviour towards OFDS tend to have favourable feedback on the continuance intention after COVID-19. Nevertheless, although results showed that there is a significant impact on the continuance intention towards OFDS after COVID-19, there are several issues and challenges that need to be addressed. Food retailers should consider how to retain the food quality and ensure fast delivery when orders increase. They should also look into collaboration with third-party apps such as GrabFood and Foodpanda to help boost their sales and maximise profits. We believe that consumers will soon adopt OFDS into their lifestyle, making it a norm, after the pandemic. Therefore, it is crucial for food retailers to work in this direction to sustain and grow their business model. Data availability Underlying data Figshare: Online Food Delivery Service. DOI: http://doi.org/10.6084/m9.figshare.14772951 55 . This project contains the following underlying data: Data file 1. (Survey results, CVS format) Extended data Figshare: Online Food Delivery Service Questionnaire 2021 DOI: http://doi.org/10.6084/m9.figshare.16566414 77 . This project contains the following extended data: Data file 1. (Survey questions, CVS format) Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). Acknowledgments We would like to thank all the participants in this research for their voluntary participation. Faculty Opinions recommended References 1. World Health Organization (WHO): Impact of COVID-19 on people’s livelihoods, their health and our food systems. 2020. Reference Source 2. Department of Statistics Malaysia: Labour force survey report, Malaysia, 2020. [Press Release], 2021. Reference Source 3. United Nations: Goal 1: end poverty in all its forms everywhere. n.d, Retrieved June 26, 2022. Reference Source 4. Kwak DH, Jain H: The role of web and E-commerce in poverty reduction: a framework based on ecological systems theory. Workshop on E-Business. 2015; 143–154. Publisher Full Text 5. We Are Social & Hootsuite: Digital 2021: Malaysia. 2021. Reference Source 6. Kamel H: Food delivery services: from odd job to the most in demand. The Malaysian Reserve. January 1, 2021. Reference Source 7. Kamaruddin NJ: Positivity in negativity: COVID-19 sees 30 pct jump in delivery orders. BERNAMA. March 18, 2020. Reference Source 8. Money Compass: Online Food Delivery market to see robust growth over next 4 years. August 7, 2020. 9. Shankar A, Jebarajakirthy C, Nayal P, et al. : Online Food Delivery: a systematic synthesis of literature and a framework development. Int J Hosp Manag. 2022; 104 : 103240. Publisher Full Text 10. Yeo VCS, Goh SK, Rezaei S: Consumer experiences, attitude and behavioral intention toward Online Food Delivery (OFD) Services. J Retail Consum Serv. 2017; 35 : 150–162. Publisher Full Text 11. Vinish P, Pinto P, Hawaldar IT, et al. : Antecedents of behavioral intention to use Online Food Delivery services: an empirical investigation. Innov Mark. 2021; 17 (1): 1–15. Publisher Full Text 12. Alalwan AA: Mobile Food Ordering Apps: an empirical study of the factors affecting customer E-satisfaction and continued intention to reuse. Int J Inf Manag. 2020; 50 : 28–44. Publisher Full Text 13. Ray A, Dhir A, Bala PK, et al. : Why do people use Food Delivery Apps (FDA)? A uses and gratification theory perspective. J Retail Consum Serv. 2019; 51 : 221–230. Publisher Full Text 14. Zhao Y, Bacao F: What factors determining customer continuingly using Food Delivery Apps during 2019 novel coronavirus pandemic period? Int J Hosp Manag. 2020; 91 : 102683. PubMed Abstract | Publisher Full Text | Free Full Text 15. Nayan NM, Hassan MKA: Customer satisfaction evaluation for online food service delivery system in Malaysia. J Inf Syst Technol Manag. 2020; 5 (9): 123–136. Publisher Full Text 16. Al Amin M, Arefin MS, Sultana N, et al. : Evaluating the customers’ dining attitudes, e-satisfaction and continuance intention toward Mobile Food Ordering Apps (MFOAs): evidence from Bangladesh. European Journal of Management and Business Economics. 2020; 30 (2): 211–229. Publisher Full Text 17. Cho YC, Sagynov E: Exploring factors that affect usefulness, ease of use, trust, and purchase intention in the online environment. Int J Manag Inf Syst. 2015; 19 (1): 21–36. Publisher Full Text 18. Troise C, O’Driscoll A, Tani M, et al. : Online Food Delivery services and behavioural intention - a test of an integrated TAM and TPB framework. Br Food J. 2021; 123 (2): 664–683. Reference Source 19. Punj G: Income effects on relative importance of two online purchase goals: saving time versus saving money? J Bus Res. 2012; 65 (5): 634–640. Publisher Full Text 20. Hooi R, Tang KL, Lai HY: Intention to use Online Food Delivery service in Malaysia among university students. CoMBInES - Conf Manag Bus Innov Edu Soc Sci. 2021; 1 (1): 60–73. Reference Source 21. Chen YM, Hsu TH, Lu YJ: Impact of flow on mobile shopping intention. J Retail Consum Serv. 2018; 41 : 281–287. Publisher Full Text 22. Brewer P, Sebby AG: The effect of online restaurant menus on consumers’ purchase intentions during the COVID-19 pandemic. Int J Hosp Manag. 2021; 94 : 102777. PubMed Abstract | Publisher Full Text | Free Full Text 23. Wang H, Zhang J, Luximon Y, et al. : The determinants of user acceptance of Mobile Medical Platforms: an investigation integrating the TPB, TAM, and patient-centered factors. Int J Environ Res Public Health. 2022; 19 (17): 10758. PubMed Abstract | Publisher Full Text | Free Full Text 24. Abu-Taieh EM, AlHadid I, Abu-Tayeh S, et al. : Continued intention to use of m-banking in Jordan by integrating UTAUT, TPB, TAM and Service Quality with ML. J Open Innov Technol Mark Complex. 2022; 8 (3): 120. Publisher Full Text 25. Mustafa MH, Ahmad MB, Shaari ZH, et al. : Integration of TAM, TPB, and TSR in understanding library user behavioral utilization intention of physical vs. E-book format. J Acad Libr. 2021; 47 (5): 102399. Publisher Full Text 26. Davis FD: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989; 13 (3): 319–340. Publisher Full Text 27. Ajzen I: The theory of planned behavior. Organ Behav Hum Decis Process. 1991; 50 (2): 179–211. Publisher Full Text 28. Ajzen L, Fishbein M: Understanding attitudes and predicting social behavior. Prentice-Hall, 1980. Reference Source 29. Lau TC, Ng DCY: Online Food Delivery services: making food delivery the new normal. J Mark Adv Pract. 2019; 1 (1): 62–77. Reference Source 30. Jayadevan GR, Thamaraiselvan N, Chandrasekar KS: Digital Food Delivery Apps revolutionizing food products marketing in India. Int J Rec Tech Eng. 2019; 8 (2S6): 662–665. Publisher Full Text 31. Google, Temasek and Bain: e-Conomy SEA 2020. 2020. Reference Source 32. Balmaceda K, Leong B: Who are Malaysia’s online shoppers? Janio Asia; 2021. Reference Source 33. Boice M: The 13 top reasons consumers shop online. Jungle Scout; 2021. Reference Source 34. Preetha S, Iswarya S: An analysis of user convenience towards food online order and delivery application (Food app via platforms). Inter J Manag Techn Eng. 2019; IX (I): 429–433. Reference Source 35. Meuter ML, Ostrom AL, Bitner MJ, et al. : The influence of technology anxiety on consumer use and experiences with self-service technologies. J Bus Res. 2003; 56 (11): 899–906. Publisher Full Text 36. Nagle TT, Hogan J, Zale J: The strategy and tactics to pricing: a guide to growing more profitably. 5th ed: Routledge; 2016. Publisher Full Text 37. Sabilillah JU, Usman O: The effect of service quality, price and facilities on visitor satisfaction in Ragunan. 2021. Publisher Full Text 38. Chiu CM, Wang ETG, Fang YH, et al. : Understanding customers’ repeat purchase intentions in B2C e-commerce: the roles of utilitarian value, hedonic value and perceived risk. Inf Syst J. 2014; 24 (1): 85–114. Publisher Full Text 39. Fishbein M, Ajzen I: Belief, attitude, intention and behavior: an introduction to theory and research. Philos Rhetor. 1977; 10 (2): 130–132. Reference Source 40. Ramadani V, Demiri A, Saiti-Demiri S: Social media channels: the factors that influence the behavioural intention of customers. Int J Bus Global. 2014; 12 (3): 297–314. Publisher Full Text 41. Brown SA, Venkatesh V: Model of adoption of technology in households: a baseline model test and extension incorporating household life cycle. MIS Q. 2005; 29 (3): 399–426. Publisher Full Text 42. Othman B: The influence of technology acceptance model on behavioral intention to use internet banking system. [Master’s thesis, Universiti Teknologi Malaysia] (January 2013). 2013. Reference Source 43. Cho M, Bonn MA, Li JJ: Differences in perceptions about food delivery apps between single-person and multi-person households. Int J Hosp Manag. 2019; 77 : 108–116. Publisher Full Text 44. Ganesh J, Reynolds KE, Luckett M, et al. : Online shopper motivations, and e-store attributes: an examination of online patronage behavior and shopper typologies. J Retail. 2010; 86 (1): 106–115. Publisher Full Text 45. Liébana-Cabanillas F, Marinković V, Kalinić Z: A SEM-neural network approach for predicting antecedents of m-commerce acceptance. Int J Inf Manag. 2017; 37 (2): 14–24. Publisher Full Text 46. Gliem JA, Gliem RR: Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. [Paper Presentation]. 2003 Midwest Research to Practice Conference in Adult, Continuing, and Community Education, The Ohio State University, Columbus, OH, 2003, October 8-10. Reference Source 47. Subedi BP: Using Likert type data in social science research: confusion, issues and challenges. Int J Contemporary App Sci. 2016; 3 (2): 36–49. Reference Source 48. Etikan I, Musa SA, Alkassim RS: Comparison of convenience sampling and purposive sampling. Am J Theor Appl Stat. 2016; 5 (1): 1–4. Publisher Full Text 49. Tongco MDC: Purposive sampling as a tool for informant selection. 2007; 5 : 147–158. Reference Source 50. Barratt MJ, Ferris JA, Lenton S: Hidden populations, online purposive sampling, and external validity: taking off the blindfold. Field Methods. 2015; 27 (1): 3–21. Publisher Full Text 51. Hair JF Jr, Hult GTM, Ringle C, et al. : A primer on partial least squares structural equation modeling (PLS-SEM). 2nd ed: SAGE Publications; 2016. Reference Source 52. Wong KKK: Partial Least Squares Structural Equation Modeling (PLS-SEM) techniques using SmartPLS. Mark Bull. 2013; 24 (1): 1–32. Reference Source 53. Dijkstra TK, Henseler J: Consistent partial least squares path modeling. MIS Q. 2015; 39 (2): 297–316. Reference Source 54. Hair JF, Ringle CM, Sarstedt M: PLS-SEM: indeed a silver bullet. The Journal of Marketing Theory and Practice. 2011; 19 (2): 139–152. Publisher Full Text 55. Tan SY, Lim SY: Online food delivery services: consumers’ attitude towards continuance intention post COVID-19 pandemic_OFDS307.csv. figshare. Dataset. 2021. 2021. http://www.doi.org/10.6084/m9.figshare.14772951 56. Othman I, Bidin A, Hussain H: Facebook marketing strategy for small business in Malaysia. International Conference on Informatics and Creative Multimedia. 2013; 236–341. Publisher Full Text 57. Cortina JM: What is coefficient alpha? An examination of theory and applications. J Appl Psychol. 1993; 78 (1): 98–104. Publisher Full Text 58. Taber KS: The use of Cronbach’s alpha when developing and reporting research instruments in science education. Res Sci Educ. 2018; 48 : 1273–1296. Publisher Full Text 59. Fornell C, Larcker DF: Structural equation models with unobservable variables and measurement error: algebra and statistics. J Market Res. 1981; 18 (3): 382–388. Publisher Full Text 60. Aguirre-Urreta MI, Marakas GM, Ellis ME: Measurement of composite reliability in research using partial least squares: some issues and an alternative approach. DATABASE for Adv Inf Sys. 2013; 44 (4): 11–43. Publisher Full Text 61. Alarcón D, Sánchez JA, De Olavide U: Assessing convergent and discriminant validity in the ADHD-R IV rating scale: user-written commands for Average Variance Extracted (AVE), Composite Reliability (CR), and Heterotrait-Monotrait ratio of correlations (HTMT). [Paper Presentation]. Spanish STATA Meeting, Universidad Pablo de Olavide, October 22, 2015; 39 . . Reference Source 62. Ab Hamid MR, Sami W, Sidek MM: Discriminant validity assessment: use of Fornell & Larcker criterion versus HTMT criterion. J Phys Conf Ser. 2017; 890 (1): 012163. Publisher Full Text 63. Henseler J, Ringle CM, Sarstedt M: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Market Sci. 2015; 43 (1): 115–135. Publisher Full Text 64. Cohen J: Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum, 1988. Reference Source 65. Hair JF Jr, Sarstedt M, Hopkins L, et al. : Partial least squares structural equation modeling (PLS-SEM): An emerging tool in Business Research. European business review. 2014; 26 (2): 106–121. Publisher Full Text 66. Chin WW: The Partial Least Squares approach for structural equation modeling. Modern Methods for Business Research. 1998; 295 (2): 295–336. Reference Source 67. Lee SW, Sung HJ, Jeon HM: Determinants of continuous intention on food delivery apps: extending UTAUT2 with information quality. Sustainability. 2019; 11 (11): 3141. Publisher Full Text 68. Korzaan ML: Going with the flow: predicting online purchase intentions. J Comput Inf Syst. 2003; 43 (4): 25–31. Reference Source 69. Yang K: Determinants of US consumer mobile shopping services adoption: implications for designing mobile shopping services. J Consum Mark. 2010; 27 (3): 262–270. Publisher Full Text 70. Aldammagh Z, Abdeljawad R, Obaid T: Predicting mobile banking adoption: an integration of TAM and TPB with trust and perceived risk. Financial Internet Quarterly. 2021; 17 (3): 35–46. Publisher Full Text 71. Buabeng-Andoh C: Exploring university students’ intention to use mobile learning: a research model approach. Education and Information Technologies. 2021; 26 (1): 241–256. Publisher Full Text 72. Gómez-Ramirez I, Valencia-Arias A, Duque L: Approach to M-learning acceptance among university students: an integrated model of TPB and TAM. International Review of Research in Open and Distributed Learning. 2019; 20 (3): 141–164. Publisher Full Text 73. Haldar P, Goel PT: Willingness to use carsharing apps: an integrated TPB and TAM. International Journal of Indian Culture and Business Management. 2019; 19 (2): 129–146. Publisher Full Text 74. Irawan MZ, Bastarianto FF, Priyanto S: Using an integrated model of TPB and TAM to analyze the pandemic impacts on the intention to use bicycles in the post-COVID-19 period. IATSS Research. 2022; 46 (3): 380–387. Publisher Full Text 75. Astari A, Yasa N, Sukaatmadja I, et al. : Integration of Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB): an e-wallet behavior with fear of COVID-19 as a moderator variable. International Journal of Data and Network Science. 2022; 6 (4): 1427–1436. Publisher Full Text 76. Ha N, Nguyen T: The effect of trust on consumers’ online purchase intention: an integration of TAM and TPB. Management Science Letters. 2019; 9 (9): 1451–1460. Publisher Full Text 77. Tan SY, Lim SY: Online Food Delivery Services questionnaire 2021.csv. figshare. Dataset. 2021. http://www.doi.org/10.6084/m9.figshare.16566414 Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 27 Sep 2021 ADD YOUR COMMENT Comment Author details Author details 1 Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 2 Faculty of Business, Multimedia University, Melaka, 75450, Malaysia Sin Yin Tan Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Su Yin Lim Roles: Conceptualization, Data Curation, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Sook Fern Yeo Roles: Methodology, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 03 Mar 2026, 10:972 https://doi.org/10.12688/f1000research.73014.3 version 2 Revised Published: 08 May 2024, 10:972 https://doi.org/10.12688/f1000research.73014.2 version 1 Published: 27 Sep 2021, 10:972 https://doi.org/10.12688/f1000research.73014.1 Copyright © 2024 Tan SY et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Tan SY, Lim SY and Yeo SF. Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.12688/f1000research.73014.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 08 May 2024 Revised Views 0 Cite How to cite this report: Teck Chai L. Reviewer Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.165223.r283065 ) The direct URL for this report is: https://f1000research.com/articles/10-972/v2#referee-response-283065 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 11 Jul 2024 Lau Teck Chai , Entrepreneurship and Enterprise Hub, Xi'an Jiaotong-Liverpool University, Suzhou, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.165223.r283065 The version of the article has gone through revision based on the review from 2 previous reviewers. The authors have addressed all the major concerns pointed out earlier. However, there are some concerns that I would like to highlight (below) ... Continue reading READ ALL The version of the article has gone through revision based on the review from 2 previous reviewers. The authors have addressed all the major concerns pointed out earlier. However, there are some concerns that I would like to highlight (below) for further improvement. 1. Abstract - Under the Method section, the authors still did not change the data collection method (From convenience to purposive?) 2. Page 4 - "This comprehensive framework combines elements from the TAM and the TPB, augmenting them with additional variables" However, in the study's research model, some constructs were omitted (subjective norm and perceived behavioral control). The authors did not explained the reason(s) for the exclusion of the two constructs. 3. Furthermore, how do the new added constructs (convenience motivation, time saving orientation, price saving orientation) are decided upon to be included in the TAM+TPB model? Are the new constructs adopted from any established theoretical model? Any particular reason(s) for their inclusion? 4. Page 6 - Figure 1 Research Model Diagram - Attitude is to be represented by ATT and not AI on the diagram. 5. Page 6 - All items were measured based on a five-point Likert-type. Please specify how they are measured specifically. 6. Page 6 - In this study, purposive sampling method was applied because the selected samples are more representative of the population. In what way are they more representative? Most likely because respondents need to have experience in online food ordering? Do you have screening questionnaire? 7. Page 6 - The authors will need to explained that only those who have experience of purchasing online will be qualified to answer the survey. 8. Page 6 - All of them has experienced using OFDS and mostly are young adults between the age of 18 to 25 years old (40.63%). This criteria need to be explained earlier during the research design stage. 9. Page 7 - A lthough 71.88% of the users were earning a low income. It is unclear on the low income group and how much they earned. The Table only mentioned classification (B40). This classification need to be explained for non-Malaysian readers who are unfamiliar with such classification. 10. Page 10 - electronic devices have already been integrated into our daily routines for a long time and people are already familiar with these devices, thus perceived ease of use is not a significant motivator that would influence consumers to continue ordering food online. Please give suggestions to companies, and platform providers on what this means to them and what they need to do and be aware. 11. Page 11 - but perhaps not for all students. I am not sure about the relevance of this sentence, as the sample consists of respondents who are not only students. 12. Page 12 - It is a valuable framework for understanding consumer behaviour, product design, and service delivery in a wide range of contexts, from OFDS to technology adoption and beyond, especially after the unprecedented pandemic. What are some tangible suggestions for food retailers, platform providers, government/policy makers? Perspective and discussion provided are mainly from consumers perspective. 13. Following up on point 12 above, detailed suggestions for businesses and retailers would be appropriate as it will aligned with what the authors have mentioned in their introduction section (page 3), quoted below: If businesses, especially, could modify their business models to cater to the population at the bottom of the pyramid, they could play a significant role in alleviating poverty while still profiting. This would assist food retailers in positioning their products and services to capitalise on this emerging market. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Marketing, Consumer Behaviour I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Teck Chai L. Reviewer Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.165223.r283065 ) The direct URL for this report is: https://f1000research.com/articles/10-972/v2#referee-response-283065 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 03 Mar 2026 Sin Yin Tan , Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 03 Mar 2026 Author Response Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.We would like to apologise ... Continue reading Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.We would like to apologise for overlooking this when revising the manuscript previously. It has been amended accordingly in the Abstract section. 2.Although subjective norm (SN) and perceived behavioural control (PBC) are key components of the Theory of Planned Behaviour, their influence varies depending on the stage of technology use. Our study focuses on the continuance stage of OFDS, where users are already familiar with the platform. In this context, PBC is less influential because users already have the necessary skills and resources, and SN is less relevant as ordering food online is typically a private, routine behaviour. At this stage, continued use is primarily shaped by users’ evaluations of usefulness, convenience, and value rather than social pressure or perceived control. We have clarified this reasoning and strengthened the explanation in the revised Theoretical Background section to justify the model specification. 3.We have added Convenience motivation (CM), time-saving orientation (TSO), and price-saving orientation (PSO) into the model to better reflect today’s realities. The original TPB is great for explaining why someone starts using a technology, but we are moving beyond the broad technology adoption perspective and looking at the actual utilitarian and economic motivations that define routine OFDS use today. We have clarified this theoretical refinement in the revised manuscript to ensure the model reflects both established behavioural theory and the practical motivations of users in a post-pandemic OFDS environment. 4.“ATT” has been inserted accordingly in Figure 1. 5.We have added the clarification for the five-point Likert scale in the Questionnaire Development section. 6.The survey contains a screening question to ensure only respondents that have experience using online food delivery services proceed with the questionnaire. We have added a statement under the Data collection section. 7.Yes, this has been addressed in No. 6 – We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience using OFDS. 8.Already addressed in No. 6 - We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience1e have added a sentence under Results to indicate how B40, M540 and T20 are classified. 9.Thank you for pointing this out. We have added a sentence under Results to indicate how B40, M540 and T20 are classified. 10.Regarding perceived ease of use, our findings show it does not significantly drive continuance intention. This reflects that usability has become a baseline expectation in mature digital environments like OFDS. Consumers are already familiar with smartphones, apps, and digital payments, so ease of use now primarily prevents dissatisfaction rather than actively motivating continued use. We have clarified in the revised Discussion that platform providers should maintain stable, intuitive, and reliable interfaces while focusing differentiation on enhancing convenience and value, such as faster delivery, accurate orders, personalised recommendations, and seamless integration with payment and loyalty systems. This explanation has been added to provide practical guidance for stakeholders in post-pandemic OFDS markets. 11.We have removed the sentence to avoid confusion. 12.We agree that the findings should provide clearer practical implications. In the revised Discussion section, we have added a brief paragraph outlining how the results can inform platform providers, food retailers, and policymakers. 13.We thank the reviewer for highlighting the importance of bottom-of-the-pyramid consumers. In response, the Discussion now explicitly addresses strategies to reach this segment. Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.We would like to apologise for overlooking this when revising the manuscript previously. It has been amended accordingly in the Abstract section. 2.Although subjective norm (SN) and perceived behavioural control (PBC) are key components of the Theory of Planned Behaviour, their influence varies depending on the stage of technology use. Our study focuses on the continuance stage of OFDS, where users are already familiar with the platform. In this context, PBC is less influential because users already have the necessary skills and resources, and SN is less relevant as ordering food online is typically a private, routine behaviour. At this stage, continued use is primarily shaped by users’ evaluations of usefulness, convenience, and value rather than social pressure or perceived control. We have clarified this reasoning and strengthened the explanation in the revised Theoretical Background section to justify the model specification. 3.We have added Convenience motivation (CM), time-saving orientation (TSO), and price-saving orientation (PSO) into the model to better reflect today’s realities. The original TPB is great for explaining why someone starts using a technology, but we are moving beyond the broad technology adoption perspective and looking at the actual utilitarian and economic motivations that define routine OFDS use today. We have clarified this theoretical refinement in the revised manuscript to ensure the model reflects both established behavioural theory and the practical motivations of users in a post-pandemic OFDS environment. 4.“ATT” has been inserted accordingly in Figure 1. 5.We have added the clarification for the five-point Likert scale in the Questionnaire Development section. 6.The survey contains a screening question to ensure only respondents that have experience using online food delivery services proceed with the questionnaire. We have added a statement under the Data collection section. 7.Yes, this has been addressed in No. 6 – We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience using OFDS. 8.Already addressed in No. 6 - We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience1e have added a sentence under Results to indicate how B40, M540 and T20 are classified. 9.Thank you for pointing this out. We have added a sentence under Results to indicate how B40, M540 and T20 are classified. 10.Regarding perceived ease of use, our findings show it does not significantly drive continuance intention. This reflects that usability has become a baseline expectation in mature digital environments like OFDS. Consumers are already familiar with smartphones, apps, and digital payments, so ease of use now primarily prevents dissatisfaction rather than actively motivating continued use. We have clarified in the revised Discussion that platform providers should maintain stable, intuitive, and reliable interfaces while focusing differentiation on enhancing convenience and value, such as faster delivery, accurate orders, personalised recommendations, and seamless integration with payment and loyalty systems. This explanation has been added to provide practical guidance for stakeholders in post-pandemic OFDS markets. 11.We have removed the sentence to avoid confusion. 12.We agree that the findings should provide clearer practical implications. In the revised Discussion section, we have added a brief paragraph outlining how the results can inform platform providers, food retailers, and policymakers. 13.We thank the reviewer for highlighting the importance of bottom-of-the-pyramid consumers. In response, the Discussion now explicitly addresses strategies to reach this segment. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 03 Mar 2026 Sin Yin Tan , Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 03 Mar 2026 Author Response Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.We would like to apologise ... Continue reading Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.We would like to apologise for overlooking this when revising the manuscript previously. It has been amended accordingly in the Abstract section. 2.Although subjective norm (SN) and perceived behavioural control (PBC) are key components of the Theory of Planned Behaviour, their influence varies depending on the stage of technology use. Our study focuses on the continuance stage of OFDS, where users are already familiar with the platform. In this context, PBC is less influential because users already have the necessary skills and resources, and SN is less relevant as ordering food online is typically a private, routine behaviour. At this stage, continued use is primarily shaped by users’ evaluations of usefulness, convenience, and value rather than social pressure or perceived control. We have clarified this reasoning and strengthened the explanation in the revised Theoretical Background section to justify the model specification. 3.We have added Convenience motivation (CM), time-saving orientation (TSO), and price-saving orientation (PSO) into the model to better reflect today’s realities. The original TPB is great for explaining why someone starts using a technology, but we are moving beyond the broad technology adoption perspective and looking at the actual utilitarian and economic motivations that define routine OFDS use today. We have clarified this theoretical refinement in the revised manuscript to ensure the model reflects both established behavioural theory and the practical motivations of users in a post-pandemic OFDS environment. 4.“ATT” has been inserted accordingly in Figure 1. 5.We have added the clarification for the five-point Likert scale in the Questionnaire Development section. 6.The survey contains a screening question to ensure only respondents that have experience using online food delivery services proceed with the questionnaire. We have added a statement under the Data collection section. 7.Yes, this has been addressed in No. 6 – We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience using OFDS. 8.Already addressed in No. 6 - We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience1e have added a sentence under Results to indicate how B40, M540 and T20 are classified. 9.Thank you for pointing this out. We have added a sentence under Results to indicate how B40, M540 and T20 are classified. 10.Regarding perceived ease of use, our findings show it does not significantly drive continuance intention. This reflects that usability has become a baseline expectation in mature digital environments like OFDS. Consumers are already familiar with smartphones, apps, and digital payments, so ease of use now primarily prevents dissatisfaction rather than actively motivating continued use. We have clarified in the revised Discussion that platform providers should maintain stable, intuitive, and reliable interfaces while focusing differentiation on enhancing convenience and value, such as faster delivery, accurate orders, personalised recommendations, and seamless integration with payment and loyalty systems. This explanation has been added to provide practical guidance for stakeholders in post-pandemic OFDS markets. 11.We have removed the sentence to avoid confusion. 12.We agree that the findings should provide clearer practical implications. In the revised Discussion section, we have added a brief paragraph outlining how the results can inform platform providers, food retailers, and policymakers. 13.We thank the reviewer for highlighting the importance of bottom-of-the-pyramid consumers. In response, the Discussion now explicitly addresses strategies to reach this segment. Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.We would like to apologise for overlooking this when revising the manuscript previously. It has been amended accordingly in the Abstract section. 2.Although subjective norm (SN) and perceived behavioural control (PBC) are key components of the Theory of Planned Behaviour, their influence varies depending on the stage of technology use. Our study focuses on the continuance stage of OFDS, where users are already familiar with the platform. In this context, PBC is less influential because users already have the necessary skills and resources, and SN is less relevant as ordering food online is typically a private, routine behaviour. At this stage, continued use is primarily shaped by users’ evaluations of usefulness, convenience, and value rather than social pressure or perceived control. We have clarified this reasoning and strengthened the explanation in the revised Theoretical Background section to justify the model specification. 3.We have added Convenience motivation (CM), time-saving orientation (TSO), and price-saving orientation (PSO) into the model to better reflect today’s realities. The original TPB is great for explaining why someone starts using a technology, but we are moving beyond the broad technology adoption perspective and looking at the actual utilitarian and economic motivations that define routine OFDS use today. We have clarified this theoretical refinement in the revised manuscript to ensure the model reflects both established behavioural theory and the practical motivations of users in a post-pandemic OFDS environment. 4.“ATT” has been inserted accordingly in Figure 1. 5.We have added the clarification for the five-point Likert scale in the Questionnaire Development section. 6.The survey contains a screening question to ensure only respondents that have experience using online food delivery services proceed with the questionnaire. We have added a statement under the Data collection section. 7.Yes, this has been addressed in No. 6 – We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience using OFDS. 8.Already addressed in No. 6 - We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience1e have added a sentence under Results to indicate how B40, M540 and T20 are classified. 9.Thank you for pointing this out. We have added a sentence under Results to indicate how B40, M540 and T20 are classified. 10.Regarding perceived ease of use, our findings show it does not significantly drive continuance intention. This reflects that usability has become a baseline expectation in mature digital environments like OFDS. Consumers are already familiar with smartphones, apps, and digital payments, so ease of use now primarily prevents dissatisfaction rather than actively motivating continued use. We have clarified in the revised Discussion that platform providers should maintain stable, intuitive, and reliable interfaces while focusing differentiation on enhancing convenience and value, such as faster delivery, accurate orders, personalised recommendations, and seamless integration with payment and loyalty systems. This explanation has been added to provide practical guidance for stakeholders in post-pandemic OFDS markets. 11.We have removed the sentence to avoid confusion. 12.We agree that the findings should provide clearer practical implications. In the revised Discussion section, we have added a brief paragraph outlining how the results can inform platform providers, food retailers, and policymakers. 13.We thank the reviewer for highlighting the importance of bottom-of-the-pyramid consumers. In response, the Discussion now explicitly addresses strategies to reach this segment. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Joung HW. Reviewer Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.165223.r275580 ) The direct URL for this report is: https://f1000research.com/articles/10-972/v2#referee-response-275580 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 08 Jun 2024 Hyun-Woo Joung , The University of Mississippi, Lubbock, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.165223.r275580 The authors have addressed the previous comments, and the current revised manuscript meets the necessary standards. The ... Continue reading READ ALL The authors have addressed the previous comments, and the current revised manuscript meets the necessary standards. The manuscript is now acceptable for indexing. Thank you for your efforts in making the required revisions. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Consumer behavior in the hospitality industry I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Joung HW. Reviewer Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.165223.r275580 ) The direct URL for this report is: https://f1000research.com/articles/10-972/v2#referee-response-275580 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 28 Jun 2024 Sin Yin Tan , Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 28 Jun 2024 Author Response Thanks again for your feedbacks. Competing Interests: No competing interests were disclosed. Thanks again for your feedbacks. Thanks again for your feedbacks. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 28 Jun 2024 Sin Yin Tan , Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 28 Jun 2024 Author Response Thanks again for your feedbacks. Competing Interests: No competing interests were disclosed. Thanks again for your feedbacks. Thanks again for your feedbacks. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 27 Sep 2021 Views 0 Cite How to cite this report: Joung HW. Reviewer Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.76632.r258703 ) The direct URL for this report is: https://f1000research.com/articles/10-972/v1#referee-response-258703 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 May 2024 Hyun-Woo Joung , The University of Mississippi, Lubbock, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.76632.r258703 Introduction: Strengthen the introduction by providing more rationale for the study and identifying research gaps. Clarify whether the study was conducted recently or in 2021, and update statistics accordingly. Address the discrepancy ... Continue reading READ ALL Introduction: Strengthen the introduction by providing more rationale for the study and identifying research gaps. Clarify whether the study was conducted recently or in 2021, and update statistics accordingly. Address the discrepancy between data collection during the pandemic in 2021 and discussing the impact "after" the pandemic. Literature Review: Expand the literature review to provide more in-depth analysis of potential predictor variables or offer detailed reasoning for the chosen variables. Strengthen the hypotheses development section by providing clear rationale for each hypothesis. Consider adopting a relevant theory (e.g., UTAUT, TAM) to support the conceptual model. Methods: Provide a brief explanation of the Krejcie and Morgan sampling method to enhance understanding. Results: Address ethical concerns regarding participants under the age of 18 and clarify how their data were handled. Explain how participants who have not used OFDS before were able to answer questions related to "Continuance intention." Evaluate the necessity of Table 3 and consider its relevance to the study. Enhance Table 5 by providing additional statistical evidence, such as chi-square analysis, in addition to ratios. Explain the discrepancy in Table 7 where CM's square root of AVE is smaller than the correlation between CM and PEOU. Discussion and Conclusion: Offer a more in-depth discussion that goes beyond repeating the findings already discussed in the results section. Strengthen the practical and theoretical implications. Overall: Acknowledge the importance of the topic in the restaurant industry. Suggest improvements to enhance the quality of the manuscript, including professional proofreading. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Consumer behavior in the hospitality industry I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Joung HW. Reviewer Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.76632.r258703 ) The direct URL for this report is: https://f1000research.com/articles/10-972/v1#referee-response-258703 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 28 May 2024 Sin Yin Tan , Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 28 May 2024 Author Response Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.Introduction: The ... Continue reading Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.Introduction: The research gaps and contributions of our study have been restructured to include the justification for the research under the introduction section. The study was conducted in 2021 as stated in our article. In future, we plan to extend our study on the impact “after” the pandemic in the OFDS domain. 2.Literature Review: Theoretical Framework has been included under the Literature Review section. Our selected variables were based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Detailed elaboration was further described in this section. 3.Methods: Purposive sampling method was applied in our study and clarified under Data collection of the Methods section. The sampling size was based on the other studies in the same domain. 4.Results: Research ethics approval was obtained from Multimedia University, Malaysia (EA1422021) and the respondents gave their written informed consent when filling out the Google Form. The survey data underwent reanalysis by excluding those who did not have any experience using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under the Result section. Table 3 illustrates feedback of respondents for each measurement item. The objective is to show that there is a significance left-skewed for each measurement item except PSO4. In this study, descriptive study is maybe more meaningful for certain variables such as the mature adults (above 41 years old), and B40 respondents (personal Income level) preferred to dine at home. The preference of dining at home is analysed based on their demographic so that sellers can approach these groups of people. In Table 7, CM's square root of AVE is greater than the correlation between CM and PEOU. 5.Discussion and Conclusion: Discussion and conclusion section have been further improved by explaining how this study contribute to existing theoretical frameworks based on the proposed research model. Further descriptions of related works in this domain were also provided. 6.Overall: Thank you for your suggestion. A significant revision had been undertaken which involves restructuring of the research study based on the theoretical framework and subsequent analysis. Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.Introduction: The research gaps and contributions of our study have been restructured to include the justification for the research under the introduction section. The study was conducted in 2021 as stated in our article. In future, we plan to extend our study on the impact “after” the pandemic in the OFDS domain. 2.Literature Review: Theoretical Framework has been included under the Literature Review section. Our selected variables were based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Detailed elaboration was further described in this section. 3.Methods: Purposive sampling method was applied in our study and clarified under Data collection of the Methods section. The sampling size was based on the other studies in the same domain. 4.Results: Research ethics approval was obtained from Multimedia University, Malaysia (EA1422021) and the respondents gave their written informed consent when filling out the Google Form. The survey data underwent reanalysis by excluding those who did not have any experience using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under the Result section. Table 3 illustrates feedback of respondents for each measurement item. The objective is to show that there is a significance left-skewed for each measurement item except PSO4. In this study, descriptive study is maybe more meaningful for certain variables such as the mature adults (above 41 years old), and B40 respondents (personal Income level) preferred to dine at home. The preference of dining at home is analysed based on their demographic so that sellers can approach these groups of people. In Table 7, CM's square root of AVE is greater than the correlation between CM and PEOU. 5.Discussion and Conclusion: Discussion and conclusion section have been further improved by explaining how this study contribute to existing theoretical frameworks based on the proposed research model. Further descriptions of related works in this domain were also provided. 6.Overall: Thank you for your suggestion. A significant revision had been undertaken which involves restructuring of the research study based on the theoretical framework and subsequent analysis. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 28 May 2024 Sin Yin Tan , Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 28 May 2024 Author Response Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.Introduction: The ... Continue reading Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.Introduction: The research gaps and contributions of our study have been restructured to include the justification for the research under the introduction section. The study was conducted in 2021 as stated in our article. In future, we plan to extend our study on the impact “after” the pandemic in the OFDS domain. 2.Literature Review: Theoretical Framework has been included under the Literature Review section. Our selected variables were based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Detailed elaboration was further described in this section. 3.Methods: Purposive sampling method was applied in our study and clarified under Data collection of the Methods section. The sampling size was based on the other studies in the same domain. 4.Results: Research ethics approval was obtained from Multimedia University, Malaysia (EA1422021) and the respondents gave their written informed consent when filling out the Google Form. The survey data underwent reanalysis by excluding those who did not have any experience using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under the Result section. Table 3 illustrates feedback of respondents for each measurement item. The objective is to show that there is a significance left-skewed for each measurement item except PSO4. In this study, descriptive study is maybe more meaningful for certain variables such as the mature adults (above 41 years old), and B40 respondents (personal Income level) preferred to dine at home. The preference of dining at home is analysed based on their demographic so that sellers can approach these groups of people. In Table 7, CM's square root of AVE is greater than the correlation between CM and PEOU. 5.Discussion and Conclusion: Discussion and conclusion section have been further improved by explaining how this study contribute to existing theoretical frameworks based on the proposed research model. Further descriptions of related works in this domain were also provided. 6.Overall: Thank you for your suggestion. A significant revision had been undertaken which involves restructuring of the research study based on the theoretical framework and subsequent analysis. Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.Introduction: The research gaps and contributions of our study have been restructured to include the justification for the research under the introduction section. The study was conducted in 2021 as stated in our article. In future, we plan to extend our study on the impact “after” the pandemic in the OFDS domain. 2.Literature Review: Theoretical Framework has been included under the Literature Review section. Our selected variables were based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Detailed elaboration was further described in this section. 3.Methods: Purposive sampling method was applied in our study and clarified under Data collection of the Methods section. The sampling size was based on the other studies in the same domain. 4.Results: Research ethics approval was obtained from Multimedia University, Malaysia (EA1422021) and the respondents gave their written informed consent when filling out the Google Form. The survey data underwent reanalysis by excluding those who did not have any experience using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under the Result section. Table 3 illustrates feedback of respondents for each measurement item. The objective is to show that there is a significance left-skewed for each measurement item except PSO4. In this study, descriptive study is maybe more meaningful for certain variables such as the mature adults (above 41 years old), and B40 respondents (personal Income level) preferred to dine at home. The preference of dining at home is analysed based on their demographic so that sellers can approach these groups of people. In Table 7, CM's square root of AVE is greater than the correlation between CM and PEOU. 5.Discussion and Conclusion: Discussion and conclusion section have been further improved by explaining how this study contribute to existing theoretical frameworks based on the proposed research model. Further descriptions of related works in this domain were also provided. 6.Overall: Thank you for your suggestion. A significant revision had been undertaken which involves restructuring of the research study based on the theoretical framework and subsequent analysis. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Khoa BT. Reviewer Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.76632.r100846 ) The direct URL for this report is: https://f1000research.com/articles/10-972/v1#referee-response-100846 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 21 Dec 2021 Bui Thanh Khoa , Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.76632.r100846 Thank you very much for the opportunity to review the study titled “Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic.” The selected topic is intriguing, and the work can add value ... Continue reading READ ALL Thank you very much for the opportunity to review the study titled “Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic.” The selected topic is intriguing, and the work can add value to the existing body of literature. However, certain flaws overshadow the paper’s potential significance. In the following lines, I offer some suggestions to this study as follows: One of the most pressing challenges is the theoretical research gap. Please provide a well-defined research gap. Furthermore, please explain why it is critical to include comprehensive poverty eradication in the theoretical contribution for the study area; hence, restructure the introduction. Typically, the framework will include the following elements: the significance of the issue, motivation (optional), research gap(s), aims, and possible contributions (optional). Lack of the research gap(s) reduces the paper’s value. This research used many constructs, such as Convenience motivation, Perceived ease of use, Time-saving orientation, Price-saving orientation, Attitude, Behavioural intention, and continuance intention; however, the reviewer cannot find the theoretical background in this research to connect them; hence, the proposed model is subjective and lacks scientific arguments. Please add the theoretical background in the first literature review. Table 2 shows the demographic profile of 307 respondents, of which there are 16.61% of respondents that have not used OFDS before. Why can they answer the research items regarding the continuance intention? The sampling method should be based on the research objective more than the prior research. The data analysis should add the f 2 and Q 2 values to check the effect size and predictive relevance. Limitation should be moved to the end of the paper. I firmly believe that the authors need a significant overhaul of the study, especially the theoretical framework and data process for ensuring the scientific validity of the article to make the manuscript suitable for indexing, given that it will be professionally revised before another submission. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: electronic commerce, online consumer behavior, marketing I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Khoa BT. Reviewer Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.76632.r100846 ) The direct URL for this report is: https://f1000research.com/articles/10-972/v1#referee-response-100846 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 28 May 2024 Sin Yin Tan , Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 28 May 2024 Author Response Thank you so much for the reviewer's comments. We sincerely appreciate the insightful feedback provided by the reviewer. To address the comments that highlighted by reviewer: The Introduction ... Continue reading Thank you so much for the reviewer's comments. We sincerely appreciate the insightful feedback provided by the reviewer. To address the comments that highlighted by reviewer: The Introduction section has been substantially revised by including more significance of the research issues and highlighting the research gaps and contributions of our study. The theoretical background is now positioned within the Literature Review section. We elaborate our study based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). The survey data underwent reanalysis by excluding those who did not have any experience of using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under Result section. Purposive sampling method was applied and clarified under Data collection of the Methods section. As suggested by reviewer, the f 2 and Q 2 values have been added in Table 9 under Result section to check the effect size and predictive relevance. Considering the journal editor's advice, the limitation was suggested to place before Conclusions section. A significant revision had been undertaken which involve restructuring of the research study based on the theoretical framework and subsequent analysis. Thank you. Thank you so much for the reviewer's comments. We sincerely appreciate the insightful feedback provided by the reviewer. To address the comments that highlighted by reviewer: The Introduction section has been substantially revised by including more significance of the research issues and highlighting the research gaps and contributions of our study. The theoretical background is now positioned within the Literature Review section. We elaborate our study based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). The survey data underwent reanalysis by excluding those who did not have any experience of using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under Result section. Purposive sampling method was applied and clarified under Data collection of the Methods section. As suggested by reviewer, the f 2 and Q 2 values have been added in Table 9 under Result section to check the effect size and predictive relevance. Considering the journal editor's advice, the limitation was suggested to place before Conclusions section. A significant revision had been undertaken which involve restructuring of the research study based on the theoretical framework and subsequent analysis. Thank you. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 28 May 2024 Sin Yin Tan , Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia 28 May 2024 Author Response Thank you so much for the reviewer's comments. We sincerely appreciate the insightful feedback provided by the reviewer. To address the comments that highlighted by reviewer: The Introduction ... Continue reading Thank you so much for the reviewer's comments. We sincerely appreciate the insightful feedback provided by the reviewer. To address the comments that highlighted by reviewer: The Introduction section has been substantially revised by including more significance of the research issues and highlighting the research gaps and contributions of our study. The theoretical background is now positioned within the Literature Review section. We elaborate our study based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). The survey data underwent reanalysis by excluding those who did not have any experience of using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under Result section. Purposive sampling method was applied and clarified under Data collection of the Methods section. As suggested by reviewer, the f 2 and Q 2 values have been added in Table 9 under Result section to check the effect size and predictive relevance. Considering the journal editor's advice, the limitation was suggested to place before Conclusions section. A significant revision had been undertaken which involve restructuring of the research study based on the theoretical framework and subsequent analysis. Thank you. Thank you so much for the reviewer's comments. We sincerely appreciate the insightful feedback provided by the reviewer. To address the comments that highlighted by reviewer: The Introduction section has been substantially revised by including more significance of the research issues and highlighting the research gaps and contributions of our study. The theoretical background is now positioned within the Literature Review section. We elaborate our study based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). The survey data underwent reanalysis by excluding those who did not have any experience of using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under Result section. Purposive sampling method was applied and clarified under Data collection of the Methods section. As suggested by reviewer, the f 2 and Q 2 values have been added in Table 9 under Result section to check the effect size and predictive relevance. Considering the journal editor's advice, the limitation was suggested to place before Conclusions section. A significant revision had been undertaken which involve restructuring of the research study based on the theoretical framework and subsequent analysis. Thank you. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 27 Sep 2021 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 3 (revision) 03 Mar 26 Version 2 (revision) 08 May 24 read read Version 1 27 Sep 21 read read Bui Thanh Khoa , Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam Hyun-Woo Joung , The University of Mississippi, Lubbock, USA Lau Teck Chai , Xi'an Jiaotong-Liverpool University, Suzhou, China Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Teck Chai L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 11 Jul 2024 | for Version 2 Lau Teck Chai , Entrepreneurship and Enterprise Hub, Xi'an Jiaotong-Liverpool University, Suzhou, China 0 Views copyright © 2024 Teck Chai L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The version of the article has gone through revision based on the review from 2 previous reviewers. The authors have addressed all the major concerns pointed out earlier. However, there are some concerns that I would like to highlight (below) for further improvement. 1. Abstract - Under the Method section, the authors still did not change the data collection method (From convenience to purposive?) 2. Page 4 - "This comprehensive framework combines elements from the TAM and the TPB, augmenting them with additional variables" However, in the study's research model, some constructs were omitted (subjective norm and perceived behavioral control). The authors did not explained the reason(s) for the exclusion of the two constructs. 3. Furthermore, how do the new added constructs (convenience motivation, time saving orientation, price saving orientation) are decided upon to be included in the TAM+TPB model? Are the new constructs adopted from any established theoretical model? Any particular reason(s) for their inclusion? 4. Page 6 - Figure 1 Research Model Diagram - Attitude is to be represented by ATT and not AI on the diagram. 5. Page 6 - All items were measured based on a five-point Likert-type. Please specify how they are measured specifically. 6. Page 6 - In this study, purposive sampling method was applied because the selected samples are more representative of the population. In what way are they more representative? Most likely because respondents need to have experience in online food ordering? Do you have screening questionnaire? 7. Page 6 - The authors will need to explained that only those who have experience of purchasing online will be qualified to answer the survey. 8. Page 6 - All of them has experienced using OFDS and mostly are young adults between the age of 18 to 25 years old (40.63%). This criteria need to be explained earlier during the research design stage. 9. Page 7 - A lthough 71.88% of the users were earning a low income. It is unclear on the low income group and how much they earned. The Table only mentioned classification (B40). This classification need to be explained for non-Malaysian readers who are unfamiliar with such classification. 10. Page 10 - electronic devices have already been integrated into our daily routines for a long time and people are already familiar with these devices, thus perceived ease of use is not a significant motivator that would influence consumers to continue ordering food online. Please give suggestions to companies, and platform providers on what this means to them and what they need to do and be aware. 11. Page 11 - but perhaps not for all students. I am not sure about the relevance of this sentence, as the sample consists of respondents who are not only students. 12. Page 12 - It is a valuable framework for understanding consumer behaviour, product design, and service delivery in a wide range of contexts, from OFDS to technology adoption and beyond, especially after the unprecedented pandemic. What are some tangible suggestions for food retailers, platform providers, government/policy makers? Perspective and discussion provided are mainly from consumers perspective. 13. Following up on point 12 above, detailed suggestions for businesses and retailers would be appropriate as it will aligned with what the authors have mentioned in their introduction section (page 3), quoted below: If businesses, especially, could modify their business models to cater to the population at the bottom of the pyramid, they could play a significant role in alleviating poverty while still profiting. This would assist food retailers in positioning their products and services to capitalise on this emerging market. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Marketing, Consumer Behaviour I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 03 Mar 2026 Sin Yin Tan, Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.We would like to apologise for overlooking this when revising the manuscript previously. It has been amended accordingly in the Abstract section. 2.Although subjective norm (SN) and perceived behavioural control (PBC) are key components of the Theory of Planned Behaviour, their influence varies depending on the stage of technology use. Our study focuses on the continuance stage of OFDS, where users are already familiar with the platform. In this context, PBC is less influential because users already have the necessary skills and resources, and SN is less relevant as ordering food online is typically a private, routine behaviour. At this stage, continued use is primarily shaped by users’ evaluations of usefulness, convenience, and value rather than social pressure or perceived control. We have clarified this reasoning and strengthened the explanation in the revised Theoretical Background section to justify the model specification. 3.We have added Convenience motivation (CM), time-saving orientation (TSO), and price-saving orientation (PSO) into the model to better reflect today’s realities. The original TPB is great for explaining why someone starts using a technology, but we are moving beyond the broad technology adoption perspective and looking at the actual utilitarian and economic motivations that define routine OFDS use today. We have clarified this theoretical refinement in the revised manuscript to ensure the model reflects both established behavioural theory and the practical motivations of users in a post-pandemic OFDS environment. 4.“ATT” has been inserted accordingly in Figure 1. 5.We have added the clarification for the five-point Likert scale in the Questionnaire Development section. 6.The survey contains a screening question to ensure only respondents that have experience using online food delivery services proceed with the questionnaire. We have added a statement under the Data collection section. 7.Yes, this has been addressed in No. 6 – We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience using OFDS. 8.Already addressed in No. 6 - We have added a statement under Data Collection section to indicate that there is a screening question to ensure those who answered the questionnaire has experience1e have added a sentence under Results to indicate how B40, M540 and T20 are classified. 9.Thank you for pointing this out. We have added a sentence under Results to indicate how B40, M540 and T20 are classified. 10.Regarding perceived ease of use, our findings show it does not significantly drive continuance intention. This reflects that usability has become a baseline expectation in mature digital environments like OFDS. Consumers are already familiar with smartphones, apps, and digital payments, so ease of use now primarily prevents dissatisfaction rather than actively motivating continued use. We have clarified in the revised Discussion that platform providers should maintain stable, intuitive, and reliable interfaces while focusing differentiation on enhancing convenience and value, such as faster delivery, accurate orders, personalised recommendations, and seamless integration with payment and loyalty systems. This explanation has been added to provide practical guidance for stakeholders in post-pandemic OFDS markets. 11.We have removed the sentence to avoid confusion. 12.We agree that the findings should provide clearer practical implications. In the revised Discussion section, we have added a brief paragraph outlining how the results can inform platform providers, food retailers, and policymakers. 13.We thank the reviewer for highlighting the importance of bottom-of-the-pyramid consumers. In response, the Discussion now explicitly addresses strategies to reach this segment. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Teck Chai L. Peer Review Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.165223.r283065) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/10-972/v2#referee-response-283065 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Joung H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 08 Jun 2024 | for Version 2 Hyun-Woo Joung , The University of Mississippi, Lubbock, USA 0 Views copyright © 2024 Joung H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have addressed the previous comments, and the current revised manuscript meets the necessary standards. The manuscript is now acceptable for indexing. Thank you for your efforts in making the required revisions. Competing Interests No competing interests were disclosed. Reviewer Expertise Consumer behavior in the hospitality industry I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 28 Jun 2024 Sin Yin Tan, Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia Thanks again for your feedbacks. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Joung HW. Peer Review Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.165223.r275580) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/10-972/v2#referee-response-275580 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Joung H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 May 2024 | for Version 1 Hyun-Woo Joung , The University of Mississippi, Lubbock, USA 0 Views copyright © 2024 Joung H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Introduction: Strengthen the introduction by providing more rationale for the study and identifying research gaps. Clarify whether the study was conducted recently or in 2021, and update statistics accordingly. Address the discrepancy between data collection during the pandemic in 2021 and discussing the impact "after" the pandemic. Literature Review: Expand the literature review to provide more in-depth analysis of potential predictor variables or offer detailed reasoning for the chosen variables. Strengthen the hypotheses development section by providing clear rationale for each hypothesis. Consider adopting a relevant theory (e.g., UTAUT, TAM) to support the conceptual model. Methods: Provide a brief explanation of the Krejcie and Morgan sampling method to enhance understanding. Results: Address ethical concerns regarding participants under the age of 18 and clarify how their data were handled. Explain how participants who have not used OFDS before were able to answer questions related to "Continuance intention." Evaluate the necessity of Table 3 and consider its relevance to the study. Enhance Table 5 by providing additional statistical evidence, such as chi-square analysis, in addition to ratios. Explain the discrepancy in Table 7 where CM's square root of AVE is smaller than the correlation between CM and PEOU. Discussion and Conclusion: Offer a more in-depth discussion that goes beyond repeating the findings already discussed in the results section. Strengthen the practical and theoretical implications. Overall: Acknowledge the importance of the topic in the restaurant industry. Suggest improvements to enhance the quality of the manuscript, including professional proofreading. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Consumer behavior in the hospitality industry I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 28 May 2024 Sin Yin Tan, Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia Thank you so much for your comments. We sincerely appreciate the valuable feedback and suggestions from you. To address the comments that highlighted by reviewer: 1.Introduction: The research gaps and contributions of our study have been restructured to include the justification for the research under the introduction section. The study was conducted in 2021 as stated in our article. In future, we plan to extend our study on the impact “after” the pandemic in the OFDS domain. 2.Literature Review: Theoretical Framework has been included under the Literature Review section. Our selected variables were based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Detailed elaboration was further described in this section. 3.Methods: Purposive sampling method was applied in our study and clarified under Data collection of the Methods section. The sampling size was based on the other studies in the same domain. 4.Results: Research ethics approval was obtained from Multimedia University, Malaysia (EA1422021) and the respondents gave their written informed consent when filling out the Google Form. The survey data underwent reanalysis by excluding those who did not have any experience using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under the Result section. Table 3 illustrates feedback of respondents for each measurement item. The objective is to show that there is a significance left-skewed for each measurement item except PSO4. In this study, descriptive study is maybe more meaningful for certain variables such as the mature adults (above 41 years old), and B40 respondents (personal Income level) preferred to dine at home. The preference of dining at home is analysed based on their demographic so that sellers can approach these groups of people. In Table 7, CM's square root of AVE is greater than the correlation between CM and PEOU. 5.Discussion and Conclusion: Discussion and conclusion section have been further improved by explaining how this study contribute to existing theoretical frameworks based on the proposed research model. Further descriptions of related works in this domain were also provided. 6.Overall: Thank you for your suggestion. A significant revision had been undertaken which involves restructuring of the research study based on the theoretical framework and subsequent analysis. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Joung HW. Peer Review Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.76632.r258703) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/10-972/v1#referee-response-258703 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2021 Khoa B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 21 Dec 2021 | for Version 1 Bui Thanh Khoa , Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam 0 Views copyright © 2021 Khoa B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Thank you very much for the opportunity to review the study titled “Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic.” The selected topic is intriguing, and the work can add value to the existing body of literature. However, certain flaws overshadow the paper’s potential significance. In the following lines, I offer some suggestions to this study as follows: One of the most pressing challenges is the theoretical research gap. Please provide a well-defined research gap. Furthermore, please explain why it is critical to include comprehensive poverty eradication in the theoretical contribution for the study area; hence, restructure the introduction. Typically, the framework will include the following elements: the significance of the issue, motivation (optional), research gap(s), aims, and possible contributions (optional). Lack of the research gap(s) reduces the paper’s value. This research used many constructs, such as Convenience motivation, Perceived ease of use, Time-saving orientation, Price-saving orientation, Attitude, Behavioural intention, and continuance intention; however, the reviewer cannot find the theoretical background in this research to connect them; hence, the proposed model is subjective and lacks scientific arguments. Please add the theoretical background in the first literature review. Table 2 shows the demographic profile of 307 respondents, of which there are 16.61% of respondents that have not used OFDS before. Why can they answer the research items regarding the continuance intention? The sampling method should be based on the research objective more than the prior research. The data analysis should add the f 2 and Q 2 values to check the effect size and predictive relevance. Limitation should be moved to the end of the paper. I firmly believe that the authors need a significant overhaul of the study, especially the theoretical framework and data process for ensuring the scientific validity of the article to make the manuscript suitable for indexing, given that it will be professionally revised before another submission. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise electronic commerce, online consumer behavior, marketing I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 28 May 2024 Sin Yin Tan, Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia Thank you so much for the reviewer's comments. We sincerely appreciate the insightful feedback provided by the reviewer. To address the comments that highlighted by reviewer: The Introduction section has been substantially revised by including more significance of the research issues and highlighting the research gaps and contributions of our study. The theoretical background is now positioned within the Literature Review section. We elaborate our study based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). The survey data underwent reanalysis by excluding those who did not have any experience of using Online Food Delivery Services (OFDS). Therefore, the new demographic profile has been presented under Result section. Purposive sampling method was applied and clarified under Data collection of the Methods section. As suggested by reviewer, the f 2 and Q 2 values have been added in Table 9 under Result section to check the effect size and predictive relevance. Considering the journal editor's advice, the limitation was suggested to place before Conclusions section. A significant revision had been undertaken which involve restructuring of the research study based on the theoretical framework and subsequent analysis. Thank you. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Khoa BT. Peer Review Report For: Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved] . F1000Research 2024, 10 :972 ( https://doi.org/10.5256/f1000research.76632.r100846) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/10-972/v1#referee-response-100846 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Online food delivery services: cross-sectional...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/10-972/v2" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/10-972/v2&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/10-972/v2" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Tan SY et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/10-972/v2/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/10-972", templates : { twitter : "Online food delivery services: cross-sectional study of consumers\’.... Tan SY et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/10-972/v2" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/73014/165223") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "165223"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "258699": 0, "258703": 27, "258700": 0, "258708": 0, "483999": 0, "483998": 0, "483997": 0, "258713": 0, "483996": 0, "483995": 0, "253855": 0, "483994": 0, "258718": 0, "483993": 0, "95519": 0, "95521": 0, "253859": 0, "95520": 0, "253858": 0, "258721": 0, "97443": 0, "95523": 0, "253857": 0, "97442": 0, "95522": 0, "253856": 0, "253863": 0, "484002": 0, "253862": 0, "484001": 0, "258725": 0, "253861": 0, "484000": 0, "253860": 0, "258729": 0, "253864": 0, "258732": 0, "283060": 0, "283061": 0, "283062": 0, "283063": 0, "283068": 0, "283069": 0, "283064": 0, "283065": 23, "130108": 0, "283066": 0, "283067": 0, "464071": 0, "486727": 0, "464070": 0, "486726": 0, "464069": 0, "486725": 0, "135488": 0, "486724": 0, "486733": 0, "126795": 0, "486732": 0, "486731": 0, "486730": 0, "126796": 0, "486729": 0, "486728": 0, "128735": 0, "473319": 0, "473318": 0, "473317": 0, "473316": 0, "473315": 0, "473314": 0, "473313": 0, "473312": 0, "464495": 0, "464494": 0, "464493": 0, "464492": 0, "473321": 0, "100847": 0, "473320": 0, "100846": 92, "100849": 0, "100848": 0, "464501": 0, "464500": 0, "100850": 0, "464499": 0, "464498": 0, "464497": 0, "464496": 0, "275580": 16, "275581": 0, "318456": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "679a01be-db23-4979-8936-3dfb8d54b964"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });